Crop Pest and Disease Remote Sensing Monitoring and Forecasting 9782759826605

Table of contents :
Authors
Foreword
Preface
Contents
Part One Overview
Chapter 1 Introduction
Chapter 2 Mechanism and Method
Part Two Non-Imaging Hyperspectral Remote Sensing Monitoring for Crop Pest and Disease
Introduction
Chapter 3 Crop Disease Monitoring
Chapter 4 Crop Pest Monitoring
Chapter 5 Crop Pest and Disease Differentiation
Part Four Remote Sensing forecasting for Crop Pest and Disease
Introduction
Chapter 6 Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease
Chapter 7 Multi-Spectral Remote Sensing Monitoring
Part Four Remote Sensing forecasting for Crop Pest and Disease
Chapter 8 Crop Pest and Disease Forecasting with Multi-Source Data
Part Five System and Application for Crop Pest and Disease Monitoring and Forecasting
Chapter 9 Crop Pest and Disease Monitoring and Forecasting System
Summary

Citation preview

Current Natural Sciences

Yingying DONG, Wenjiang HUANG, Yun GENG, Linyi LIU, Huiqin MA, Anting GUO and Chao RUAN

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

Printed in France

EDP Sciences – ISBN(print): 978-2-7598-2659-9 – ISBN(ebook): 978-2-7598-2660-5 DOI: 10.1051/978-2-7598-2659-9 All rights relative to translation, adaptation and reproduction by any means whatsoever are reserved, worldwide. In accordance with the terms of paragraphs 2 and 3 of Article 41 of the French Act dated March 11, 1957, “copies or reproductions reserved strictly for private use and not intended for collective use” and, on the other hand, analyses and short quotations for example or illustrative purposes, are allowed. Otherwise, “any representation or reproduction – whether in full or in part – without the consent of the author or of his successors or assigns, is unlawful” (Article 40, paragraph 1). Any representation or reproduction, by any means whatsoever, will therefore be deemed an infringement of copyright punishable under Articles 425 and following of the French Penal Code. The printed edition is not for sale in Chinese mainland. Ó Science Press, EDP Sciences, 2022

Authors

Yingying DONG received the B.S. degree in applied mathematics from Shandong Normal University, Shandong, China, in 2006; M.S. degree in computational mathematics from Capital Normal University, Beijing, China, in 2009; and Ph.D. degree in remote sensing in agriculture and information technology from Zhejiang University, Zhejiang, China, in 2013. After getting her Ph.D., she went to Canada as a post-doctoral research fellow in University of Lethbridge. Currently, she is working in Aerospace Information Research Institute, Chinese Academy of Sciences (AIR-CAS). Her research interests include vegetation parameter inversion, crop growth monitoring, pest and disease monitoring and forecasting, and system development. She has published more than 70 articles and more than 10 patents. Wenjiang HUANG received the Ph.D. degree in cartography and GIS from Beijing Normal University, Beijing, China, in 2005. Currently, he is a professor working in Aerospace Information Research Institute, Chinese Academy of Sciences (AIR-CAS). He was acting as PI for more than 40 major scientific projects from Global Earth Observation (GEO), Ministry of Science and Technology (MOST), National Science Foundation of China (NSFC), Chinese Academy of Sciences (CAS), etc. He has published more than 200 SCI journal papers and 31 patents focused on remote sensing for vegetation variables inversion and remote sensing for vegetation pest and disease monitoring. He has been awarded more than 10 science and technology awards including National Science and Technology Progress Award. His research interests cover quantitative remote sensing for precision agricultural applications and monitoring of crop, grass and forest pests and diseases by remote sensing technology.

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Authors

Yun GENG received the B.E. degree in computer science and technology from Beijing Forestry University, Beijing, China, in 2017. She is a Ph.D. student in cartography and GIS at the University of Chinese Academy of Sciences, Beijing, China. Her research interests are remote sensing forecasting of crop pests and diseases. Linyi LIU is a postdoctoral researcher, working in Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China. He received the B.S. degree in geographic information system from Capital Normal University, Beijing, China, in 2015, and Ph.D. degree in Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China, in 2020. His current research interests include crop disease monitoring, remote sensing, and geographic information systems. Huiqin MA is a postdoctoral researcher, working in Aerospace Information Research Institute, Chinese Academy of Sciences Beijing, China. She received the B.S., M.S., and Ph.D. degrees from Nanjing University of Information Science & Technology, Nanjing, China, in 2014, 2017, and 2020, respectively. Her main research interests cover agro-meteorology and remote sensing of vegetation pests and diseases. Anting GUO received the M.S. degree in agricultural informatization from Henan Agricultural University, Zhengzhou, China, in 2017, and the Ph.D. degree in cartography and GIS from the University of Chinese Academy of Sciences, Beijing, China, in 2021. His research interests include remote sensing monitoring of crop pests and diseases. Chao RUAN received the M.S. degree in signal and information processing from Anhui University, Anhui, China, in 2019. Currently, he is a Ph.D. student in cartography and GIS at the University of Chinese Academy of Sciences, Beijing, China. His research interests include remote sensing forecasting of crop pests and diseases.

Foreword

Food security has always been a hot spot of concern in the international community. In the context of climate change, the scope and prevalence of pests and diseases have obviously expanded and increased. Crop pests and diseases are serious biological disasters in China, which are also key factors restricting high yield, high quality, high efficiency, ecology and safety of agricultural production. However, the basic researches of crop pests and diseases mechanisms, large-scale rapid monitoring and forecasting modelling in China still need to be strengthened, to effectively support the prevention and control of crop pests and diseases. Traditional visual hand-check single-point monitoring methods and limited-site meteorological prediction methods could only obtain information on the occurrence and development of pests and diseases at sampling points, and could not meet the needs of large-area monitoring and timely prevention and control at regional scale. In contrast, remote sensing could efficiently and objectively monitor the occurrence and development of pests and diseases on a large scale in time and space. The rapid development of earth observation technology in recent years has provided effective technical means for large-scale monitoring of pests and diseases and is highly effective in large-scale and rapid guidance of pests and diseases. Scientific prevention and control and food security are of great significance. In addition, the continuously updated encrypted meteorological station data and the area meteorological parameter products formed by the coupling of remote sensing and meteorological data provide a richer source of information for the dynamic monitoring of pest and disease occurrence. In this context, the Vegetation Remote Sensing & Pest and Disease Application Research Team systematically conducted studies on the spectral mechanisms of major crop pests and diseases, remote sensing early warning, and remote sensing dynamic monitoring of disaster habitat and its occurrence and damage levels. It is believed that these researches could bring important and long-term impact in ensuring food security, improving agricultural productivity, and increasing the income of agricultural community at home and abroad. DOI: 10.1051/978-2-7598-2659-9.c901 Ó Science Press, EDP Sciences, 2022

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Foreword

Yingying Dong, an outstanding young innovator in the research field of crop pest and disease remote sensing monitoring and forecasting, has achieved a series of research results in data processing, monitoring and forecasting modelling, intelligence system development, and applications. She and her team collected tens of years of field survey data, remote sensing satellite data, etc., and then systematically analyzed pest and disease mechanisms, developed imaging and non-imaging remote sensing monitoring methods, and dynamically forecasted the occurrence and damage of crop pests and diseases. This research combined basic theories and cutting-edge technologies in an innovative way to provide practical pest and disease information for field management, moreover, to support plant protection departments and agricultural departments for prevention strategy design. This book could be used as a reference book for agricultural plant protection applications, scientific research and teaching. The advent of the big data era provides a valuable opportunity for remote sensing technology’s application in promoting the development of disciplines. It is hoped that this book could help young remote sensing workers, promote the development and application of remote sensing technology in China, and enhance breakthrough in remote sensing science and technology to ensure food security and human well-being.

Preface

The increasing occurrence and damage of crop pests and diseases due to global climate change leads to huge decline in quantity and quality of agriculture production, seriously threatening food security and global stability. According to the statistic of the Food and Agriculture Organization of the United Nations (FAO), the worldwide food production is reduced by 10% due to pests, and 14% due to diseases worldwide. In China, the annual food loss caused by various pests and diseases is about 40 billion kilograms, accounting for 8.8% of the total food production. Food security has always been a big issue for national economic development, social stability, and national independence in China. Crop pests and diseases as the main agricultural disasters are characterized by multiple types, wide impact, and frequent outbreaks. The scope and severity of crop pests and diseases have caused significant losses to our national economy, especially for agricultural production. The epidemic of pests and diseases has seriously affected agricultural production, and it is urgent to control the occurrence and development of these pests and diseases to ensure food security. Traditionally, the monitoring and forecasting of pests and diseases in China mainly rely on manual visual inspection, field sampling and meteorological analysis. Although these traditional methods are highly authentic and reliable, they are time-consuming and labor-intensive, and have drawbacks such as lower spatial representativeness, poor timeliness, and strong subjectivity, making it difficult to adapt the current needs for real-time monitoring and forecasting on a large-scale. Remote sensing technology could quickly obtain spatial and continuous land surface information on a large scale. Extensive research and application in crop planting area extraction, growth monitoring, and remote sensing yield estimation have been carried out. With the development of space technology, especially the development of satellite, aviation and UAV technologies in recent years, various airborne and spaceborne remote sensing data sources provide users with high spatial, high temporal and hyperspectral earth observations. The remote sensing techonologies show DOI: 10.1051/978-2-7598-2659-9.c902 Ó Science Press, EDP Sciences, 2022

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Preface

better performance for crop pests and disesase monitoring and forecasting. However, to promote its application, we need to handle bottlenecks including pest/disease remote sensing mechanism clarification, large-scale and rapid monitoring and forecasting, intelligence system and service products development. Then the technologies will be contributed to ensuring food security, increasing farmers' income, and reducing environmental pollution. The realization of sustainable agricultural development is of vital theoretical and practical significance. Vegetation Remote Sensing & Pest and Disease Application Research Team systematically introduces crop pest and disease remote sensing monitoring and forecasting thematical mechanisms, algorithms and models, intelligence system development, and applications, by integrating with multi-disciplinary knowledge and artificial intelligence technologies in “Crop Pest and Disease Monitoring and Forecasting” over the last ten years, supported by National Key R&D Program of China (2017YFE0122400), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19080304), Key International Cooperation Project of the Chinese Academy of Sciences (183611KYSB20200080), National Natural Science Foundation of China (42071320, 42071423, 41801338), Beijing Nova Program of Science and Technology (Z191100001119089), National Special Support Program for High-level Personnel Recruitment (Wenjiang Huang), Ten-thousand Talents Program (Wenjiang Huang), Youth Innovation Promotion Association CAS (2017085), Hainan Provincial High Level Talent Program of Basic and Applied Basic Research Plan in 2019 of China (2019RC363), Alliance of International Science Organizations (ANSO-CR-KP-2021-06), etc., as well as government departments, universities, research institutions, science and technology innovation alliances, and application & demonstration companies. It also includes many M. S/Ph.D. students and postdoctoral fellows’ academic achievements in this team work. This book has five parts and nine chapters. In the first part, the significance and current research and application status of crop pests and diseases monitoring and forecasting are introduced, including chapters 1 and 2, which are introduction and remote sensing monitoring and forecasting mechanisms and methods of crop pests and diseases, respectively. Then, crop pests and diseases non-imaging remote sensing monitoring is discussed in the second part, including crop diseases non-imaging remote sensing monitoring, crop pests non-imaging remote sensing monitoring, and differentiation of crop pests and diseases in chapters 3, 4 and 5, respectively. Imaging remote sensing monitoring is discussed in the third part, including hyperspectral remote sensing monitoring of crop pests and diseases, and multispectral remote sensing monitoring of crop pests and diseases in chapters 6 and 7. Remote sensing forecasting of crop pests and diseases is discussed in the fourth part, which includes crop pests and diseases forecasting based on multi-source data in chapter 8. Finally, the construction and application of crop pest and disease remote sensing monitoring and forecasting system are described in chapter 9 of the fifth part. Yingying Dong, Wenjiang Huang, Yun Geng, Anting Guo, Chao Ruan, Juhua Luo, Jingcheng Zhang, Xianfeng Zhou, Junjing Lu, etc., are the authors of Part one. Yingying Dong, Linyi Liu, Huiqin Ma, Qiong Zheng, Yue Shi, Hong Chang, Xiaoping Du, Cuicui Tang, Furan Song, Qingsong Guan, Chenwei Nie, Xia Jing,

Preface

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Lin Yuan, etc., are the authors of Part two. Part three is written by Huiqin Ma, Anting Guo, Yu Ren, Weiping Kong, Longlong Zhao, Jing Jiang, Hansu Zhang, Jianli Li, Jing Wang, etc. Part four is written by Yun Geng, Yue Shi, Zhaochuan Wu, Yingxin Xiao, Qinying Yang, Xia Jing, Chuang Liu, etc. Part five is written by Linyi Liu, Fang Xu, Tingguang Hu, Wenjing Liu, Kang Wu, Yong Liu, etc. The whole book is drafted by Yingying Dong, Yun Geng, Yanru Huang, Ruiqi Sun, Linsheng Huang, Xueling Li, etc. Qiuyan Li from the Resources and Environment Division of Science Press provides advice and help in manuscript writing and editing. Thanks to all the contributors. The development of remote sensing technology and artificial intelligence has promoted the enhancement of crop pest and disease remote sensing monitoring and forecasting methods and applications. This book is expected to provide references for crop pest and disease management and promote the development of related research.

Contents Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII Part One Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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CHAPTER 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . 1.2 Crop Pest and Disease . . . . . . . . . . . . . . . 1.2.1 Wheat Yellow Rust . . . . . . . . . . . . 1.2.2 Wheat Powdery Mildew . . . . . . . . . 1.2.3 Wheat Fusarium Head Blight . . . . . 1.2.4 Wheat Aphid . . . . . . . . . . . . . . . . . 1.2.5 Rice Leaf Roller . . . . . . . . . . . . . . . 1.2.6 Oriental Migratory Locust . . . . . . . 1.3 Progress and Development . . . . . . . . . . . . 1.3.1 Crop Pest and Disease Monitoring . 1.3.2 Crop Pest and Disease Forecasting . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Mechanism and Method . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Spectral Mechanism . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Spectral Response of Healthy Crop . . . . . . . . 2.1.2 Spectral Response of Crop Pest and Disease . 2.2 Spectral Feature Extraction . . . . . . . . . . . . . . . . . . 2.2.1 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . 2.2.2 Spectral Feature . . . . . . . . . . . . . . . . . . . . . . 2.3 Monitoring Method . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Recognition and Differentiation Method . . . . 2.3.2 Severity Monitoring Method . . . . . . . . . . . . .

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Forecasting Method . . . . . . . . . . . . 2.4.1 Bayesian Network . . . . . . . . 2.4.2 Logistic Regression Analysis . 2.4.3 Markov Chain . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . .

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58 58 60 62 64

Part Two Non-Imaging Hyperspectral Remote Sensing Monitoring for Crop Pest and Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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CHAPTER 3 Crop Disease Monitoring . . . . . . . . . . . . . . . . . 3.1 Wheat Yellow Rust Monitoring . . . . . . . . 3.1.1 Monitoring at Leaf Scale . . . . . . . 3.1.2 Monitoring at Canopy Scale . . . . . 3.2 Wheat Powdery Mildew Monitoring . . . . 3.2.1 Monitoring at Leaf Scale . . . . . . . 3.2.2 Monitoring at Canopy Scale . . . . . 3.3 Wheat Fusarium Head Blight Monitoring 3.3.1 Monitoring at Ear Scale . . . . . . . . 3.3.2 Monitoring at Canopy Scale . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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109 109 110 111 120 127 128 129 135

Crop Pest and Disease Differentiation . . . . . . . . . . . . . . . 5.1 Wheat Yellow Rust and Nutrient Stress . . . . . . . . 5.1.1 Spectral Response . . . . . . . . . . . . . . . . . . . 5.1.2 Rust and Nutrient Stress Differentiation . . . 5.2 Wheat Disease and Nutrient Stress . . . . . . . . . . . . 5.2.1 Spectral Response . . . . . . . . . . . . . . . . . . . 5.2.2 Disease and Nutrient Stress Differentiation . 5.3 Crop Pest and Disease . . . . . . . . . . . . . . . . . . . . . 5.3.1 Differentiation at Leaf Scale . . . . . . . . . . . . 5.3.2 Differentiation at Canopy Scale . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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137 137 137 139 143 143 146 150 150 167 174

CHAPTER 4 Crop Pest Monitoring . . . . . . . . . . . . . . . . 4.1 Wheat Aphid Monitoring . . . . . . . . 4.1.1 Spectral Response . . . . . . . . 4.1.2 Monitoring at Leaf Scale . . . 4.1.3 Monitoring at Canopy Scale . 4.2 Rice Leaf Roller Monitoring . . . . . . 4.2.1 Spectral Response . . . . . . . . 4.2.2 Monitoring at Canopy Scale . References . . . . . . . . . . . . . . . . . . . . . . . .

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CHAPTER 5

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Part Three Imaging Remote Sensing Monitoring for Crop Pest and Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 CHAPTER 6 Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Wheat Yellow Rust Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Monitoring at Leaf Scale . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Monitoring at Canopy Scale . . . . . . . . . . . . . . . . . . . . . . . 6.2 Wheat Powdery Mildew Monitoring . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Spectral Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Monitoring at Leaf Scale . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Wheat Aphid Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Extraction of Aphid Information by Sensitive Wavebands . 6.3.2 Extraction of Aphid Information by Spectral Index . . . . . 6.3.3 Extraction of Aphid Information by Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.4 Construction of Aphid Index and Extraction of Aphid Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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CHAPTER 7 Multi-Spectral Remote Sensing Monitoring . . . . . . . . . . . . . . 7.1 Wheat Yellow Rust Monitoring . . . . . . . . . . . . . . . . . . 7.1.1 Monitoring at Regional Scale . . . . . . . . . . . . . . 7.1.2 Monitoring at National Scale . . . . . . . . . . . . . . 7.2 Wheat Powdery Mildew Monitoring . . . . . . . . . . . . . . 7.2.1 Monitoring with HJ Data . . . . . . . . . . . . . . . . . 7.2.2 Monitoring with GF Data . . . . . . . . . . . . . . . . 7.2.3 Monitoring with Multi-Temporal Landsat Data 7.3 Wheat Fusarium Head Blight Monitoring . . . . . . . . . . 7.3.1 Monitoring at Regional Scale . . . . . . . . . . . . . . 7.3.2 Monitoring at National Scale . . . . . . . . . . . . . . 7.4 Oriental Migratory Locust Monitoring . . . . . . . . . . . . 7.4.1 Monitoring at Regional Scale . . . . . . . . . . . . . . 7.4.2 Monitoring at National Scale . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Part Four Remote Sensing forecasting for Crop Pest and Disease . . . . . . . . . 261 CHAPTER 8 Crop Pest and Disease Forecasting with Multi-Source Data 8.1 Wheat Yellow Rust . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . 8.1.2 Modelling and Validation . . . . . . . . . . . . . . . .

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Wheat Powdery Mildew . . . . . . 8.2.1 Data Acquisition . . . . . . 8.2.2 Modelling and Validation 8.3 Oriental Migratory Locust . . . . 8.3.1 Data Acquisition . . . . . . 8.3.2 Modelling and Validation References . . . . . . . . . . . . . . . . . . . . .

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Part Five System and Application for Crop Pest and Disease Monitoring and Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 CHAPTER 9 Crop Pest and Disease Monitoring and Forecasting System . . . . . . . . . . . . 9.1 Field Data Acquisition System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Crop Pest and Disease Remote Sensing Monitoring and Forecasting System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 National Locust Remote Sensing Monitoring and Early Warning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319

Part One

Overview

Chapter 1 Introduction 1.1

Background

Crop pests and diseases have always been important factors restricting agricultural production and endangering food security. The global annual food production loss caused by pests and diseases accounts for about a quarter of total food production. Among them, loss related to crop pests accounts for about 42%, and 58% related to diseases (Strange and Scott, 2005). According to the statistics of the Ministry of Agriculture and Rural Affairs of the People’s Republic of China, the damaged area of crop pests and diseases showed a significant increase from 1971 to 2007. The area affected by pests and diseases increased from about 100 million hectares in 1971 to 345 million hectares in 2007 (Piao et al., 2010). At the same time, from 2000 to 2015, the annual loss of grain production due to crop pests and diseases in China was about 40 billion kilograms, accounting for 8.8% of the total grain production (Zhao et al., 2014a; Zhao et al., 2014b). Therefore, rapid and real-time monitoring and early warning of crop pests and diseases are the key to protecting food security and increasing food production. At present, the main methods of crop pest and disease monitoring and forecasting still rely on traditional field observations and manual inspections. These traditional methods have high veracity and reliability in survey points, but they are time-consuming, labor-wasting, and spatially unrepresentative on a large scale. It is difficult to adapt to the current demand for real-time pest and disease monitoring and forecasting on a large scale. Crop pests and diseases monitoring and forecasting with remote sensing is a non-contact rapid diagnosis method for pests and diseases. It has the characteristics of precision, efficiency, and non-destruction, which can greatly improve the ability to monitor and forecast the occurrence and scope of various pests and diseases. In recent years, with the increasing enrichment of remote sensing observation methods at different scales, multi-source remote sensing data have been widely used in agricultural management and pests and diseases monitoring and forecasting (Qiao, 2007). How to comprehensively utilize the surface information provided by multi-temporal and spatial-scale remote sensing products, to carry out remote sensing quantitative

DOI: 10.1051/978-2-7598-2659-9.c001 © Science Press, EDP Sciences, 2022

4

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

monitoring and forecasting research of crop stress is becoming more and more popular among experts and researchers (Yuan, 2015). With the strengthening relationship between remote sensing science and crop pathology, the research scope continues to expand, and the application of crop pests and diseases monitoring and forecasting at different scales has changed the traditional agricultural management model, aiming at high yield, high quality, high efficiency, ecology, security, modernization, and informatization (Graziosi et al., 2016; Del et al., 2010; Sundaram et al., 2009; Elmasry et al., 2008; Mehl et al., 2004). In general, due to the complexity of the types and characteristics of pests and diseases, and the highly dynamic nature of their occurrence and development, many researches have been concentrated on this field, and lots of distinctive features have been formed. Therefore, it is necessary to systematically sort out and summarize the methods of remote sensing monitoring and forecasting of crop pests and diseases to provide references for further research and application. The exploration in this research field will help reduce the use of pesticides and fungicides, decrease grain loss caused by pests and diseases, and ensure the ecological environment of cropland and the safety of subsequent agricultural products. Moreover, it will have an important and far-reaching impact on guaranteeing food security and increasing production and farmers’ income in China.

1.2

Crop Pest and Disease

Crop pests and diseases are the major agricultural disasters in China, featuring in multi-types, wide impact, and frequent outbreaks. The scope and severity of crop pests and diseases have caused significant losses to our national economy, especially agricultural production. The epidemic of pests and diseases has seriously affected agricultural production, and it is urgent to control the occurrence and development of these pests and diseases to ensure food security. The previous researches and applications of remote sensing technology in the field of crop pest and disease monitoring and forecasting have covered a variety of crops and their corresponding pests and diseases, including wheat yellow rust, wheat leaf rust, wheat powdery mildew, wheat Fusarium head blight, wheat aphid, cotton verticillium wilt, cotton root rot, cotton aphids, rice blast, rice leaf roller, rice planthopper, early tomato blight, late tomato blight, broad bean bacterial blight, broad bean blight, flower rust disease, soybean verticillium wilt disease, soybean sclerotinia disease, sugar beet snake eye disease, sugar cane orange rust disease, celery sclerotinia disease, and maize aphid. This book includes the main types of crop pests and diseases such as wheat yellow rust, wheat powdery mildew, wheat Fusarium head blight, wheat aphid, rice leaf roller, maize armyworm, and oriental migratory locust, and discusses how to use remote sensing technology to monitor and forecast these pests and diseases. The main hazards and characteristics of the above pests and diseases are detailed in the subsequent sections.

Introduction

1.2.1

5

Wheat Yellow Rust

Wheat yellow rust (Puccinia striiformis), a kind of wheat rust, is a regional epidemic disease. From the perspective of environmental preference, wheat yellow rust is a low-temperature, high-humidity, high-light fungal disease. The spores are spread through the air. It has the characteristics of wide incidence, strong epidemic, and high probability. It is the most widespread and harmful disease in China and even in the world. After the wheat is damaged, it can cause early leaf withering and reduce ear number and thousand-grain weight. Generally, the yield can reduce by 5%–10%, and the yield in severely damaged fields can reduce by more than 20%. Wheat yellow rust outbreaks have been recorded in Hebei, Henan, Shaanxi, Shandong, Shanxi, Gansu, Sichuan, Hubei, Yunnan, Qinghai, and Xinjiang, which caused great losses to wheat production. In 1950, 1964, 1990, and 2002, there were four pandemic wheat yellow rust in China, which caused about 6 billion, 3 billion, 2.6 billion, and 1 billion kilograms of yield losses, respectively. In recent years, with the emergence and development of new physiological races and other pathogenic types, the damage of wheat yellow rust has been increasing. The wheat yellow rust pathogen is a Basidiomycotina. The mycelium is fibrous and separated, and grows in the interstitial space of the host cells. The nutrient in the wheat cells is sucked with an aspirator to produce a spore pile in the disease infested part. The typical symptoms of wheat yellow rust mainly occur on leaves, followed by leaf sheaths and stalks, ears, glumes and awns. If the infection occurs at the seedling stage, a multi-layered arrangement of bright yellow summer spore piles on the leaves of seedlings. At the beginning of adult leaf onset, the summer spore piles are small strips, bright yellow, oval, parallel to the veins, and arrange in rows, showing a dotted line (figure 1.1). The epidermis ruptures in the later period, and the rusty powder appeares. When the wheat is nearly mature, heaps of round to oval black-brown summer spores appear on the leaf sheaths, and scatter bright yellow powder, i.e., summer spores. Pathogens spread to contaminated wheat leaves by conidia or ascospores by airflow. If the temperature and humidity conditions are appropriate, the germs germinate and grow germ tubes. The front end of the germ tube expands to form attachment cells and invasion lines, penetrates the cuticle of the leaves, and invades epidermal cells to form a primary aspirator and grow mycelia to the outside of the host body. Conidial stalks and conidia are produced in the mycelial plexus, which fall off after maturity, spread with airflow, and undergo multiple reinfection. The germs reproduce sexually during the later stages of development, forming vesicles on the flora.

1.2.2

Wheat Powdery Mildew

Wheat powdery mildew (Blumeria graminis f. sp. tritici) could occur in all growth stages of wheat, and it is a relatively serious disease. It is common in Sichuan, Guizhou, Yunnan, and coastal areas of Shandong in China. Recently, this disease has also become increasingly serious in Northeast, North, and Northwest wheat planting areas.

6

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

FIG. 1.1 – Wheat yellow rust. Wheat powdery mildew pathogen is Ascomycota. The parasite of the mycelium spreads on the surface of the host and forms aspirators in the host epidermal cells to absorb host nutrients. At the end of the conidial stalk perpendicular to the mycelium, 10–20 conidia are concatenated, oval, and the single cell is colorless. The infectivity lasts for 3–4 days. Generally, wheat leaves are mainly damaged by powdery mildew, the ear, stem and leaf sheath of wheat could also be damaged in severe cases. After the wheat is damaged, the leaves become blight at the early stage and turn yellow (figure 1.2), with reduced number of ears and thousand-grain weight, the yield can be reduced by 5%–10%, and the seriously damaged fields can reach more than 20%.

Introduction

7

FIG. 1.2 – Wheat powdery mildew. Pathogens spread to contaminated wheat leaves by conidia or ascospores by airflow. The subsequent infection process is similar to wheat yellow rust. There are two ways for the pathogen to overwinter, one is overwintering in the form of conidia, and the other is overwintering in the host tissue with mycelium incubation. The overwintering pathogen first infects the bottom leaves to expand horizontally, then moves to the upper and middle leaves, and the onset center is obvious in the early stage of the disease. The source of spring pathogenic bacteria in winter wheat area is mainly from the local area. In addition to local sources, the pathogens in the spring wheat area also comes from neighboring areas with early onset. When the plant grows weak and the fertilization is not properly managed, it is easy to aggravate the disease.

1.2.3

Wheat Fusarium Head Blight

Wheat Fusarium head blight (Fusarium graminearum) is a devastating wheat disease that occurs widely in warm, humid and semi-humid regions around the world. High temperature and humidity are necessary conditions for the growth and development of wheat Fusarium head blight. 22–28 °C is the suitable temperature for the growth of pathogenic mycelium. 80%–100% is the suitable humidity for disease development. Wheat Fusarium head blight mainly causes serious damage to the spring and winter wheat regions in Northeast China and the wheat regions in the Yangtze River valley. The damaged area exceeds 7 million hectares each year, causing a yield loss of 10%–40%. Wheat Fusarium head blight not only reduces

8

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

wheat production, but also deteriorates wheat quality. In addition, it infects wheat grains and produces a variety of mycotoxins. These toxins are harmful to humans and animals. DON toxin is the most harmful. When the concentration of DON toxin reaches 50 mg/kg, it can inhibit 80% of human T cells. With climate change, wheat Fusarium head blight has spread to the Huai River basin and Huanghuai River basin in recent years. There are many species of Fusarium that cause wheat head blight. The main pathogen that causes wheat head blight in China is Fusarium graminearum. The host of Fusarium graminearum is extensive, including a variety of cultivated crops such as barley, wheat and rice, and wild weeds such as setaria and barnyardgrass. Crop seeds, stalks and stubble of other crops and soil are all sources of infection of wheat Fusarium head blight. Fusarium graminearum begins to mature and release ascospores before and after wheat heading and blooming, and spreads with wind and rain. Ascospores will produce conidia after saprophytic on wheat floral organs and infect spikelets many times. After 5–7 days, it will cause ear rot of wheat, accompanied by a large amount of pink mold layer (figure 1.3). In the late stage of the disease, ascospores and ascospore shells will be produced in the affected parts of wheat. After wheat harvest, the Fusarium head blight on the plant remains will last all summer in a saprophytic manner, or the Fusarium head blight will continue to infect other hosts and become the main source of infection for the next year. Although disease-infested seeds and saprophytes on the surface of the soil can cause basal rot and seedling rot, they have no direct effect on the later ear rot.

FIG. 1.3 – Wheat Fusarium head blight.

Introduction

1.2.4

9

Wheat Aphid

Wheat aphid (Sitobion avenae & Rhopalosiphum padi), also called worms, is widely distributed in almost every wheat-producing country in the world. There are many kinds of aphids that damage wheat in China, usually including Sitobion avenae, Schizaphis graminum, Rhopalosiphum padi and metopolophium dirhodum. There are three main types of aphids that seriously endanger wheat in China, including Sitobion avenae, Schizaphis graminum and Rhopalosiphum padi. In general, the density of Aphis gossypii is quite high in both the north and the south, but it is more serious in the north. The harm of wheat aphid includes direct damage and indirect damage. The direct one is mainly due to the damage to wheat growth. If the aphid sucks the sap of young or senescent leaves, stalks, tender heads and ears, it will endanger the normal wheat growth. When the seedling stage is severely damaged, the growth of wheat will be stagnant, and the tillers will be reduced. Aphids will discharge honeydew and adhere to the surface of the leaves, which often makes the leaves moldy and black, seriously affecting the photosynthesis of wheat leaves and ultimately reducing wheat production. Sitobion avenae mostly damages the upper leaves of plants. After heading and filling stages, it multiplies rapidly and damages the ears. Indirect damage means that the wheat aphid can spread wheat virus disease while damaging it, among which wheat yellow dwarf disease is the most harmful. Early damage can cause yellowing of wheat seedlings and affect growth, and later damage leaves curling, black plant hair oil and reduces thousand grain weight. In severe cases, wheat ears turn white, become unable to bear fruit, and even the whole plant dies, which seriously affects the wheat yield (figure 1.4). The habits of wheat aphids vary by species. Sitobion avenae is light-tolerant to humidity, highly adaptable to moderate temperature and not resistant to high

FIG. 1.4 – Wheat aphid.

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

temperature. The suitable humidity range is 40%–80%, and the suitable temperature range is 16–25 °C. When the weather is dry, the occurrence rate of aphids is generally high. As for the reasons, when the humidity is low, the water content in the plant is relatively small, while the nutrients are relatively rich, which is conducive to its growth and development. Therefore, the rainfall during the drought has no adverse effects on the reproduction of Sitobion avenae, and the number of aphids suddenly increases after the rain to form a disaster. However, excessive drought will make the plant excessively dehydrated, which will increase the viscosity of the juice and reduce the turgor of the cells, making it difficult for aphids to eat and affecting their growth and development. On the contrary, if it is rainy in summer and the plant has too much water, the acidity will increase, which will cause indigestion and death of aphids. A moderate rain that lasts 2–3 days also has a significant inhibitory effect on the occurrence of wheat aphids, especially during the heading period. Moderate rains are unfavorable for the winged aphids to live on the wheat ears and reproduce. Therefore, rainfall indirectly affects the growth and decline of aphids through the effect of atmospheric humidity. In particular, heavy rain is not good for the occurrence of Sitobion avenae, and the mechanical impact of storms often reduces the aphids significantly. According to the literature, when the rainfall reaches 30 mm in one hour and the wind is strong, the drop rate of aphids will reach 80%.

1.2.5

Rice Leaf Roller

Rice leaf roller (Cnaphalocrocis medinalis) belongs to Lepidoptera and Mothidae. It is a migratory pest that damages rice in Southeast Asia and Northeast Asia. It is distributed in all provinces (cities/districts) in rice-planting areas in China. At the outset, it was a pest that caused local and intermittent damage. However, since the 1960s, its occurrence and damage have increased year by year. Since the 1970s, the frequency of occurrence has increased significantly, and it has become one of the pests severely affecting rice production. The growth and development of the rice leaf roller needs moderate temperature and high humidity. Generally speaking, the suitable temperature is 22–28 °C and relative humidity is above 80%. Temperatures above 30 °C or below 20 °C, or relative humidity below 70% are not conducive to the pest development. Rainy and high humidity are favorable for its occurrence, but in the incubation period, when there is a storm, the survival rate of the larvae being washed decreases. The newly hatched larvae feed on the heart leaves and appear as needle-shaped dots. As the insect age increases, they spin silk around the leaf margins of the rice leaves. The leaves are rolled into cylindrical buds, and the larvae hide inside and gnaw the leaf flesh, and the skin is left with white streaks. The host plant is mainly rice, barley, wheat, sugar cane, and occasionally, millet. It can also feed on barnyard grass, game grass, paspalum, crabgrass, setaria, thatch, reed, willow leaf and other grassy weeds. Rice at the tillering stage and panicle stage is easily damaged by the rice leaf roller. The leaves are damaged at the tillering stage because their photosynthetic products mainly supply plants for vegetative growth, and crops have a certain ability to compensate, which has little

Introduction

11

impact on yield. However, the photosynthetic products of the leaves behind the booting panicles mainly provide for the development of young panicles. Damage to rice leaves can degradate spikelets and branch stems, increase the empty rate, and reduce the seed setting rate and thousand-grain weight. In particular, damage to rice functional leaves directly affects the accumulation of dry matter and has the greatest impact on rice yield. Therefore, the damage loss of rice from booting stage to heading stage is greater than that of the tillering stage.

1.2.6

Oriental Migratory Locust

The oriental migratory locust (Locusta migratoria manilensis) (figure 1.5) is a migratory pest. It has a wide breeding area, strong fecundity, fast growth, mixed feeding habits, and the habit of gathering in groups and flying long distances. It mainly damages maize, wheat, sorghum, rice, millet and other gramineous crops. Adults and nymphs bite vegetation leaves and stems, and eat plants into stalks, which is a devastating agricultural pest. At present, locusts in China essentially occur every year, with the damaged area around 1–2 million hm2, accounting for more than 90% of the area where the migratory locusts occur. According to statistics from the Ministry of Agriculture and Rural Affairs of the People’s Republic of China, during the summer and autumn periods from 1990 to 2018, the total area of locusts across the country reached 44 million hm2. Over the decade from 2008 to 2017, the average annual area of locusts was as high as 1.5 million hm2, which had a serious negative impact on food security, ecological security, farmers' income growth, social stability and even national reputation in China.

FIG. 1.5 – Oriental migratory locust.

12

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

Locusts from egg to nymph, and then to adult is called a life cycle or life history, also named as a generation. The time required for one generation of oriental migratory locust varies with temperature and season, generally between 60 and 200 days, so the migratory locust does not only have one generation per year, but two, three or four generations per year in China. The number of locust generations depends on the geographical location and the growth and development of eggs, nymphs and adults. In conditions where food is abundant, temperature has the greatest impact on the generation. When the temperature is low, the development of locust eggs will stop or slightly slow down. The large difference in winter temperature from south to north in China leads to a large difference in the unearthed period of locust, which affects the number of generations. Locust eggs are mostly found on grasslands, river beaches and lakes along adret slopes. 1–2 instar locust flies cluster on plants, and locust flies above two instar flock on bare and shallow grasslands. The gregarious migratory locusts form when the density is high. The gregarious locust flies and adults have the habit of migrating in groups. One locust can eat 260–280 g food in its lifetime, and the food intake of adults is 3–7 times that of nymphs.

1.3

Progress and Development

Remote sensing technology could quickly obtain spatial and continuous land surface information on a large scale. It could detect and monitor the physical characteristics of objects with the analysis of the reflection and scattering at a distance. With the rapid development of remote sensing and computer science, a variety of remote sensing data have been widely used in crop pests and diseases remote sensing monitoring and forecasting. The remote sensing characteristics, monitoring models and forecasting models have been studied on multiple scales, which make crop pests and diseases become an important research topic of agricultural remote sensing. The process of crop pest and disease remote sensing monitoring and forecasting could be divided into three parts as data acquisition, data processing, analysis and application (figure 1.6). The signal transmitted from the energy (including the energy of passive remote sensing: the sun, the energy of active remote sensing: radar sensor) reaches the ground and interacts with the surface material, and then reaches the sensor through reflecting. The image data products are generated, including band, time, and other information. Series products, including thematic maps, tables, etc., are formed through visual interpretation, analog image processing and computer data image processing methods. The generated information of products will be applied to practice. The research results in this book are mainly applied to support crop pests and diseases control and management. This section summarizes related research on crop pest and disease monitoring and forecasting with remote sensing technology in recent years. For the crop pest and disease monitoring, hyperspectral and multispectral remote sensing technologies are applied for pests and diseases spectral response mechanism analysis and monitoring method development. For the forecasting part, algorithms and methods

Introduction

13

FIG. 1.6 – Crop pest and disease remote sensing monitoring and forecasting. are introduced for main pests and diseases early forecasting and dynamic forecasting.

1.3.1

Crop Pest and Disease Monitoring

With the continuous enrichment of remote sensing satellite data sources, the newly launched GF series in China and the Sentinel series of European Space Agency (ESA) in recent years, together with FY series and HJ series in China, Landsat series in the United States etc., have greatly improved the spatial and temporal resolution of remote sensing observation data. Recently, the use of remote sensing to monitor crop pests and diseases has mainly focused on the characteristics of different remote sensing data sources to analyze the spectral response characteristics of different pests and diseases. Analyzing and modelling the remote sensing signals are carried out by selecting the spectral characteristics of the pest/disease sensitivity band to realize the monitoring and classification of pests and diseases. 1. Hyperspectral remote sensing monitoring Research on crop pest and disease monitoring based on hyperspectral technology mainly focuses on visible and near-infrared wavelengths. The continuous spectral information of crops obtained through hyperspectral observation is mainly used in the following two aspects in the remote sensing monitoring and identification of

14

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

pests and diseases. On the one hand, the use of hyperspectral sensors can simultaneously acquire the spectral differences and texture differences of crop pests and diseases, and then combine the different information of the two aspects to extract stress characteristics. On the other hand, the acquired hyperspectral band information can effectively characterize the changes in vegetation physical and chemical components caused by pests and diseases. The changes in the spectral reflectance of the “visible-near infrared” band on the leaf surface caused by crops under the stress of pests and diseases are the direct characteristics of remote sensing, reflecting the response of vegetation physical and biochemical components. The research on the spectral response caused by pests and diseases has attracted the attention of many scholars, and has been widely used in remote sensing monitoring and early stress diagnosis research (Yao et al., 2018; Prabhakar et al., 2011; Naidu et al., 2009). Luo et al. (2013) studied the spectral response of aphids which infested wheat leaves, and the results showed that the spectral reflectance of leaves at 700–750 nm, 750–930 nm, 950–1030 nm and 1040–1130 nm had significant response rates to wheat aphids. In addition, the use of the feature transformation form of the original spectrum can effectively enhance the difference in spectral features, thereby extracting the category and severity of the target pest/disease. For example, Spilelli et al. (2006) obtained the derivation of pear tree canopy spectrum data, and conducted remote sensing identification and early monitoring of fire blast by screening the derivative features that are more sensitive to pear blast. Purcell et al. (2009) used a hyperspectral analyzer to measure sugarcane samples at different levels of infestation. Besides, the research used Fourier transform (FT) to extract spectral texture information, principal component analysis (PCA) to screen important characteristic variables and partial least-square method (PLS) to model and analyze the screening characteristics and the severity of different pest/disease. The results showed that the second-order differential spectrum has higher monitoring accuracy than other characteristics. It has great application potential in the early identification of pest/disease. The spectral index formed by combining sensitive bands not only has clear physical meaning, but also highlights the physiological and biochemical processes of pests and diseases, thereby realizing the monitoring and distinguishing of pests and diseases from the perspective of biological mechanisms. Shi et al. (2017a) obtained the canopy hyperspectral data of wheat yellow rust, powdery mildew and aphid through inoculation experiments, selected sensitive bands through correlation analysis and extracted multiple vegetation index (VI) features based on sensitive bands; and then passed multiple kernel discriminant analysis and construction of a variety of nonlinear classifiers, and used the constructed classifier to monitor and recognize crop pests and diseases. The results showed that the nonlinear classifier constructed based on the Sigmoid kernel function can obtain high-precision monitoring effects. Naidu et al. (2009) obtained hyperspectral data of grape leaves infested by grape leaf roll disease through field experiments, and found that the spectral reflectance of the green band and near-infrared band had a significant response to disease stress through correlation analysis. Subsequently, the relevant VI was constructed based on the sensitive bands, and the high-precision remote sensing identification of grape leaf roll disease was realized. Based on these studies, more and

Introduction

15

more scholars have found that crop pests and diseases showed different responses in different spectral bands (Lowe et al., 2017; Shi et al., 2017b; Chen et al., 2007). Therefore, how to find and construct highly specific monitoring indices for different types of pests and diseases in actual monitoring and choose a more appropriate model construction method is a key issue that continues to be solved in remote sensing monitoring of crop pests and diseases (Han et al., 2016; Wu et al., 2008; Delalieux et al., 2007). The current common idea is to extract and construct relevant spectral features by searching for hyperspectral bands that are more sensitive to the severity of pests and diseases. Table 1.1 displays the current main crop pests and diseases remote sensing identification and monitoring of the spectral characteristics, to distinguish and identify different pests and diseases. 2. Multispectral remote sensing monitoring At the regional scale, with the continuous improvement of aviation/aerospace remote sensing platforms, a complete remote sensing earth observation system has been established at home and abroad, providing technical support for large-scale remote sensing monitoring of pests and diseases. Held et al. (2004) analyzed the EO-1 Hyperion hyperspectral image using the DWSI index by analyzing the spectrum data of sugarcane stressed by sugarcane rust, and successfully realized the monitoring of the occurrence range of pests and diseases in the study area. Yuan et al. (2014) obtained high-resolution multispectral images from SPOT-6 satellite and realized powdery mildew monitoring based on maximum likelihood classifier (MLC), mahalanobis distance (MD) and artificial neural network (ANN) in a typical outbreak site in Shannxi, China. The results showed that the monitoring accuracy reached 78%, 84% and 90% of MLC, MD and ANN, respectively, indicating that the ground hyperspectral and multispectral image fusion technology can be applied to remote sensing monitoring of pests and diseases. Lenthe et al. (2007) obtained ground measurement data of wheat yellow rust and powdery mildew through inoculation experiments, as well as corresponding thermal infrared images. By selecting sensitive features and constructing a monitoring model, the overall accuracy reached 88.6%. Yang et al. (2010) compared the multispectral and hyperspectral image information on cotton root rot and concluded that the multispectral image could achieve a relatively satisfactory effect in the remote sensing monitoring and identification of pests and diseases in a large area. Zhang et al. (2014) used MD, partial least squares regression (PLSR), maximum likelihood estimate (MLE), and mixture tuned matched filtering (MTMF) to monitor wheat powdery mildew. At the regional scale, multi-temporal remote sensing satellite images are used to monitor the occurrence and development of wheat powdery mildew. The results showed that the monitoring method coupled with PLSR and MTMF can monitor powdery mildew with an accuracy of 78% at the regional scale. Compared with large-scale satellite remote sensing observations, airborne hyperspectral/multispectral sensors based on aerial remote sensing platforms not only use the spectral characteristics of the target crop, but also need to analyze the image structure and texture characteristics. For example, Kim et al. (2009) extracted texture features such as information entropy and contrast of the acquired

Type

Index Db λb

Differential spectrum

Dy λy SDy Dr λr SDr

Continuum feature

DEP550-750 DEP920-1120 DEP1070-1320 WID550-750 WID920-1120 WID1070-1320 AREA550-750 AREA920-1120 AREA1070-1320

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

SDb

Description or formula The blue edge is generally distributed in the range of 490–539 nm λb characterizes the wavelength at the blue edge Db Characterizes the sum of the first-order differential spectra of 35 bands in the blue edge The yellow edge is generally distributed in the range of 550–582 nm λy characterizes the wavelength at the yellow edge Dy Characterizes the sum of the first-order differential spectra of 35 bands in the yellow edge The red edges are generally distributed in the 670–737 nm band λr represents the wavelength at the red edge Dr Characterizes the sum of the first-order differential spectra of 35 bands in the red edge part Spectral depth in the band range of 550–750 nm Spectral depth in the band range of 920–1120 nm Spectral depth in the band range of 1070–1320 nm Spectral width in the band range of 550–750 nm Spectral width in the band range of 920–1120 nm Spectral width in the band range of 1070–1320 nm The multiplication of DEP and WID in band range of 550–750 nm The multiplication of DEP and WID in band range of 920–1120 nm The multiplication of DEP and WID in band range of 1070–1320 nm

16

TAB. 1.1 – Features and vegetation indices used in discrimination of pests and diseases.

Greenness Index, GI Normalized Difference Vegetation Index, NDVI Triangular Vegetation Index, TVI Photochemical Reflectance Index, PRI Chlorophyll Absorption Ratio Index, CARI

Vegetation index

Modified Chlorophyll Absorption Reflectance Index, MCARI Red-edge Chlorophyll Index, CIRed-edge Structural Independent Pigment Index, SIPI Plant Senescence Reflectance Index, PSRI Normalized Pigment Chlorophyll Ratio Index, NPCI Optimized Soil Adjusted Vegetation Index, OSAVI Simple Ratio Index, SR Water Index, WI Normalized Difference Water Index, NDWI Aphid index, AI Green Normalized Difference Vegetation Index, GNDVI Damage Sensitive Spectral Index, DSSI Healthy Index, HI Ration Triangular Vegetation Index, RTVI

R554/R677 (RNIR − RR)/(RNIR + RR) 0.5*[120*(R750 − R550) − 200*(R670 − R550)] (R570 − R531)/(R570 + R531) ((|a670 + R670 + b)|/(a2 + 1)1/2)*(R700/R670) a = (R700 − R550)/150, b = R550−(a*550)

Introduction

TAB. 1.1 – (continued).

[(R700 − R670) − 0.2*(R700 − R550)]*R700/R670 (RNIR/RE)−1 (R800 − R445)/(R800 + R680) (R678 − R550)/R750 (R680 − R430)/(R680 + R430) (RNIR − RR)/(RNIR + RR + 0.16) R1600/R819 R900/R970 (R860 − R1240)/(R860 + R1240) (R740 − R887)/(R691 − R698) (RNIR − RG)/(RNIR + RG) (R747 − R901 − R537 − R572)/ (R747 − R901 + R537 − R572) (R534 − R698)/(R534 + R698)−0.5*R704 [55*(R750 − R570)−90*(R680 − R570)]/[90*(R750 + R570)]

17

18

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

airborne remote sensing images based on the color co-occurrence matrix method, thereby realizing the detection and disease recognition of pomelo peel, with a classification accuracy of 96.7%. In addition, it is worth noting that in the classification and recognition of multiple pests and diseases, some scholars try to use computer graphics algorithms to identify the characterization information. Wang et al. (2008) used the tomato blast, sheath blight and flax leaf spot texture structure characteristics displayed by drone images to distinguish and monitor the three diseases. Yao et al. (2009) identified a variety of wheat pests and diseases based on the directional consistency characteristics of crops in remote sensing images. Table 1.2 lists the characteristics and application cases of multispectral remote sensing monitoring of pests and diseases based on aviation/aerospace platforms at farm scale and at regional scale.

1.3.2

Crop Pest and Disease Forecasting

In recent years, high space time, high spectral resolution remote sensing data of continuous observation as well as its different bands containing rich information have been used in monitoring crop condition, soil moisture and temperature inversion. However, the occurrence and development of crop pests and disease forecasting by using these surface information has also aroused wide public concern. At the same time, when pests and diseases occur, the incubation and growth of pests and the reproduction, transmission and stress of bacterial spores of diseases require appropriate landscape patterns and habitat conditions. The key environmental information related to occurrence of pests and diseases is the basis of pests and diseases remote sensing to forecast. Peng et al. (2015) analyzed the occurrence characteristics of yellow rust in wheat in Nanchong city, and established a mathematical model for predicting the area and plant rate of yellow rust in the same year. Yang et al. (2010) compared the performance of multispectral and hyperspectral images based on near-ground platform in predicting root rot of cotton, and found that the accuracy of prediction and calculation speed based on multispectral images were satisfied. It is worth noting that the use of multi-temporal remote sensing images for pests and diseases prediction is also an important research topic. Yang et al. (2008) proposed a spectral point and parameter model based on multi-temporal hyperspectral images to predict the occurrence trend of yellow rust in wheat. Zhang et al. (2014a) used multi-temporal HJ-1 image to monitor wheat powdery mildew on at regional scale, and predicted the occurrence of wheat powdery mildew with a prediction accuracy of 78% by analyzing the temporal and spatial distribution characteristics extracted from time series. Helmi (2009) used modified soil adjusted vegetation index (MSAVI2), humidity, and land surface temperature (LST) to conduct ordinary kriging regression to establish a prediction model for wheat sheath blight, and the prediction correlation was 0.852. Chen et al. (2005) studied the correlation between the fractal dimension characteristics of the Gaizhou shoal tide and the shellfish habitat information using high-resolution images of the multi-phase ZY-2 and Landsat-5 images, providing a theoretical basis for the protection and development of Gaizhou shoal. In addition, Ni et al. (1999)

Scale

Cropland scale

Regional scale

Equipment

Imaging multi-spectrometer, thermal infrared imager

Multispectral satellites (Landsat, GF, Sentinel), Thermal Infrared Satellite (Landsat, Aster, HJ)

Platform

Multi-rotor drones, Fixed-wing drones, Traditional big plane

Satellite

Features and applications

The observation range is large, the cost is high, and the accuracy is high. The aerial vehicle is used as a platform to output field pest/disease the matic maps

The observation range is extremely large and the cost is low. Remote sensing satellites are used as data sources to provide the basis for large-scale detection and forecasting

Introduction

TAB. 1.2 – Multispectral pest and disease remote sensing monitoring and applications. Pests and diseases Wheat yellow rust Wheat powdery mildew Wheat aphids Wheat macular disease Grape yellowing disease Rice planthopper Rice blast Celery sclerotium Tomato leaf miner Tomato bacterial leaf spot Beet brown spot Wheat yellow rust Wheat powdery mildew Wheat aphid Rice planthopper Rice dwarf disease Cotton root rot, Maize armyworm, Oriental migratory locust

19

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

used time-series Landsat-5 images to monitor aphid habitat changes, providing an important basis for aphid occurrence prediction in this area. Wilson (2002) used remote sensing images with high spatial resolution and high spectral resolution to carry out remote sensing inversion of habitat landscape structure characteristics, and used the surface parameters of these inversion to predict the spread of wheat diseases and insect pests. Wolter et al. (2008) used multi-temporal Landsat-5 images to invert the habitat parameters of spruce leaf moth hosts, and then forecast the development of the pest. The outbreak of crop pest and disease has caused devastating losses and threats to agriculture and seriously restricted the stability and yield of agricultural products in China. The main crop pest and disease monitoring and forecasting using remote sensing technology could greatly improve the ability of large-scale monitoring and forecasting. The agricultural losses caused by crop pests and diseases, and field protection and control are described in this chapter. Also, the occurrence, epidemic and infestation of main crop pests and diseases in China are mentioned in this book. Finally, the present status and existing problems of crop pest and disease remote sensing monitoring and forecasting are expounded through the hyperspectral and multispectral features and methods. In the following chapters, the existing problems and trends will be discussed and analyzed through methods introduction and example demonstration by combining crop pest and disease multi-scale remote sensing monitoring and forecasting.

References Chen B., Wang K., Li S., et al. (2007) Spectrum characteristics of cotton canopy infected with verticillium wilt and inversion of severity level// Proceedings of First IPIF TC 12 International Conference on Computer and Computing Technologies in Agriculture. Chen X. F., Yang X. M., Wang J. G., et al. (2005) Study on fractal characuerisuics of tidal creeks and information of seashell habitats in the gaizhou beach based on high-resolution satellite images. Acta Oceanolog. Sinica. 27, 39. Del F. A., Reverberi M., Ricelli A., et al. (2010) Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. Int. J. Food Microbiol. 144, 64. Delalieux S., Van A. J., Keulemans W., et al. (2007) Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications. Eur. J. Agron. 27, 130. Elmasry G., Wang N., Vigneault C., et al. (2008) Early detection of apple bruises on different background colors using hyperspectral imaging. LWT - Food Sci. Technol. 41, 337. Graziosi I., Minato N., Alvarez E., et al. (2016) Emerging pests and diseases of South-east Asian cassava: a comprehensive evaluation of geographic priorities, management options and research needs. Pest Manage. Sci. 72, 1071. Han L., Haleem M. S., Taylor M. (2016) Automatic detection and severity assessment of crop diseases using image pattern recognition. Emerging Trends and Advanced Technologies for Computational Intelligence. Springer, Cham. Held A. (2004) Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery. Int. J. Remote Sens. 25, 489. Helmi Zulhaidi M. S., Hamdan N. (2009) Hyperspectral imagery for mapping disease infection in oil palm plantation using vegetation indices and red edge techniques. Am. J. Appl. Sci. 6, 1031.

Introduction

21

Lenthe J. H., Oerke E. C., Dehne H. W., et al. (2007) Digital infrared thermography for monitoring canopy health of wheat. Precision Agric. 8, 15. Lowe A., Harrison N., French A. P. (2017) Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods. 13, 1. Luo J., Huang W., Zhao J., et al. (2013) Detecting aphid density of winter wheat leaf using hyperspectral measurements. IEEE J. Sel. Top. App. Earth Obs. & Remote Sens. 6, 690. Mehl P. M., Chen Y. R., Kim M. S., et al. (2004) Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J. Food Eng. 61, 67. Naidu R. A., Perry E. M., Pierce F. J., et al. (2009) The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Comput. Electron. Agric. 66, 38. Ni S. X., Jiang J. J., Wang J. C. (1999) Landscape ecology of the region surrounding QinghaiLake, Qinghai Province of China based on remote sensing. J. Environ. Sci. 277, 84. Peng J. C., Feng L. B., Bai T. K., et al. (2015) Study on the epidemic characteristics and causes of wheat stripe rust in Nanchong city. J. Agric. 5, 39. Piao S., Ciais P., Huang Y., et al. (2010) The impacts of climate change on water resources and agriculture in China. Nature. 467, 43. Prabhakar M., Prasad Y. G., Thirupathi M., et al. (2011) Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae). Comput. Electron. Agric. 79, 189. Purcell D. E., O'shea M. G., Johnson R. A., et al. (2009) Near-Infrared spectroscopy for the prediction of disease ratings for Fiji leaf gall in sugarcane clones. App. Pectros. 63, 450. Qiao H. B. (2007) Study on remote sensing technology for the wheat aphid and wheat powdery mildew monitoring. In: Chinese Academy of Agricultural Sciences. Shi Y., Huang W., Luo J., et al. (2017a) Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis. Comput. Electron. Agric. 141, 171. Shi Y., Huang W., Zhou X., et al. (2017b) Evaluation of wavelet spectral features in pathological etection and discrimination of yellow rust and powdery mildew in winter wheat with hyperspectral reflectance data. J. App. Remote Sens. 11, 026025. Spinelli F., Noferini M., Costa G. (2006) Near infrared spectroscopy (NIRs): Perspective of fire blight detection in asymptomatic plant material. Acta Horticulturae. 704, 87. Strange R. N., Scott P. R. (2005) Plant disease: a threat to global food security. Ann. Rev. Phytopathology. 43, 83. Sundaram J., Kandala C. V., Butts C. L. (2009) Application of near infrared spectroscopy to peanut grading and quality analysis: overview. Sens. Instrum. Food Qual. Saf. 3, 156. Wang X., Zhang M., Zhu J., et al. (2008) Spectral prediction of phytophthora infestans infection on tomatoes using artificial neural network (ANN). Int. J. Remote Sens. 29, 1693. Wilson M. L. (2002) Emerging and vector-borne diseases: Role of high spatial resolution and hyperspectral images in analyses and forecasts. J. Geographical Syst. 4, 31. Wolter P. T., Townsend P. A., Sturtevant B. R., et al. (2008) Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario. Remote Sens. Environ. 112, 3971. Wu D., Feng L., Zhang C., et al. (2008) Early detection of botrytis cinerea on eggplant leaves based on visible and near-infrared spectroscopy. Trans. Am. Soc. Agric. Biol. Eng. 51, 1133. Yang C. H., Everitt J. H., Fernandez C. J. (2010) Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. Biosystems Eng. 107, 131. Yang K. M., Chen Y. H., Guo D. Z., et al. (2008) Spectral information detection and extraction of wheat stripe rust based on hyperspectral image. Acta Photonica Sinica. 37, 145. Yao Z., He D., Lei Y. (2018) Hyperspectral Imaging for identification of powdery mildew and stripe rust in wheat. 2018 ASABE Annual International Meeting. Yao Q., Guan Z., Zhou Y., et al. (2009) Application of support vector machine for detecting rice diseases using shape and color texture features. 2009 International Conference on Engineering Computation, 79.

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Yuan L. (2015) Identification and Differentiation of Wheat Diseases and Insects with Multi-source and Multi-scale Remote Sensing Data. In: Zhejiang University. Yuan L., Zhang J. C., Shi Y., et al. (2014) Damage mapping of powdery mildew in winter wheat with high-resolution satellite image. Remote Sens. 6, 3611. Zhang J. C., Yuan L., Pu R., et al. (2014) Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat. Comput. Electron. Agric. 100, 79. Zhao M., Ouyang F., Zhang Y. S., et al. (2014a) Characteristics of occurrence and damage from diseases and insect pests in rice production in China during 2000-2010. Biol. Disaster Sci. 37, 275. Zhao M., Ouyang F., Zhang Y. S., et al. (2014b) On occurrence characteristics and damage from diseases and insect pests in wheat in China during 2000-2010. Biol. Disaster Sci. 38, 1. Zhang J., Pu R., Yuan L., et al. (2014a) Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery. Plos One. 9, e93107.

Chapter 2 Mechanism and Method The above chapters focused on the threats of crop pests and diseases, and the significance and status of remote sensing monitoring and forecasting, bringing us some insight into the research content of the book. However, clarifying the spectral response mechanism of pests and diseases, and selecting appropriate feature extraction and modeling methods are the key to crop pest and disease remote sensing monitoring and forecasting. First, this chapter clarifies the spectral response characteristics of healthy crop and infested crop by analyzing the spectral absorption and reflection characteristics and the spectral reflectance changes. Then, we introduce some feature extraction and modeling methods, which are used to extract the features closely related to pest/disease damage and build the correspondence between remote sensing observation data and study object. Finally, the crop pest and disease monitoring and forecasting models are constructed, and used to obtain the occurrence of study area. This chapter aims to comprehensively introduce the physiological mechanisms of the crop, the method of feature extraction and model construction, and provide a theoretical basis for the introduction of crop pest and disease remote sensing monitoring and forecasting.

2.1

Spectral Mechanism

Currently, optical remote sensing is the most widely used data source for crop pest and disease monitoring. For the crop spectral data, different characteristics of absorption and reflection at different wavelengths are shown due to different damaged levels of pests and diseases, which is the main spectral mechanism. The specific spectral response is the basis for crop pest and disease remote sensing monitoring, while this kind of response could be approximated as a function of crop morphology structure, cellular structure, pigments, water content, etc. In Section 2.1.1, we mainly introduce the spectral response of healthy crops. In Section 2.1.2, we mainly introduce the spectral response of crops damaged by pests and diseases.

DOI: 10.1051/978-2-7598-2659-9.c002 © Science Press, EDP Sciences, 2022

24

2.1.1

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

Spectral Response of Healthy Crop

The spectral characteristics of healthy crop mainly depend on its leaves, and the spectral curves of healthy leaves show obvious “peaks and valleys”. Due to the absorption of various pigments (e.g., chlorophyll, carotenoids, anthocyanins), it usually has a lower reflectance in the visible region. Due to multiple scattering from the internal tissues of the leaves, it tends to have a higher reflectance in the near-infrared region. Due to the absorption of water, protein and other carbon-containing components, it exhibits a lower reflectance in the short-wave infrared region. Taking rice as an example, the spectral curve of healthy leaves is shown in figure 2.1. Figure 2.1 shows that in the troughs of the visible bands (blue at 450 nm and red at 670 nm), the spectrum of crops is mainly dominated by various pigments in the leaves. Since pigments strongly absorb blue and red light and reflect green light, healthy crops that people see are green. In the near-infrared region (700–1300 nm), the reflectance and transmittance of crop leaves are similar, with each accounting for 45%–50% of the incident energy, and the absorption rate is mostly less than 5%. In the 740 nm, the reflectivity increases sharply, which is mainly caused by the difference in refractive index between the cell wall and cell space of the crop leaves, which leads to multiple reflections. In the short-wave infrared region, the incident energy of crops is mainly absorbed or reflected, with very little transmission. The spectrum of crops is affected by the total water content of the leaves, and the reflectance of the leaves is approximately negatively correlated with the total water content. Affected by the moisture between and inside leaf cells, the spectral curve of

FIG. 2.1 – Spectral curve of healthy leaves.

Mechanism and Method

25

crops will form obvious reflection peaks at 1450 nm and 1900 nm, and reflection trough at 1600 nm (Lillesand and Kiefer, 1994).

2.1.2

Spectral Response of Crop Pest and Disease

When crops are infested by pests and diseases, the cell tissues in the leaves will be destroyed and the pigment ratio will also change, which make the two absorption valleys in the visible region inconspicuous, while the spectral reflection peak at 550 nm will be flattened. In the near-infrared region, the infested spectrum curve in the range of 760–1000 nm is significantly reduced (figure 2.2). Therefore, according to the spectral curve comparison of damaged crops and healthy crops, it can be determined whether the crops are stressed by pests and diseases. The symptoms of wheat pests and diseases are typical, and the physiological damage mechanism is clear. It is often used as an experimental material for spectral diagnosis. We take wheat yellow rust as an example to introduce its spectral response. For the monitoring of winter wheat yellow rust, the crop canopy reflectance varies greatly with time and space due to the great influence of environmental factors (soil coverage, canopy geometry, atmosphere, etc.) on the spectrum absorption. Models established under different conditions have time and space constraints, which affects the versatility of the model to a certain extent. Therefore, to establish a remote sensing monitoring model for yellow rust, we must first

FIG. 2.2 – Comparison of spectral curves between healthy and yellow rust-infested wheat leaves.

26

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

understand the spectral response of yellow rust in leaf scale. As an important organ of crops, leaves play a decisive role in photosynthesis, growth and nutrient supply of crops, and they also make a large contribution to the canopy spectrum. The spectrum of a single leaf is not affected by factors such as soil and canopy structure, and it is easier to understand the essential characteristics of the disease spot spectrum, which provides a reference and selection basis for further remote sensing monitoring at the canopy level. Figure 2.3 shows the leaf spectrum curves of yellow rust with different severity. The results showed that due to factors such as leaf cell structure damage, water loss and spore powder accumulation, the reflectance curves of leaves with different severity levels were different. Among them, at 550–700 nm, with the increase of yellow rust severity, the spectral reflectance increased. Correlation analysis between single leaf severity and leaf spectrum (400–1000 nm) of wheat yellow rust was conducted, and the correlation coefficient is shown in figure 2.4. The result showed that the spectral reflectance at the wavelength ranging from 550 nm to 700 nm and 750 nm to 1000 nm has significant correlation with leaf severity. The study on the leaf spectrum characteristics of wheat yellow rust showed that the chlorophyll content and the water content of the leaves infested by yellow rust decreased. The leaf cell structure changed with the thickening and enlargement of the yellow rust spore heap. In the visible region (550–700 nm), the spectral reflectance increased significantly; in the near-infrared region, the spectral reflectance decreased.

FIG. 2.3 – Spectral curves of yellow rust-infested single leaf with different severity.

Mechanism and Method

27

FIG. 2.4 – Correlation between single leaf severity and spectral reflectance.

2.2

Spectral Feature Extraction

The realization of remote sensing monitoring of crop pests and diseases needs to be based on specific spectral bands or features. Therefore, an important issue is the selection and extraction of the sensitive characteristics to pests and diseases from a large amount of spectral bands. For spectral feature extraction, the most effective methods are to conduct spectral sensitivity analysis on the existing spectral bands, which mainly include correlation analysis, variance analysis, independent sample T test, and principal component analysis. The results of spectral sensitivity analysis provide a basis for feature selection or monitoring model construction. In Section 2.2.1, we mainly introduce the spectral sensitivity analysis methods in detail. In Section 2.2.2, we mainly introduce the types and forms of spectral features.

2.2.1

Sensitivity Analysis

1. Correlation analysis Correlation analysis refers to the analysis of two or more variable elements to measure the degree of correlation between the variable elements. There needs to be a certain connection or probability between the variables before correlation analysis can be performed. According to the number of variables, it can be divided into univariate correlation and multivariate correlation. According to the form, it can be divided into linear correlation and nonlinear correlation. According to the changing direction, it can be divided into positive correlation and negative correlation. Correlation analysis mainly includes the following 4 steps. 1) Determine whether there is a correlation between the variables and the manifestation of the correlation. 2) Determine the closeness of related relationships. 3) Determine the expression of the correlation.

28

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

4) Check the error of the estimated value. Generally, based on judging whether there is a correlation between variables and what kind of relationship, it is necessary to determine the direction, form and closeness between variables by compiling correlation tables, drawing correlation diagrams, calculating correlation coefficients and determination coefficients, etc. Under the condition of linear correlation, the correlation coefficient “r” indicates the close degree of linear correlation between variables. Pn i¼1 ðxi  x Þðyi  y Þ r ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð2:1Þ Pn 2 Pn 2 i¼1 ðxi  x Þ i¼1 ðyi  y Þ r is the correlation coefficient between x and y. x is the average of all x. y is the average of all y. The range of r is: 0 ≤ |r| ≤ 1, r > 0 indicates a positive correlation between variables, r < 0 is a negative correlation; r = 0 means that there is no linear relationship; |r| = 1 means completely linear correlation; 0 < |r| < 1 indicates that there are different degrees of linear correlation. 2. Variance analysis Analysis of variance, also known as “variation analysis” or “test”, is a statistical concept proposed by Fisher R.A., which is used to test the significance of the difference between the means of two or more samples. The purpose of variance analysis is to find out the factors that have a significant impact on the matter, the interaction between the factors. Variance analysis is a technique that decomposes the total “variation” between data according to the specified sources of variation. The following assumptions need to be met when performing variance analysis: 1) The samples under each processing condition are random. 2) The samples under each processing condition are independent. 3) The samples under each processing condition are from the common distribution population. 4) The sample variance is the same under each processing conditions. According to the type of data, variance analysis can be divided into one-way variance analysis and multi-factor variance analysis. The main steps of variance analysis are: 1) Put forward the original hypothesis: 0 is no difference; 1 is significant difference. 2) Select test statistic. The test statistic used in the variance analysis is the F statistic. 3) Calculate the observed value and probability value of the test statistic. 4) Get the level of significance and make decisions. For one-way variance analysis, after completing the basic analysis described above, you can get a conclusion about whether the control variable has a significant impact on the observed variable. Then, there are several other important analyses

Mechanism and Method

29

that should be done, including homogeneity test of variance and multiple comparison tests. 1) Homogeneity test of variance The homogeneity of variance test is to test whether the overall variance of each observed variable is equal at different levels of the control variable. There is no significant difference in the overall variance of the observed variables at different levels of the control variable, which is a prerequisite for variance analysis. If this prerequisite is not met, then the overall distribution cannot be considered the same. Therefore, it is necessary to test whether the variance is homogeneous. 2) Multiple comparison test The basic analysis of one-way variance analysis can only judge whether the control variable has a significant effect on the observed variable. If the control variable does have a significant effect on the observed variable, it is necessary to further determine how much different levels of the control variable affect the observed variable. 3. Independent sample t test The t test, published by Goster in 1908, used the t distribution theory to infer the probability of a difference, so as to compare whether the difference between the two averages is significant. It is mainly used for normal distribution with small sample content and unknown overall standard deviation. The test is divided into single population test and double population test, and the independent sample t test belongs to double population test. The prerequisites for the independent sample t test are: 1) The two samples should be independent of each other. 2) The sample population should be normally distributed. The realization process of independent sample t test are  as follows. Suppose that the sample set X1 obeys the normal distribution N u1 ; r21 , and sample set X2 follows   the normal distribution N u2 ; r22 . Samples (x11, x12,…, x1n) and (x21, x22,…, x2n) are drawn from these two populations, and the two samples are independent of each other. It is required to test whether there is a significant difference between μ1 and μ2. 1) Establish the null hypothesis, H0: u1 ¼ u2 . 2) Using the Levene F test method to determine whether the variances of the two populations are equal. If the P value corresponding to the F value is less than the significance level, then the two population variances are considered to be unequal. If the P value is greater than the significant level, the two population variances are considered to be equal. 3) Construct statistics. There are two cases. (1) The variance of the two populations is unknown and equal, t¼

x1  x2  ðl1  l2 Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffi Sp n11 þ n12

ð2:2Þ

30

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

Among them; Sp2 ¼

ðn1  1ÞS12 þ ðn2  1ÞS22 n1 þ n2  2

where x1 and x2 represent the mean of the two samples, respectively; S1 and S2 represent the standard deviation of the two samples, respectively; n1 and n2 represent the number of two samples, respectively; and Sp is the combined standard deviation. (2) The variance of the two populations is unknown and unequal, t¼

x1  x2  ðl1  l2 Þ ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffi S12 S22 þ n1 n2

ð2:3Þ

4) Calculate t value and corresponding P value. 5) Conclusion. If the P value is less than the significant level α, then the two population means are considered to be significantly different. If the P value is greater than the significant level α, the two populations are not considered to have a significant difference. 4. Principal component analysis Principal components analysis (PCA) is often called the K-T transform. It is the best orthogonal linear transform based on statistical characteristics with minimal root mean square error. The purpose is to simplify multiple indicators into a few comprehensive indicators. Hyperspectral remote sensing can completely record the spectral curves of observed object and obtain continuous spectral information. Therefore, the number of bands can reach hundreds or even thousands. However, as the amount of information increases, there could be a large amount of redundancy in hyperspectral data due to the high correlation between adjacent bands. PCA constructs an appropriate combination of original variables to generate a series of unrelated new variables, and selects a few new variables that contained as much information as possible on the original variables. On the one hand, this method is helpful for data dimensionality reduction and compression, and at the same time it can enhance the amount of image information. It has been widely used in different vegetation remote sensing data processing, including wide-band remote sensing and narrow-band remote sensing data. In recent years, many scholars have used the PCA to process the hyperspectral images in the study of fruit quality detection and identification of pests and diseases (Guo et al., 2020; Liu et al., 2006; Shahin et al., 2011). 1) The principle of principal component analysis PCA is a method of mathematically reducing the dimensionality of data. The basic idea is to recombine the original numerous indices (x1, x2,…, xp) with certain correlations into a set of relatively small number of uncorrelated comprehensive

Mechanism and Method

31

indices (Fh). Comprehensive indicators should not only reflect the information represented by the original variables to the greatest extent, but also ensure that the new indicators remain independent of each other. Suppose F1 represents the principal component index formed by the first linear combination of the original variables (F1 = a11x1 + a21x2 + ⋯ + ap1xp); the amount of information extracted by each principal component can be measured by its variance. The larger the variance, the more the information F1 contains. It is often hoped that the first principal component F1 contains the largest amount of information. Therefore, the F1 selected in all linear combinations should be the largest variance among all linear combinations of x1, x2,…, xp. F1 is called the first principal component. If the first principal component is not enough to represent the information of the original p indicators, then the second principal component index F2 should be considered. In order to effectively reflect the original information, the existing information in F1 does not need to appear in F2, that is, F2 and F1 must remain independent and unrelated. The mathematical expression is that the covariance Cov (F1, F2) = 0, so F2 is the largest variance among all linear combinations of x1, x2,…, xp that are not related to F1, named as second principal component. The constructed F1, F2,…, Fm (m = 1, 2, …, p) are the first, second, m-th principal components of the original variable index x1, x2,…, xp. 2) The calculation steps of principal component analysis Summarizing the above analysis, it can be seen that the calculation steps of PCA are as follows: (1) Standardize the data Xi ¼

xi  xi si

ð2:4Þ

In the above formula, Xi is standardized data; xi is the mean of xi, and si is the sample standard deviation of xi. (2) Calculate the covariance matrix V of the normalized data matrix X, where V is the correlation coefficient matrix of X. (3) Find the first m eigenvalues of V (k1  k2      km ) and their corresponding eigenvectors (a1 ; a2 ; . . .; am ), and require them to be standard orthogonal. (4) Find the h-th principal component (Fh ). Fh ¼ Xah ¼

p X

ajh xj

ð2:5Þ

j¼1

In the above formula, ajh is the j-th component of the main axis ah. Therefore, the principal component Fh (h ≤ m) is a linear combination of the original variables x1, x2,…, xp, and the combination coefficient is exactly ajh(j = 1,2,…, p).

32

2.2.2

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

Spectral Feature

1. Spectral reflectance of specific wavelength Spectral reflectance is the simplest and the most direct method of spectral analysis, which can better correspond to the interpretation of physiological mechanisms, and is also the basis for the transformation and formation of other spectral features. Usually, finding the effective spectral band reflectance from the pre-processed original spectrum is helpful to discover the basic spectral characteristics and laws of the ground objects. 2. Vegetation index Except for the band reflectance, many scientists construct different forms of vegetation indices based on the relationship between the spectrum and various crop physiological and biochemical characteristics. These vegetation indices are used as the main variable for vegetation remote sensing monitoring. From a mathematical point of view, the vegetation index refers to the linear or non-linear band combination by addition, subtraction, multiplication, division, etc. The key to establishing the vegetation index is how to effectively synthesize the relevant spectral signals, i.e., enhancing vegetation information and minimizing non-vegetation information, due to the strong absorption of green vegetation in the visible region range of 600–700 nm and the high reflection and high transmission in the near-infrared region range of 700–1100 nm. It is often used for multiple combinations such as ratio, difference, and linear combination to form obvious contrast, so as to enhance or reveal the hidden vegetation information. When crops are infested by pests and diseases, their biophysical and biochemical parameters often change greatly. Related researches have also showed that it is feasible to monitor pests and diseases using multispectral/hyperspectral reflectance data and the vegetation index derived therefrom. Jordan (1969) proposed the first ratio vegetation index (RVI) using the ratio of the near-infrared band to the red band. NDVI is widely used to detect stress due to its high correlation with vegetation parameters (Curran, 1980). Although the future of NDVI seems to be promising, the influence of soil background and the bidirectional reflectance difference factor (BRDF) limits its application (Huete, 1988). To reduce the influence of soil background, Huete (1988) proposed the soil adjusted vegetation index (SAVI), and Qi et al. (1994) constructed MSAVI. The atmospherically resistant vegetation index (ARVI) and global environmental monitoring index (GEMI) proposed by Kaufman and Tanre (1996) and Pinty and Verstraete (1992) are widely used due to their insensitivity to atmospheric effects. Penuelas et al. (1993) proposed the water band index (WBI) to quantify the water stress suffered by crops. Adams et al. (1999) proposed the yellowness index (YI) to characterize diseases at the leaf scale. The specific definitions, calculation formulas and sources of some commonly used vegetation indices are shown in table 1.1 and table 2.1. 3. Spectral differential transformation In remote sensing detection of pests and diseases, the spectral reflectance is generally not used directly. Instead, some new spectra or spectral variables are

Vegetation index Triangular Vegetation Index, TVI Specific Leaf Area Vegetation Index, SLAVI Water Band Index, WBI Leaf Measuring-interval Index, LMI Atmospherically Resistant Vegetation Index, ARVI Enhanced Vegetation Index, EVI Modified Soil-Adjusted Vegetation Index, MSAVI Global Environment Monitoring Index, GEMI Soil-Adjusted Vegetation Index, SAVI Difference Vegetation Index, DVI Ratio Vegetation Index, RVI

Formula

References

0:5  ½120ðR750  R550 Þ  200ðR670  R550 Þ

Zhao et al., 2004

NIR=ðRed þ NIRÞ

Lymburner et al., 2000

R950 =R900

Riedell and Blackmer, 1999

R1650 =R830

Parker, 1995

ðNIR  ð2Red  BlueÞÞ=ðNIR þ ð2Red  BlueÞÞ

Kaufman and Tanre, 1996

ð1 þ LÞðNIR  RedÞ=ðNIR þ C1  Red  C2  Blue þ LÞ; C1 ¼ 6:0; C2 ¼ 7:5; L ¼ 1:0 h i 0:5 ð2ðNIR þ 1ÞÞ  ððð2NIRÞ þ 1Þ2  8 ðNIR  RedÞÞ1=2

Verstraete and Pinty, 1996

gð1  0:25gÞ  ðRed  0:125Þ=ð1  RedÞ;     2 2 g ¼ 2 NIR  Red þ 1:5NIR  0:5Red =ððNIR þ RedÞ þ 0:5Þ ð1 þ LÞ  ðNIR  RedÞ=ðNIR þ Red þ LÞ; L ¼ 0:5 NIR  Red NIR=Red

Mechanism and Method

TAB. 2.1 – Vegetation indices commonly used to detect crop stress.

Qi et al., 1994 Pinty and Verstraete, 1992 Huete, 1988 Tucker, 1979 Jordan, 1969

33

34

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

obtained through some transformations to extract information about pests and diseases. Compared with the original spectral reflectance, the spectrum after some transformations can eliminate some background effects to a certain extent and highlight the required information. The spectral differential transformation is one of the most important analytical techniques for the analysis and processing of hyperspectral remote sensing data. Differentiate the spectrum curve or use a mathematical function to estimate the slope of the entire spectrum, and the slope of the spectrum curve obtained from this is called differential spectrum. The derivative of the spectrum essentially reflects the change in the absorption waveform of the crop internal material. In crop spectral analysis, spectral differentiation technology can reduce background noise and improve overlapping spectral resolution. In the atmospheric window of sunlight, the measured spectrum is the mixed spectrum of the ground object absorption spectrum and the atmospheric absorption and scattering spectrum, which is generally expressed in the data image of the reflectance. Research usually uses differential spectroscopy technology to correctly interpret remote sensing data images, eliminate the influence of atmospheric and background noise, and extract characteristic information of target objects. Differential spectra can be divided into first-order derivative spectra and high-order derivative spectra. Due to the discreteness of the spectral sampling interval, the derivative spectra are generally calculated approximately by different methods. R 0 ð kÞ ¼

dRðkÞ Rðki þ 1 Þ þ Rðki1 Þ ¼ dk ki þ 1  ki1

ð2:6Þ

where λi is the wavelength value of the band i; R0 ðkÞ is the first derivative spectrum of the wavelength λi. Figure 2.5 shows wheat canopy spectrum, the soil background spectrum and their first derivative spectrum. It can be found from the figure: (1) Derivative spectrum can be conveniently used to determine the spectral characteristics such as the bending point of the spectral curve, the wavelength position at the maximum and minimum reflectance; (2) In areas where the spectrum changes, such as the blue, yellow, and red edge of the vegetation spectrum, the derivative spectrum can eliminate the interference of the soil background; (3) Derivative spectroscopy is very sensitive to the spectral signal-to-noise ratio. Generally, it can only be applied in the area where the spectral curve changes. For example, the near-infrared reflectance platform is a very important vegetation feature, but after the first derivative processing, the vegetation information in this spectral range is lost. In view of the characteristics of the derivative spectrum, it is more appropriate to use the derivative spectrum to determine the position of the spectral feature, such as the position of the red edge. When the derivative spectral feature is directly used to extract the target feature parameters, it should be used with caution on the basis of proper filtering and denoising. On the basis of spectral transformation, scholars further proposed the spectral position variable, which refers to the wavelength of a certain characteristic point (highest point, lowest point, inflection point, etc.) on the spectral curve. The most

Mechanism and Method

35

FIG. 2.5 – Wheat canopy spectrum, soil background spectrum and their first derivatives. common methods are the red edge effect and spectral absorption characteristic analysis. Vegetation spectral absorption caused by pigments, water content and other dry matter forms blue edge, yellow edge, and red edge in the visible-near infrared region, which are the unique properties that distinguish vegetation from other land types. Therefore, the blue edge, yellow edge, red edge, etc. can be used to estimate crop physiological and biochemical parameters. The red edge is the most commonly used one, which is usually located at the wavelength of 680–750 nm. The change of the red edge is affected by the chlorophyll content, biomass and internal structure parameters in the leaves. When the crop loses its green color due to infection with pests and diseases, the red edge will move towards the blue light; when the crop grows vigorously, the biomass and pigment content become higher, and the red edge will move in the direction of long waves.

36

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

The definition and main characteristic parameters of red edge are shown in figure 2.6. In the figure, Ro is the minimum reflectivity of the red band in the strong chlorophyll absorption band, also called the starting point of the red edge. Rs is the maximum reflectivity in the near-infrared region, and P is the inflection point of the red edge. The position λp at point P is called the red edge, and σ is the width of the red edge (λp − λo). The “red edge” position λp can be easily determined by the first derivative of the reflection spectrum. In addition, the inverse Gaussian function can be used to simulate the red edge and calculate the red edge parameters. This method has been widely used. Horler et al. (1983) and Miller et al. (1990) proposed that the “red edge” of the plant reflectance spectrum can be fitted with a semi-inverted Gaussian curve (IG model). The inverted Gaussian curve function expression is: ! ðko  kÞ2 RðkÞ ¼ Rs  ðRs  Ro Þ exp ð2:7Þ 2r2 In the formula, Rs is the maximum reflectivity in the near-infrared region; Ro is the minimum reflectivity of the red light band in the strong chlorophyll absorption band; λo is the wavelength corresponding to Ro; σ is the standard deviation coefficient of Gaussian function; RP ¼ Ro þ r is the simulated red edge position. Various stresses, such as nitrogen deficiency, drought, pests and diseases, will change the reflection characteristics of crops, thereby changing the position of the red edge. As the IG model is used to fit the “red edge”, the red edge parameters are calculated. Therefore, when there is no hyperspectral data (for example, only a few discontinuous band data in the range of 670–800 nm), the IG model can also be used to fit the red edge and extract the corresponding red edge parameters. Red edge

FIG. 2.6 – Definition of red edge and main parameters.

Mechanism and Method

37

parameters include red edge position, red edge peak value, red edge amplitude, minimum amplitude, red edge area, red edge width, etc. To better extract the effective information of plants, scholars have constructed a series of spectral feature parameters according to the spectral feature location parameters and red edge parameters, including hyperspectral location variables, area variables and vegetation index variables extracted from the first-order differential spectrum. 4. Continuum removal transformation The continuum removal transformation is to capture the shape characteristics of vegetation spectrum curve, which is an effective method to extract the characteristic information of the absorption valley of hyperspectral data. For vegetation, the most easily judged and most sensitive to physiological state is the chlorophyll absorption valley in the red light band. Absorption characteristics are very important in the response of plant leaf tissue structure, pigment content, moisture and protein to reflectance spectrum. The reflectance absorption characteristics proposed by Zheng et al. (1992) include wavelength position (P), depth (H), width (W), slope (K), symmetry (S), area (A) and absolute reflectance of the spectrum, as shown in figure 2.7. The position of the absorption band (P) is the wavelength corresponding to the minimum in the absorption valley. The depth of the absorption valley (H) refers to the degree to which a certain pigment has a lower reflectivity at a certain wavelength point than that of the adjacent wavelength band. The width of the absorption valley (W) is defined as the width at half the depth of the absorption valley. The formula for the symmetry of the absorption valley is S = A1/A, where A1 is the area of the left half of the absorption valley, and A is the overall area of the absorption valley. Area (A) is a comprehensive parameter of width and depth. The angle θ is shown in figure 2.7a, which is the angle between the line connecting Re and Rs and the absorption baseline. The absorption slope (K) is defined as:

FIG. 2.7 – Spectral absorption characteristics (a) Spectral absorption characteristics (b) Normalized spectral absorption characteristics.

38

Crop Pest and Disease Remote Sensing Monitoring and Forecasting K ¼ tan1 ½ðRe  Rs Þ=ðke  ks Þ

ð2:8Þ

In the formula, Re and Rs are the reflectance values of the absorption end point and the absorption start point. While λe and λs are the wavelengths at the absorption end point and the absorption start point. 5. Continuous wavelet feature 1) Introduction to continuous wavelet transform Wavelet transform is an important signal processing method in engineering. It was first proposed by Morlet J. in 1974. After more than 30 years development, a set of important mathematical formal systems has been gradually established, and it has been widely used in informatics, medicine, geophysics, image processing, and speech processing (Huang et al., 2018; Shi et al., 2018; Zhang et al., 2014; Addison, 2005). In wavelet analysis, continuous wavelet analysis (CWA) can decompose the entire spectrum curve on continuous wavelengths and scales, so as to facilitate quantitative analysis of the spectrum. CWA makes correlation analysis between the original spectrum curve and a specific wavelet basis function at different positions and different scales to generate a series of continuous wavelet energy coefficients. This transform can not only capture the information on the intensity, position and shape of the spectrum at the same time, but also extract the high-frequency signal and low-frequency signal of the spectrum. Therefore, it may have a stronger ability to characterize spectral changes than the above-mentioned spectral features. 2) The extraction method of continuous wavelet feature The overall analysis process of CWA includes (1) continuous wavelet decomposition of the original spectral signal; (2) correlation analysis between the wavelet coefficients obtained from the decomposition and the pests and diseases severity; (3) feature selection in the generated correlation coefficient matrix (figure 2.8). The continuous wavelet decomposition of the original spectrum is the most important process. The general form of the mother wavelet is as follows:   1 kb wa;b ðkÞ ¼ pffiffiffi w ð2:9Þ a a where a represents the wave width and b represents the phase. After wavelet decomposition, the energy coefficients at a series of positions and the decomposition scale can be obtained:  Z þ1 

kb w Wf ða; bÞ ¼ f ; wa;b ¼ dk ð2:10Þ a 1 where f(λ) is the reflectance spectrum (λ = 1, 2, …, n, n is the number of bands). The wavelet coefficient Wf (aj, bi) contains two dimensions of i and j, which are the bands (j = 1, 2, …, n) and the decomposition scale (i = 1, 2, …, m), these two dimensional data form a n × m matrix. In addition, the shape of the vegetation absorption

Mechanism and Method

39

FIG. 2.8 – Continuous wavelet transform feature extraction process.

features is similar to the Gaussian and quasi-Gaussian functions. To reduce the computational complexity, only the wavelet coefficients whose decomposition scale is the exponential power of 2 (e.g., 21, 22,…, 210,…) are retained for subsequent analysis. Previous studies have shown that the impact of this omission on the extraction of wavelet feature can be ignored (Cheng et al., 2010).

2.3

Monitoring Method

To achieve remote sensing recognition and differentiation of crop pests and diseases, and extract spectral features sensitive to pests and diseases, appropriate recognition, differentiation, and monitoring algorithms need to be selected or constructed to establish the relationship between spectral features and disaster occurrence. At present, according to the characteristics of different types of pests and diseases, researchers have proposed a variety of methods. According to their different research purposes, monitoring methods can be roughly divided into two categories, i.e. one is to diagnose the types of pests and diseases, and the other is to differentiate the damage degrees of pests and diseases, involving multivariate statistical analysis methods and data mining algorithms. In Section 2.3.1, we mainly introduce the monitoring methods of recognition and degree differentiation of crop pests and diseases. In Section 2.3.2, we mainly introduce the monitoring methods of crop pests and diseases severity.

40

2.3.1

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

Recognition and Differentiation Method

1. Fisher linear discrimination Linear discriminant analysis is a kind of statistical analysis method, which helps to determine new samples’ categories based on discriminant standards. Therefore, this method can be used to discriminate the types of crop pests and diseases. The input variables are characteristics which are sensitive to crop pests and diseases introduced in Section 2.2. It projects the samples onto a straight line through training sample set, and makes the projection points of similar samples as close as possible, and the projection points of different ones as far as possible. When classifying a new sample, it would be projected onto the same straight line, and then the new sample’s class is determined according to the location of the projected points. The basic idea of the linear discriminant analysis is to select the vector that maximizes the criterion function as the optimal projection direction (figure 2.9). The high dimensional problem is reduced to single dimensional one, and after the samples projected in this direction, the maximum inter-class dispersion and the minimum intra-class dispersion achieve the best separability in the space. However, the important assumption is that there must be significant differences for the sample mean values of different groups. For samples with similar mean values and large variance differences, the discrimination is not ideal. Therefore applying discrimination, it is necessary to evaluate the differences in the mean values of the features between different categories. Fisher linear discriminant analysis determines the projection direction through the given training data. For which, we determine the linear discriminant function firstly, and then testing the test data according to this linear discriminant function to obtain the category of the test data. When applying statistical methods to solving the problem of pattern recognition, one of the concerns is dimensionality.

FIG. 2.9 – Basic idea of Fisher linear discrimination.

Mechanism and Method

41

The methods that can be calculated in low-dimensional space are often not feasible in high-dimensional space. Therefore, reducing the number of dimensions becomes the key to dealing with projection problems. We can consider projecting a sample of d-dimensional space to one dimension space. Even if the samples form several compacts and separated sets in the d-dimensional space, they are projected on an arbitrary line. It may make several types of samples mixed and become unrecognizable. However, you can always find a direction in which the projection of the samples can be best separated on a line. The problem is how to find the best and most easily categorized line projection according to the actual situation. This is the problem to be solved by the Fisher linear discriminant method. First, we discuss general mathematical transformation methods from d-dimensional space to one-dimensional space. Supposing that there is a set X containing N pieces of d-dimensional samples x 1 ; x 2 ; . . .; x N . Samples N1 are belong to the category W1, recorded as the subset X1, and samples N2 are belong to the category W2, recorded as the subset X2. Scalars can be obtained by linearly combining the components of xn yn ¼ w T xn ;

n ¼ 1; 2; . . .; Ni

ð2:11Þ

In this way, a set of N one-dimensional samples yn can be obtained, which can be divided into two subsets of Y1 and Y2. Geometrically, if ∥w∥ = 1, each yn is the corresponding projection of xn onto a line with direction w. In fact, the absolute value of w is irrelevant. It only makes yn multiply by a scale factor. It is important to choose the direction of w. The different directions of w will make the samples separable to different degrees after projection, which will directly affect the recognition effect. Therefore, the solution to finding the best projection direction is to the problem of finding the best variable w. Before defining the Fisher criterion function, we first define the necessary basic variables: 1) In d-dimensional space Mean vector of various samples mi: 1X mi ¼ x; i ¼ 1; 2 N x2xi

ð2:12Þ

Sample intra-class dispersion matrix Si and total intra-class dispersion matrix Sw: Si ¼

X

ðx  mi Þðx  mi ÞT ; i ¼ 1; 2

ð2:13Þ

x2xi

Sw ¼ S1 þ S2

ð2:14Þ

Inter-class dispersion matrix Sb: Sb ¼ ðm1  m2 Þðm1  m2 ÞT

ð2:15Þ

42

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

Sb is a semi-definite symmetric matrix, and it is usually non-singular when N > d. Sb is also stacked into a positive semi-definite matrix. In both cases, its rank is equal to a maximum of 1. 2) In one-dimensional space Means of various samples m i : mi ¼

1 X y N y2Y

ð2:16Þ

i

2

Sample intra-class dispersion S i and total intra-class dispersion S w : X 2 Si ¼ ðy  m i Þ2 ; i ¼ 1; 2

ð2:17Þ

y2Yi

2

2

Sw ¼ S1 þ S2

ð2:18Þ

Defining the Fisher criterion function. It is hoped that after the projection, various types of samples are separated in the one-dimensional space; at the same time, all kinds of samples are expected to be as dense as possible. Therefore, the Fisher criterion function can be defined as: JF ðw Þ ¼

ðm 1  m 2 Þ2 2 S1

2 þ S2

¼

w T Sb w w T Sw w

ð2:19Þ

JF(w) in the above formula is a generalized Rayleigh quotient, which can be solved by the Lagrange multiplier method. Let the denominator be equal to a nonzero constant, that is, let wTSww = c ≠ 0, and define the Lagrange function as:   ð2:20Þ Lðw; kÞ ¼ w T Sb w  k w T Sw w  c where λ is a Lagrange multiplier. Finding the partial derivative of w by formula (2.14), we get: @Lðw; kÞ ¼ Sb w  kSw w @w

ð2:21Þ

Let the partial derivative be zero, we get: Sb w  ¼ kSw w 

ð2:22Þ

where w* is the solution of extreme value of JF(w), then we get, w  ¼ Sw1 ðm1  m2 Þ

ð2:23Þ

w* is the solution that maximizes the Fisher criterion function JF(w), that is, the best projection method from d-dimensional space to one-dimensional space. With w*, using equation (2.11), you can project the d-dimensional sample xn to one

Mechanism and Method

43

dimension, which is actually a kind of projection from multi-dimensional space to one-dimensional space. This is mapping the sample set X in the d-dimensional space into the one-dimensional sample set Y. The direction w* of one-dimensional space is the best relative to the Fisher criterion JF(w). We still have not solved the classification problem. However, d-dimensional problems have been transformed into one-dimensional classification ones. In fact, as long as a threshold y0 is determined and the projection point yk is compared with the threshold y0, a decision can be made. 2. Neural network Artificial Neural Networks (ANNs) are a kind of algorithms that imitate the behavioral characteristics of human neural networks and perform information processing. There are some inputs and outputs, and the mechanism of how to get the output from the inputs is not clear, then we can regard the process from inputs to outputs as a “network”, and the outputs are obtained from the inputs by continuously adjusting the weights between the nodes. Then, when after the training is over, we give an input, and the network will calculate an output based on the weights. This is the simple principle of neural networks. With the rapid development of ANNs in various aspects, people began to pay attention to their application in crop pests and diseases monitoring. Therefore, this method can be used to discriminate the types of crop pests and diseases. The inputs of the networks are the characteristics sensitive to crop pests and diseases as described in Section 2.2. Neural networks generally have many layers, which are divided into input layer, output layer, and hidden layer. The more the layers, the more accurate the calculated result, but the longer the time it takes. So the network layer is often designed according to requirements in actual applications. At present, there are at least thirty different neural networks proposed and used in applications and researches. The three neural network models are mainly introduced below. 1) Back propagation neural network (BPNN) BPNN is a multi-layer feedforward network trained according to the error back propagation algorithm. Its structure is shown in figure 2.10. The basic idea of the BPNN is giving the network initial weights and thresholds, calculating the network’s output forward, modifying the network’s weights and thresholds in the reverse direction, and then repeating the training to minimize the error. The specific steps of the BPNN are as follows. Supposing that a three-layer forward network has N input units, M output units, with the number of units in the hidden layer is L. The activated function of the neuron is the Sigmoid function, and the number of training samples is P. The input vector is Xp = (xp1, xp2,… ,xpN)T, p = 1, 2,…, P, and the output vector is Yp = (yp1, yp2,…, ypM)T, p = 1, 2,…, P, the ^ p ¼ ð^ yp1 ; y^p2 ; . . .; y^pM ÞT , p = 1, 2,…, P, and the output expected output vector is Y error is E.

44

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

FIG. 2.10 – BPNN structure.

E¼

P X

Ep

ð2:24Þ

p¼1

Ep ¼

M  2 1X ypj  y^pj ; p ¼ 1; 2; . . .; P 2 j¼1

ð2:25Þ

The BPNN needs to minimize E by modifying the weight wj (assuming the thresholdPis 0). For the j neuron uj of a layer in the network, its current weight sum is Netpj ¼ i wji opi , where opi is the output of the previous layer. The output of the neuron uj is opj = f(Netpj). When uj is the input unit, opj = xpj. Then the modified formula of weight of the neuron uj is Δpwji = ηδpjopj, where the     P 0 output layer opj ¼ y^pj  ypj fj0 Netpj , hidden layer dpj ¼ fj ðNetpj Þ M j¼1 dpk wkj , and the parameter η is the learning rate. Therefore, the BPNN actually propagates the input information forward along the network, propagates the error signal backward along the network, and corrects the weights, so that the input-output mapping can be learned from the training samples for the multi-layer forward neural network. It uses the simplest gradient method in optimization to modify the weights achieving a non-linear transformation from input space to output space. But this problem is a non-linear optimized problem, so there is a local minimum extreme value. In practice, some methods are commonly used to make the system jump out of the local minimum point with large errors.

Mechanism and Method

45

2) Probabilistic neural network (PNN) Probabilistic Neural Network (PNN) is a new type of neural network combining radial basis function neurons and competitive neurons. It has the characteristics of simple structure and fast training. It is widely used and especially suitable for solving pattern classification problems. In pattern classification, its advantage is that the linear learning algorithm can be used to accomplish the work of the previous nonlinear algorithms, while maintaining the high-precision characteristics of nonlinear algorithms. The network structure is shown in figure 2.11. PNN consists of three layers of neurons, namely the input layer, the radial basis layer, and the competition layer. The input layer corresponds to the sensitive spectral band of the pest/disease or a new spectral variable extracted by various methods. The second layer is radial neurons. The number of hidden neurons in the network is the same as the number of input sample vectors. The third layer uses the competition layer, which is the output layer of the network, and the number of neurons is equal to the number of pest/disease categories in the training sample data that need to be classified. The classification of PNN is as follows. First, an input pattern vector is provided for the network, the radial basis layer calculates the distance between the input vector and the sample input vector, and the output of this layer is a distance vector. The competition layer accepts the distance vector as the input vector, calculates the probability of each pattern appearing, and outputs “1” corresponding to the element with the highest probability through the competition transfer function, which is a type of pattern; otherwise, it outputs “0” as other patterns. Taking wheat aphids as an example, healthy wheat samples are assigned to category “0”, and wheat affected by aphids is assigned to category “1”. The output result is either 0 or 1.

FIG. 2.11 – PNN structure.

46

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

3) Learning vector quantization (LVQ) LVQ algorithm is a learning method for training the competition layer. It evolved from the Kohonen competition algorithm. LVQ has the advantages of simple network structure, and the input vectors do not need to be normalized or orthogonalized. Therefore, it is widely used in the field of pattern recognition and optimization. LVQ neural network consists of three layers of neurons, namely the input layer, the hidden layer, and the output layer. The network is fully linked between the input layer and the hidden layer, and partially connected between the hidden layer and the output layer. Both hidden neurons, also known as Kohonen neurons, and output neurons have binary output values. When a pattern is sent to the network, the hidden neuron whose reference vector is closest to the input pattern wins the competition by being excited, so it is allowed to produce “1”, while the other hidden neurons are forced to produce “0”. The output neurons that produce “1” classify the input patterns, and each output neurons are represented by a different category. The LVQ network structure is shown in figure 2.12. In this study, the input layer neurons are sensitive spectral bands or spectral variables, the trained output layer neurons correspond to the number of categories of pests and diseases. It should be noted that “0” cannot be used to replace a certain category, otherwise the output of the LVQ network cannot be displayed. Taking wheat aphid as an example, healthy wheat samples are given a category of “1”, while wheat affected by aphid is given a category of “2”.

FIG. 2.12 – LVQ structure.

Mechanism and Method

47

3. Support vector machine Support vector machine (SVM) is a new machine learning method built on the basis of statistical theory. It can successfully deal with many problems such as regression (time series analysis) and pattern recognition (classification problem and discriminant analysis) and can be extended to the fields of prediction and comprehensive evaluation. SVM represents the training sets as points in the space, and then finds a hyperplane. The training sets of different categories are separated as far as possible. Finally, the new samples are projected to the same space, and the category is determined based on which side of the hyperplane they fall on. Therefore, SVM can be used for discriminant analysis of different types of crop stress. The input of this model is the spectral characteristics sensitive to crop pests and diseases introduced in Section 2.2. SVM is an implementation of the structural risk minimization inductive principle. To minimize the upper bound of expected risk, SVM minimizes confidence of vapnik chervonenkis (VC) dimension under the condition of fixed learning experience risk. 1) Principle of SVM The given training set is (xi, yi), xi 2Rk, yi 2 {+1,−1}, i = 1, 2,…, n. If all vectors in the training set can be correctly divided by a hyperplane and the distance between the heterogeneous vectors closest to the plane is the largest, then the hyperplane is called the optimal hyperplane, as shown in figure 2.13. Among them, the heterogeneous vector closest to the hyperplane is called support vector, and a set of support vectors can uniquely determine a hyperplane. SVM is developed from the optimal classification surface in the case of linear separability. Its hyperplane is written as w  x þ b ¼ 0. We conduct normalization to make the sample set meet the following requirements: yi ðw  xi þ bÞ  1  0;

i ¼ 1; 2; . . .; n

ð2:26Þ

Due to the distance between the support vector and the hyperplane is 1/‖w‖, the problem of the hyperplane is transformed into the minimum of (2.27) under the constraints of (2.26):

FIG. 2.13 – Optimal hyperplanar.

48

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

Uðw Þ ¼

kwk2 2

ð2:27Þ

Statistical learning theory states that in N dimensional space, if the samples are distributed in a hypersphere with radius R, then the VC dimension of the regular hyperplane constituent index function set f ðx; w; bÞ ¼ sgnfðw  xÞ þ bg, which satisfies the condition ‖w‖ ≤ A, satisfies the following bounds:    h  min R2 A2 ; N þ 1 ð2:28Þ It can be known from the above formula that the confidence of the VC dimension can be minimized by minimizing ‖w‖2. 2) Least squares support vector machine Standard support vector machines solve constrained quadratic programming problem, and the number of constraints is equal to the sample size. Therefore, the solution workload for large samples is quite large and the training time is long. The least squares support vector machine (LSSVM) algorithm is developed on the basis of SVM. It is a variant of the standard support vector machine. It replaces the insensitive loss function in the SVM with a quadratic loss function and changes the inequality constraints to equality constraints; the solution turns into solving a set of equations, which avoids solving quadratic programming problems and the solution speed becomes faster. The description of LSSVM classification algorithm is as follows. If the number of sample points in the training sample data set is n, (xi, yi), xi 2 Rn, yi 2 {+1, −1}, i = 1, 2,…, n. Among them, xi is input data and yi is output data. In the non-linear classification, by introducing the non-linear mapping Φ(x): RA → H, the training data of the input space is mapped into a high-dimensional space H, and the optimal hyperplane is constructed in the feature space H. The optimization problem expression of LSSVM is: n 1 1 X min J ðw; nÞ ¼ kw k2 þ c n2 w;b;n 2 2 i¼1 i   s:t yi w T /ðxi Þ þ b ¼ 1  ni ; i ¼ 1; 2; . . .; n

ð2:29Þ

The first term of the objective function corresponds to the model generalized capability, and the second term corresponds to the model accuracy. w 2 H is the weight vector. ξ 2 R is the relaxation factor. b 2 R is deviation. γ is adjustable parameter, which is similar to the parameter C of SVM, and is used to control J(w, ξ). Introduction of Lagrange function: n n X    1 1 X Lðw; b; n; aÞ ¼ kwk2 þ c n2i  ai yi w T /ðxi Þ þ b  1 þ ni 2 2 i¼1 i¼1

ð2:30Þ

Mechanism and Method

49

Among them, αi, i = 1, 2,…, n are Lagrange multipliers. According to the karush–kuhn–tucker (KKT) condition (Tucker, 1979), solve the partial derivatives of w, b, ξi, α, and make the partial derivatives equal to zero. 8 N P > @L > ¼0 ) w¼ ai yi /ðxi Þ > @w > > i¼1 > > < N P @L ) ai yi ¼ 0 ð2:31Þ @b ¼ 0 > i¼1 > > @L > > > @n ¼ 0 ) ai ¼ cni >  : @Li T @ai ¼ 0 ) yi w /ðxi Þ þ b þ ni  1 ¼ 0 Equation (2.31) can be transformed into the following matrix equation: 2 32 3 2 3 w 0 I 0 0 Z T 6 0 0 0 Y T 76 b 7 6 0 7 6 76 7 ¼ 6 7 ð2:32Þ 4 0 0 cI I 54 n 5 4 0 5 a Ln Z Y I 0 h iT In formula (2.32), as a n × n matrix, Z ¼ /ðx1 ÞT y1 ; /ðx2 ÞT y2 ; . . .; /ðxn ÞT yn , Y = [y1, y2,…, yn], ξ = [ξ1, ξ2,…, ξn], α = [α1, α2,… αn], Ln = [1, 1,…, 1]. Eliminating w and ξ can get the equation.

   0 YT b 0 ¼ Y X þ 1c I a Ln

ð2:33Þ

where Ω is a symmetric matrix of n × n, Ω = ZZT = [Ωij]n×n. Applying the Mercer condition, we can get Ωij = yiyj ϕ(xi)Tϕ(xj) = yiyj K(xi, xj), i, j = 1, 2,…, n. K(xi, xj) is a kernel function that satisfies the Mercer condition. The expression (2.32) is expressed in the form of a kernel function, which can be transformed into a matrix equation: 2 3 2 3 2 3 0 y1  yn b 0 6 y1 y1 yn K ðx1 ; xn Þ þ 1=c    7 6 a1 7 6 1 7 y y K ð x ; x Þ 1 n 1 n 6 7 6 7 6 7 ð2:34Þ 6 .. 7  6 .. 7 ¼ 4 .. 5 .. .. .. 4 . 5 4 . 5 . . . . 1 an yn y n y 1 K ðx n ; x 1 Þ    yn yn K ðxn ; xn Þ þ 1=c Solve the above matrix equation to get the decision function. y ðx Þ ¼

n X

ai K ðx; xi Þ þ b

ð2:35Þ

i¼1

4. Relevance vector machine Relevance vector machine (RVM) is a new supervised learning method based on the kernel function method of Bayesian framework. It has the same functional form as SVM, and bases on kernel function mapping to transform nonlinear problems in

50

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

low-dimensional space into linear problems in high-dimensional space. Therefore, RVM can be used for discriminant analysis of different types of crop stresses. The input of this model is the spectral characteristics sensitive to crop pests and diseases introduced in Section 2.2. The model construction method based on RVM is the same as the model construction method based on SVM. RVM performs machine learning under the Bayesian framework by defining the Gaussian prior probability controlled by hyper-parameters and uses auto relevance determination (ARD) to remove irrelevant points. To obtain a sparse model, during the iterative learning process of the sample data, the posterior distribution of most parameters tends to zero, while the learning samples corresponding to nonzero parameters are only represent prototype samples in the data, and not related to the samples of the decision domain. Therefore, these samples are named as relevance vectors, which reflect the core characteristics of the data. Compared with SVM, the greatest advantage of RVM is that it dramatically reduces the calculation of the kernel function, and also overcomes the shortcomings of the selected kernel function which must meets the Mercer condition, while the number of kernel functions required will increase with the number of training samples, making the training time is relatively long. RVM has achieved good results in terms of obtaining the sparseness of the solution and the probability of the predicted value. 1) RVM model Assuming that the training set fxn ; tn gN n¼1 is the target, values tn are distributed independently, and the input value xn is an independently distributed sample. The relationship between the input x and the target t can be expressed by the formula (2.36), where ξn is the additional noise and satisfies the Gaussian distribution of the following formula (2.37), where the expectation is 0 and the variance is σ2. σ2 is assumed to be an unknown parameter in the model, and could be got by iteration during data training, which can be obtained from equations (2.36) and (2.38). tn ¼ y ðxn ; w Þ þ nn

ð2:36Þ

  nn N 0; r2

ð2:37Þ

    P tn jw; r2 ¼ N Ux; r2

ð2:38Þ

Among them, Φ is a structural matrix composed by kernel functions, with Φ = [φ(x1), φ(x2),…, φ(xn)]T and φ(xn) = [1,k(xn,x1), k(xn,x2)…k(xn,xN)]T. To prevent over-adaptation of maximum likelihood estimation of w and σ2, the ARD prior probability distribution is defined. P ðW jaÞ ¼

N Y i¼0

  N xi j0; a1 i

ð2:39Þ

Mechanism and Method

51

Among them, α = (α0, α1,…, αN) is a vector composed by super-parameters, assuming that the super-parameter α and the noise parameter σ2 obey the Gamma prior probability distribution. P ðai Þ ¼ Gammaða; bÞ

ð2:40Þ

  P r2 ¼ Gammaðc; d Þ

ð2:41Þ

Gammaða; bÞ ¼ CðaÞ1 ba a a1 eba Z 1 where; CðaÞ ¼ t a1 et dt

ð2:42Þ

0

The parameters a and c are scale parameters of the Gamma distribution. To achieve the prior hypothesis without information, the values are generally 10−4. Using graph theory knowledge, the relationship between the above parameters α、 w、σ2、t, can be expressed as a directed acyclic graph as follows (figure 2.14). 2) Regression model Given a training sample set fxn ; tn gN n¼1 , through the learning of these training samples, the RVM model can learn super parameters αi, σ2, and μ from it. The posterior probability distribution based on weight depends on the optimal values of the variables αMP and r2MP . When some test sample data x* are newly input, the posterior probability distribution of the target data t* can be expressed by the following relationship. Z       2 ð2:43Þ P t jt; aMP ; rMP ¼ P t jw; r2MP P wjt; aMP ; r2MP dw

FIG. 2.14 – RVM structure.

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

We can simplify formula (2.43):     ð2:44Þ P t jt; aMP ; r2MP ¼ N y ; r2    It can be seen that P t t; aMP ; r2MP satisfies the Gaussian distribution, the expectation is y* and the variance is r2 , where y ¼ lT uðx Þ

ð2:45Þ

The true value of t* can be predicted by formula (2.43). Formula (2.44) gives the sum of the expected errors of the two predictor variables, one of which is noise error, and the other is the error caused by the uncertainty of the weight w. In practice, if it is solvable, which is in the error term, the value of parameter w can be obtained by the value of parameter μ. 5. Random forest Random forest (RF) is an algorithm based on decision trees. It is composed of a set of independent, unnamed decision trees. RF assumes that the original training set has N instances, and each instance has M attributes. In the process of forest construction, “randomness” is manifested in two aspects. (1) In each iteration, a new training set is replaced with a sample, while the new training set is the same size as the original training set. (2) Choose M attributes from all attributes, instead of choosing the best split attribute in each node, and then these M attributes are used to subdivide the node according to the principle of decision tree algorithm (figure 2.15). RF will generate multiple decision trees, each of which learns and makes predictions independently, by counting the results of each decision tree. RF selects the result with the highest number of votes. Therefore, RF can be used for discriminant analysis of different crop stresses types. The input of this model is the spectral characteristics which are sensitive to crop pests and diseases introduced in Section 2.2. RF is an integrated learner composed by classification and regression decision tree, h ðX; hk Þ; k ¼ 1; . . .; N , where X is the input variable vector, hk is random vector of the first k tree, and the generated random variables with h1 , h2 , h3 ,…, hN are independent distribution relationship. X is the input variable. hk could determine the prediction result of the first k tree corresponding to input variable X. The prediction result of RF is determined by the results of all decision trees. For the classification problem, the RF gives the most appropriate label according to the voting results. For regression problems, the average of all decision tree prediction results will be given. For instance, if the problem is about regression in nature, its formal mathematical expression is: Y ¼

N X

h ðX; hk Þ

ð2:46Þ

k¼1

where, Y represents the prediction result of RF; N is the total number of decision trees. The value of N will affect the generalization ability of RF, so the optimal result is generally obtained by means of parameter adjustment.

Mechanism and Method

53

FIG. 2.15 – Basic idea of RF.

2.3.2

Severity Monitoring Method

To monitor the severity of crop pests and diseases, appropriate estimation algorithms need to be selected. The severity monitoring methods can be divided into continuous description and discrete description. Among them, regression analysis and partial least square method are continuous descriptions. SVM and RVM are discrete descriptions. 1. Regression analysis Regression analysis is based on a large number of observations, using mathematical statistics to establish a regression relationship function expression between the dependent variable and the independent variable. In regression analysis, when the causal relationship involves one dependent variable and one independent variable, it is called univariate regression analysis; when the causal relationship involves one dependent variable and two or more independent variables, it is called multiple regression analysis. Depending on the number of independent variables, it can be univariate regression or multiple regression. In addition, regression analysis is divided into linear regression analysis and nonlinear regression analysis based on whether the function expression describing the causal relationship between the independent variable and the dependent variable in linear or nonlinear way. Depending on the nature of the problem being studied, it can be a linear regression or a nonlinear regression. Generally, the linear regression analysis method is the most basic analysis method. When encountering a nonlinear regression problem, it can be transformed into a linear regression problem by mathematical methods. Regression analysis method is used to predict the future value of a random variable, according to the changes of one or a group of independent variables. Regression analysis is required to establish a regression equation that describes the correlation between variables (figure 2.16). Therefore, regression analysis can be used for

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

FIG. 2.16 – Basic idea of regression analysis. monitoring the severity of different crop stresses types. The input of this model is the spectral characteristics which are sensitive to crop pests and diseases introduced in Section 2.2. The following is the introduction to multiple stepwise regression. Multiple stepwise regression is a multivariate statistical method for establishing optimal regression equations. Firstly, the introduced factors are tested, and the significant one is retained, while the insignificant one is eliminated. Secondly, for each new factor introduced, the new factor introduced in front should be tested, and the significant one is retained, while the insignificant one is eliminated. This process is repeated until all the factors entering in the equation are significant and all the factors not entering in the equation are insignificant, then the optimal regression equation is obtained. In multiple stepwise regression, in order to facilitate calculation, a standardized multiple linear regression equation model is usually used, and its canonical systems are 1 0 10  1 0 b1 r1y 1 r12    r1p B r21 1    r2p CB b2 C B r2y C C B CB C B  ð2:47Þ B .. .. CB .. C ¼ B .. C; Rxx b ¼ Rxy .. @ A @ A @ . A . . . . rp1

rp2



1

bp

rpy

To solve canonical systems (2.47) by using the compaction algorithm, the augmented matrix is

Mechanism and Method

55 0

Rð0Þ

ð0Þ

B B B B ¼B B B B @

ð0Þ

r11

ð0Þ

r21 .. .

ð0Þ

ð0Þ



ð0Þ

r1p



r2p

r12 r22 .. .

ð0Þ

rp1

rp2

ry1

ry2

ð0Þ

ð0Þ

ð0Þ

ð0Þ ð0Þ

ð0Þ

r1y

ð0Þ

r2y

.. .. . .  

ð0Þ

rpp ð0Þ ryp

ð0Þ

rpy ð0Þ ryy

1 C C C C C C C C A

ð2:48Þ

ð0Þ

Among them, rij ¼ rij ; riy ¼ riy ; ryy ¼ 1. It is convenient to introduce and exit any independent variable into and out of the regression equation with compaction algorithm. 2. Partial least squares regression Partial least squares regression (PLSR) is a new multiple statistical data analysis method developed on the basis of multiple linear regression. It is also called the second-generation regression method. Partial least squares method can be used to better solve many problems that could not be solved by ordinary multiple regression in the past. It can simultaneously implement regression modeling, simplify data structure, and effectively overcome multiple correlations between variables. PLSR does not consider the regression modeling between the dependent and independent variables directly. First, it uses component extraction to extract variables that have the best explanatory ability for the system, then establishes the regression relationship between the new variables and the dependent variable, and finally converts them into regression equations for the original variables. Therefore, PLSR can be used for monitoring the severity of different types of crop stresses. The input of this model is the spectral characteristics which are sensitive to crop pests and diseases introduced in Section 2.2. In analysising and modeling multi-variables, the common methods include the establishment of explicit models using least squares regression and the use of ANNs for learning and training. All of these methods have some drawbacks in their use. The classic least squares regression method is difficult to overcome the multiple correlations between variables, and the artificial neural network is poor in interpreting the model. None of these methods have the ability to filter variables, but partial least squares method can be used for variable screening, effectively overcome multiple correlations between variables, and allow regression analysis and modeling under the condition that the number of sample points is less than the number of variables. 1) Basic idea of PLSR PLSR analysis is not directly considered the regression modeling of the dependent and independent variables. It uses the idea of component extraction to comprehensively screen the information in the variable system and selects a new comprehensive variable that has the best interpreted ability for the system. The regression relationship between the new variable and the dependent variable is established, and finally the regression equation of the original variable is expressed.

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

56

Assuming that there are q dependent variables {y1, y2,…, yq} and p independent variables {x1, x2,…, xp}. After observing n sample points, the independent variable and dependent variable data table XY are formed. PLSR extracts components t1 and u1 in X and Y. When extracting these two components, t1 and u1 must meet the following two conditions, i.e., t1 and u1 should carry the variation information in their data as much as possible first, and t1 and u1 should have the greatest correlation. If the above conditions are met, then t1 and u1 contain the information of data tables X and Y, and the independent variable component t1 has the strongest explanatory power to the dependent variable component u1. After the first components t1 and u1 are extracted, the PLSR is applied to the regression of X to t1 and Y to u1. If the regression equation meets the preset accuracy, the algorithm stops. Otherwise, the participation information of X interpreted by t1 and the residual information of Y interpreted by u1 will be used for the component extraction in the second round, until the accuracy meets the requirements. If a total of m components (t1, t2,…, tm) are extracted from X, PLSR will be performed by the regression of yk (k = 1, 2,…, q) on t1, t2,…, tm, and then expressed as yk’s regression equation with t1, t2,…, tm. 2) PLSR algorithm Let E0(n × p) be the normalized independent variable data matrix, and F0 (n × 1) is the corresponding dependent variable vector, then the formula for calculating the component ti is: ti ¼ Ei1 Wi

ð2:49Þ

Among them, Wi is the eigenvector corresponding to the maximum eigenvalue of ET F

T F0 F0T Ei1 , and the calculation formula is: Wi ¼ F Ti1Ei10 . T represents the matrix Ei1 0

the transposed matrix. Ei−1 is the residual obtained by regression of the independent variable matrix Ei−2 on the component ti−1. The formula is Ei ¼ Ei1  ti pTi , and pi ¼

T Ei1 ti . kti k2

The specific selection of several components can be determined by cross-validation. When adding new components does not significantly improve the prediction error of the equation, the extraction of new components is stopped. Add a total of k components into the selection, and establish a regression equation. ^0 ¼ r1 t1 þ r2 t2 þ    þ rk tk F

ð2:50Þ

b 0 can be written as a linear expression of Since ti as a linear combination of E0, F E0 . ^ 0 ¼ E0 b F where b ¼

Pk

 i¼1 ri Wi , ri

¼

F0T ti , tiT ti

and Wi ¼

Qi1

j¼1 ðI

ð2:51Þ  Wj pTj ÞWi with I is the matrix.

Finally, according to the standardized inverse operation, it can be transformed into

Mechanism and Method

57

the regression equation of the dependent variable to the original independent variable. 3) Cross-validation of PLSR model PLSR analysis is the same as other modeling methods. When the number of components increases, the error will be reduced, and the prediction accuracy of the model will be improved. Determining the number of extracted components is one of the key issues of PLSR. Cross-validation is generally used to determine the number of optimal components in PLSR. First, use all sample points and extract the first h partial least squares components for regression modeling, and set b y ij ðhÞ as i sample point, by using this model, to calculate the simulated value corresponding to the original data yi. Then the sum of squared errors Sss,h of the dependent variable Y = (y1, y2,..., yp) is: Sss;h ¼

p X n  X

2 yij  b y ij ðhÞ

ð2:52Þ

j¼1 i¼1

Then, delete the sample point i and extract the first h partial least squares components for regression modeling, and set b y ðiÞj ðhÞ as i sample point, by using this model, to calculate the simulated value corresponding to the yj (j = 1, 2,…, p). Then the prediction sum of squared errors SPRESS,h is: SPRESS;h ¼

p X n  2 X yij  b y ðiÞj ðhÞ

ð2:53Þ

j¼1 i¼1

The increase of the component brings the perturbation error of the sample point. If the robustness of the equation is not good, it is very sensitive to the change of the sample point. This perturbation error will increase the sum of the squared prediction errors of the dependent variable y. If the perturbation error of the h component regression equation can be smaller than the simulated value of the h−1 counterpart to a certain extent, it is considered that adding one component th will significantly S improve the prediction accuracy. Therefore, the smaller ratio of SPRESS;h the better. ss;h1 For the dependent variable y, the cross validation of the component th is defined as follows. Qh2 ¼ 1 

SPRESS;h Sss;h1

ð2:54Þ

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffi At SPRESS;h  0:95 Sss;h1 , that is, when the cross validation of component th is greater than or equal to 0.0975, the introduction of a new component th will significantly improve the prediction ability of the model.

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

3. Fisher linear discrimination For details, see Section 2.3.1. In addition to the two methods described above to quantitatively estimate the pest index (PI)/disease index (DI), you can also use Fisher discriminant analysis to make qualitative classification judgments. For example, the severity of pest/disease on the leaves is divided into three levels, i.e. absence, slight and severe, based on the extent of infection. Therefore, the estimation of the pest/disease severity is transformed into the problem of classification and discrimination. 4. Neural networks For details, see Section 2.3.1. In this section, the output of the neural network is the severity of pests and diseases occurrence, which can be continuous PI/DI or qualitative severity. 5. Support vector machine For details, see Section 2.3.1. In this section, you can use this method to perform regression fitting on PI/DI, and then estimate the occurrence of pest/disease. You can also use this method to classify the severity of pest/disease. 6. Relevance vector machine For details, see Section 2.3.1. In terms of pest/disease severity estimation, this method is the same as the SVM.

2.4

Forecasting Method

Crop pests and diseases cause great harm to food safety and quality, thus, to predict the occurrence of crop pests and diseases is of great significance. It can not only effectively prevent and control the occurrence of crop pests and diseases, but also raise the management level of agricultural production and the development of precision agriculture, reduce losses of pests and diseases and improve the yield and quality of agriculture. There have been a large number of previous studies; and this section mainly introduces several common forecasting methods. In Section 2.4.1, we mainly introduce the mechanism of Bayesian network. In Section 2.4.2, we mainly introduce the mechanism of logistic regression analysis. In Section 2.4.3, we mainly introduce the mechanism of Markov chain.

2.4.1

Bayesian Network

Bayesian network (BN) is a probabilistic graph theory model based on probabilistic statistics theory. It has the characteristics of rigorous reasoning process, clear semantic expression and data learning ability. It can obtain the probability information of other variables through the information of some variables. It is an effective tool for uncertainty reasoning and data analysis. Since the 1980s, it has been widely

Mechanism and Method

59

used in expert systems, data mining, pattern recognition, image processing, artificial intelligence, and many other fields. BN is formed by plotting the random variables in a directed graph (figure 2.17). It is mainly used to describe the conditional relationships among random variables, with circles indicating random variables and arrows indicating conditional relationships. In addition, for any random variable, its joint probability can be obtained by multiplying local conditional probability. Therefore, this method can be used to forecast the types of crop pests and diseases. The input variables are characteristics sensitive to crop pests and diseases introduced in Section 2.2. There is a limited set of discrete variables X ¼ fX 1 ; X 2 ; . . .; X n ; C g, n ¼ 1; 2; . . .; N ; N [ 1, X i is random variable and C is class variable. Each variable can take a limited number of values, and the set of values is indicated by ValðX i Þ. x i is used to denote the value of variable X i , and c is used to denote the value of class variable C . For the set X, the BN can be expressed as B ¼ hG; Hi, in which G is a directed acyclic graph; each vertex in the graph corresponds to the variable X i , and the directed edges between the vertices represent the dependence between the corresponding variables. The variable pointed by the directed arrow is the child node, if not, the parent node. The network structure means that given the parent node of a variable X i . X i is independent of its non-child nodes. H represents the parameters of the BN, i.e., the conditional probability and a priori probability of each vertex in the network. Given an example D ¼ fx 1 ; x 2 ; . . .; x n g and class label c, the BN structure is shown in figure 2.17. According to the Bayesian formula, the posterior probability of the class label c is as follows. pðcjD Þ ¼

pðDjcÞpðcÞ pðD Þ

ð2:55Þ

Therefore, giving the set of variables D, the posterior probability of the most likely class label c is:

FIG. 2.17 – BN structure.

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

60

w ðx Þ ¼ argmax c2C

pðDjcÞpðcÞ pðD Þ

ð2:56Þ

For a given set, p(D) is a constant that does not depend on c, so formula (2.56) can be written as: w ðx Þ ¼ argmax pðDjcÞpðcÞ

ð2:57Þ

c2C

The applied multiplication rules are: pðDjcÞ ¼ pðx1 jcÞpðx2 jx1 ; cÞ    pðxn jx1 ; x2    xn1 ; cÞ n Y pðxi jx1 ; x2    xi1 ; cÞ ¼

ð2:58Þ

i¼1

For each x i , if pðx i Þ 2 fx 1 ; . . .x i1 g exists, where pðx i Þ represents the set of parent nodes x i , making pðx i Þ, x i and fx 1 ; . . .x i1 g independent of the other variable conditions, as: pðxi jx1 ; . . .; xi1 Þ ¼ pðxi jpðxi ÞÞ

ð2:59Þ

Formula (2.59) can be written as: w ðx Þ ¼ argmax c2C

n Y

pðxi jpðxi Þ; cÞpðcÞ

ð2:60Þ

i¼1

In the BN, given examples fx 1 ; x 2 ; . . .; x n g and class labels c, the formula for BN classification can be obtained from formula (2.60): w ðx Þ ¼ argmax pðcÞ c2C

n Y

pðxi jpðxi Þ; cÞ

ð2:61Þ

i¼1

where C is the categorical variable and A1–A4 are attributed nodes.

2.4.2

Logistic Regression Analysis

Logistic regression analysis is a generalized linear regression analysis model, which is often used in data mining, automatic pest/disease diagnosis, economic forecasting and other fields. For example, logistic regression analysis could be used to discuss the risk factors of pests and diseases and predict the probability of pests and diseases occurrence based on the risk factors. Logistic regression is a machine learning method used to solve classification problems. This model solves the classification problem by assuming that the dependent variable Y obeys Bernoulli distribution and the independent variable X has a linear relationship with the dependent variable Y, and using Sigmoid function to process nonlinear data. Therefore, logistic regression analysis can be used for forecasting severity. The input of this model is the spectral characteristics which are sensitive to crop pests and diseases introduced in

Mechanism and Method

61

Section 2.2. The establishment of logistic regression model requires the following process. First, establish the loss function, then solve the optimal model parameters by the optimized method, and finally test and verify the logistic regression model. Datasets contain t1, t2,…, tn, these data give a weight w, The formula of weight of data as: w T t ¼ w1 t1 þ    þ wn tn ¼

n X

wi ti

ð2:62Þ

i¼1

Defining f ðt Þ ¼ w T  t, g ði Þ ¼ Sigmoidði Þ ¼ 1 þ1ei , the function image of Sigmoid function is shown in figure 2.18, the range of value is (0,1). The function g(i) is used to map f(t). When g(f(t)) ≥ 0.5, it means that t is divided into positive class, and when g(f(t)) < 0.5, it means that t is divided into inverse class. The value of g(f(t)) between (0,1) can be seen as the probability. At this time, the data can be classified into two categories, and the classification function is denoted as PðyjxÞ. h(x) is defined as the probability that the predicted sample data are positive class, then the probability of the negative class is 1−h(x). The concession ratio is defined as the ratio between the positive class and the negative class, and the logarithm is used to solve equation (2.63). h ðx Þ 1  h ðx Þ h ðx Þ e f ðx Þ ¼ 1  h ðx Þ 1 h ðx Þ ¼ 1 þ ef ðx Þ

f ðx Þ ¼ log

ð2:63Þ

Weights of concentration data are substituted into function f(x) for nonlinear summation to obtain h(x), as shown in figure 2.19, mapping f(x) with g(i) function to obtain h(x). Concentration data are substituted, then use the data to perform weighted summation and nonlinear output to obtain h(x), as is shown in figure 2.20. The function h(x) has the characteristic of Sigmoid function as the prediction function of the model.

FIG. 2.18 – Sigmoid function.

62

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

FIG. 2.19 – Weighted sum of linear models.

FIG. 2.20 – Weighted and nonlinear output.

2.4.3

Markov Chain

Markov Chain (MC) is a stochastic process in probability theory and mathematical statistics which has Markov property and exists in the discrete index set and state space. MC is applicable to continuous exponential sets. It is known as Markov process, but it is sometimes regarded as a subset of Markov chains, namely continuum-timemc (CTMC), which corresponds to discrete-timemc (DTMC), so MC is a relatively broad concept. For a system, in the transition process of one state to another state, there is a transition probability, and this transition probability can be calculated according to its previous state. It has nothing to do with the original state of the system or prior transition process. Therefore, MC can be used for forecasting pest/disease severity. The input of this model is the spectral characteristics sensitive to crop pests and diseases introduced in Section 2.2. MC is a set of discrete random variables with Markov properties. Specifically, in the probability space (Ω, F, P), a set of random variables X = {Xn: n > 0} of a one-dimensional countable set as an index set. If all the values of the random variable are in the countable set, X = Si, Si ∊ S, and the conditional probability of the random variable satisfies the following relation. pðXt þ 1 jX t ; :::; X1 Þ ¼ pðXt þ 1 jXt Þ

ð2:64Þ

X is called MC, and countable sets S 2 Z are called state space. The value of MC in a state space is called state. The Markov chain defined here is DTMC. Although it is called CTMC, it is essentially a Markov process. The above formula defines both

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MC and Markov properties, which is also known as memorylessness, meaning that the random variable t + 1 becomes independent of the rest of the random variables given a random variable at t. MC has strong Markov property, that is, for any stopping time, the state of MC before and after the stopping time is independent of each other. The evolution of the MC can be expressed as a transition graph in accordance with the structure of a graph, and each edge of the graph is assigned a transition probability. The concepts of “reachable” and “connected” can be introduced through the transfer diagram. If there is state Si in the Markov chain, Sj ¼ pi;k1 pk1;j . . .pkn;j [ 0, that is, if all transition probabilities on the sampling path are not 0, then state Si could reach state Sj, which is represented by Si → Sj in the transition diagram. If Si and Sj are mutually accessible states, Si ↔ Sj is used to represent it in the transition diagram. By definition, accessibility and connectivity can be indirect, they do not have to be completed in a single time step. Connectivity is a set of equivalence classes, so equivalence classes can be constructed, and equivalence classes that contain as many states as possible in MC are called communicating classes. Giving a subset of the state space, if MC cannot leave after entering the subset, the subset is closed, namely closed set, and all states outside closed set are not in reachable states. If there is only one state in closed set, then the state is an absorbed state. In the transition diagram, it is a self-loop with probability 1. A closed set may include one or more connected classes (figure 2.21). This chapter introduces mechanisms and methods of crop pest and disease remote sensing monitoring and forecasting. First, we introduce the spectral response characteristics of crop pests and diseases through the spectral difference between healthy crops and infested crops. Then we introduce some feature extraction methods used in remote sensing data processing, including spectral sensitivity analysis methods and the forms of spectral features. Finally, we introduce the methods that can be used for monitoring and forecasting model construction, and

FIG. 2.21 – Markov chain transition graph.

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explain the principles of these methods. This chapter provides theoretical basis and support for the remote sensing monitoring and forecasting of crop pests and diseases.

References Adams M. L., Philpot W. D., Norvell W. A. (1999) Yellowness application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. Int. J. Rem. Sens. 20, 3663. Addison P. S. (2005) Wavelet transforms and the ECG: a review. Physiol. Meas. 26, R155. Bruce L. M., Li J., Huang Y. (2002) Automated detection of subpixel hyperspectral targets with adaptive multichannel discrete wavelet transform. IEEE Trans. Geosci. Remote Sens. 40, 977. Curran P. J. (1980) Relative reflectance data from preprocessed multispectral photography. Int. J. Remote Sens. 1, 77. Cheng T., Rivard B., Sánchez-Azofeifa G. A., et al. (2010) Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sens. Environ. 114, 899. Guo A., Huang W., Ye H., et al. (2020) Identification of wheat yellow rust using spectral and texture features of hyperspectral images. Remote Sens. 12, 1419. Horler D. N., Dockray M., Barber J. (1983) The red edge of plant leaf reflectance. Int. J. Remote Sens. 4, 273. Huang W., Lu J., Ye H., et al. (2018) Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis. Int. J. Agric. Biol. Eng. 11, 145. Huete A. R. (1988) A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25, 295. Jordan C. F. (1969) Derivation of leaf area index from quality of light on the forest floor. Ecology. 50, 663. Kaufman Y. J., Tanre D. (1996) Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: from AVHRR to EOS-MODIS. Remote Sens. Environ. 55, 65. Lillesand T. M., Kiefer R. W. (1994) Remote Sensing and Image Interpretation. John Willey & Sons. Inc, United States of America. Liu Y., Chen Y. R., Wang C. Y., et al. (2006) Development of hyperspectral imaging technique for the detection of chilling injury in cucumbers; spectral and image analysis. Appl. Eng. Agric. 22, 101. Lymburner L., Beggs P. J., Jacobson C. R. (2000) Estimation of canopy-average surface-specific leaf area using Landsat TM data. Photogram. Eng. Remote Sens. 66, 183. Miller J. R., Hare E. W., Wu J. (1990) Quantitative characterization of vegetation red edge reflectance 1. An inverted-Gaussian reflectance model. Int. J. Remote Sens. 11, 1755. Parker S. P., Shaw M. W., Royle D. J. (1995) The reliability of visual estimates of disease severity on cereal leaves. Plant Pathol. 44, 856. Penuelas J., Filella I., Biel C., et al. (1993) The reflectance at the 950-970 nm region as an indicator of plant water status. Int. J. Remote Sens. 14, 1887. Pinty B., Verstraete M. M. (1992) GEMI: a non-linear index to monitor global vegetation from satellites. Vegetation. 101, 15. Qi J., Chehbouni A., Huete A. R., et al. (1994) A modified soil adjusted vegetation index. Remote Sens. Environ. 48, 119. Riedell W. E., Blackmer T. M. (1999) Leaf reflectance spectra of cereal aphid-damaged wheat. J. Crop Sci. 39, 1835. Shahin M. A., Symons S. J. (2011) Detection of fusarium damaged kernels in Canada western red spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis. Comput. Electron. Agric. 75, 107.

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Shi Y., Huang W., González-Moreno P., et al. (2018) Wavelet-based rust spectral feature set (WRSFs): a novel spectral feature set based on continuous wavelet transformation for tracking progressive host–pathogen interaction of yellow rust on wheat. Remote Sens. 10, 525. Tucker C. J. (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127. Verstraete M. M., Pinty B. (1996) Designing optimal spectral indexes for remote sensing applications. IEEE Trans. Geosci. Remote Sens. 34, 1254. Zhao C., Huang M., Huang W., et al. (2004) Analysis of winter wheat stripe rust characteristic spectrum and establishing of inversion models. Geoscience and Remote Sensing Symposium, 2004. IGARSS’04. Proceedings. 2004 IEEE International. IEEE, 2004, 6, pp. 4318. Zhang J., Pu R., Loraammm R. W., et al. (2014) Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat. Comput. Electron. Agric. 100, 79.

Part Two

Non-Imaging Hyperspectral Remote Sensing Monitoring for Crop Pest and Disease

Crop pests and diseases are biological disasters in agricultural production. In recent years, global warming provides suitable conditions for the occurrence, prevalence and spread of pests and diseases, making pests and diseases control becomes more difficult. Traditional monitoring methods of crop pests and diseases cannot meet urgent needs of precision agricultural. Remote sensing technology is widely used in monitoring, analysis and evaluation of crop pests and diseases due to its advantages of quick and continuous regional observation, lossless monitoring, and rich information. Although non-imaging hyperspectral technology cannot observe image, it often has the characteristics of high spectral resolution, large number of bands, and rich information. It can detect subtle features of ground objects, which is incomparable with imaging multispectral technology. Non-imaging hyperspectral data involved in this part refers to ASD FieldSpec Pro FR (350–2500 nm) ground non-imaging hyperspectral data. Since non-imaging spectral data are collected near ground, they are less affected by atmosphere and external environment, which is more accurate to measure the true spectrum of ground objects. Therefore, non-imaging spectral technology can be used to study spectral response mechanism of pests and diseases, which is the theoretical basis for aviation and aerospace remote sensing monitoring of pests and diseases. For non-imaging field, this part mainly introduces monitoring on the leaf and canopy scales. At the leaf scale, spectral characteristics are not affected by soil, atmosphere, vegetation geometry, and coverage. Spectra are only affected by pests and diseases, which helps to accurately understand remote sensing monitoring mechanism of pests and diseases. At the canopy scale, spectral characteristics are close to that of satellite remote sensing, but it overcomes resolution of satellite remote sensing data based on time, space and spectrum. Therefore, studying canopy spectrum at different damage levels, clarifying their spectral response characteristics

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and constructing remote sensing monitoring models can provide the theoretical basis for satellite remote sensing monitoring of crop pests and diseases. This part takes main pests and diseases (including wheat yellow rust, wheat powdery mildew, wheat aphid, rice leaf roller) as examples to introduce the following contents, including measuring leaf and canopy spectra that are infested by pests and diseases, studying physiological structure changes and spectral response mechanisms of crops infested by pests and diseases, constructing sensitive spectral index and non-imaging spectral monitoring models of pests and diseases, and providing theoretical basis for further research on pests and diseases monitoring with imaging remote sensing. The research in this part is based on several experiments of pests and diseases as listed below. ✧ Experiment 1: Wheat yellow rust experiment Wheat yellow rust experiment was an artificial inoculation experiment in 2011 at Beijing Xiao tangshan National Precision Agriculture Demonstration Base. The wheat variety was “Beijing 9428” with normal water and fertilizer management. After inoculation, 220 wheat leaves with different disease damaged levels were collected in two growth periods, i.e. jointing period (April 29, period І) and filling period (May 21, period II). Disease incidence of leaf infested by yellow rust was calculated according to the size of rust spots. To compare leaf spectrum of different levels, disease incidence is divided into 9 categories (0%, 1%, 10%, 20%, 30%, 45%, 60%, 80%, and 100%), where 0% is healthy and 1% is disease level 1, 10% is disease level2......100% is disease level 8. In addition, representative wheat plants were selected, and relative content of chlorophyll (SPAD), chlorophyll Chl, flavonoid (Fla) and Nitrogen balance index (NBI) were measured by SPAD-502 and Dualex. ✧ Experiment 2: Wheat powdery mildew experiment Wheat powdery mildew is a susceptible disease of wheat. The obvious white spots on leaves make it more suitable for remote sensing monitoring. The experiment was conducted in experimental field of Beijing Academy of Agriculture and Forestry Sciences, located at 116°16′ E and 39°56′ N. The tested variety “Jingdong 8” was widely planted in Beijing and Hebei Province, and was moderately susceptible to wheat powdery mildew. Between May and June 2010, about half of the wheat in test field was spontaneously infested by powdery mildew. Symptoms were not obvious in the early stage, and become obvious from the filling stage. And early filling stage is an important time slot for field control. Therefore, experiment at leaf scale was conducted in May 23, 2010 (early filling stage). Scissors were used to cut 114 leaves with petioles, including 34 healthy samples and 80 infested samples. Samples were immediately put in ice bag to avoid the loss of water and keep normal physiological state after cutting. Then, they were transferred to lab immediately to measure spectrum and biochemical parameters and disease severity. Biochemical parameters mainly include three pigments, i.e. chlorophyll a, chlorophyll b and carotenoid. On May 29, 36 fields with different

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degrees of disease were selected, canopy spectrum was measured, and disease severity of each field was recorded. This experimental data were mainly used to explore spectral response characteristics of wheat powdery mildew leaves and then to estimate disease levels. ✧ Experiment 3: Wheat aphid experiment at leaf scale The main organs affected by wheat aphids are leaves. Leaves experiment was conducted in Beijing Academy of Agriculture and Forestry Sciences on May 13, 2010. The variety in field was Zhongmai 16, which was planted from September 22 to 26 in 2009 and expected to harvest around June 10, 2010. Observations include NBI, SPAD, leaf spectrum and quantity of aphids. A total of 60 leaf samples were obtained in experiment. According to occurrence of aphids in field, the quantity of aphids was collected (number of aphids on each leaf sample), ranging from 0 to 120. And in order to ensure the representativeness of test samples, the number of aphids was investigated at an interval of 5 and the number of samples in each interval is equal. In terms of data collection, we cut leaves with scissors, counted and recorded aphids quantity, swept aphids with fine brush, and immediately measured NBI, SPAD and leaf spectrum. ✧ Experiment 4: Wheat aphid experiment at canopy scale The wheat aphid canopy spectrum experiment was carried out in spring of 2010 in Beijing Xiaotangshan National Precision Agriculture Demonstration Base, Changping District, Beijing (40º 10′ N, 116º 26′ E). The length and width of the experimental field is 250 m and 50 m. The wheat variety was Zhongmai 175, which was sown on October 4, 2009. On May 28, 2010 (best control period for aphids), southern 150 m × 50 m test field was divided into five plots with each as 150 m × 10 m, and control strategies were carried out to form different levels of aphids. The experiment was conducted on June 7, 2010, which covered not only the during mid-late filling stage, but also flourishing period of wheat. Wheat canopy spectrum and aphid severity were observed. ✧ Experiment 5: Rice leaf roller experiment Rice leaf roller is the main pest in China. With increasing in severity of rice leaf roller, rice leaf rolling phenomenon has become more obvious, and bidirectional reflection characteristics of rice changed. Therefore, it is possible to use reflectance to monitor rice leaf roller. There are three observation places for leaf spectrum of rice leaf roller. The first place is located in Yundong Village, Guali Town, Xiaoshan District, Hangzhou, Zhejiang Province (30°12′ N, 120°28′ E), the second place is located in Zhangpanqiao Village in Shushan Street, Xiaoshan District, Hangzhou, Zhejiang Province (30° 06′ N, 120° 15′ E), and the third place is located in Modern Agriculture Research and Demonstration Center of Zhejiang University (120° 10′ E, 30° 14′ N). On July 27 and August 14, 2007, rice leaf roller surveys were carried out at observation places, and 19 and 35 samples at leaf scale were collected for indoor spectrum acquisition, respectively.

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The experiment of measuring canopy spectrum of rice leaf roller was carried out at the experimental base of South China Agricultural University in Guangdong Province from October 7 to 10, 2008. Rice had entered heading stage. 50 Rice leaf roller affected plots and 30 healthy plots with the size of 5 m × 5 m were observed. The canopy spectrum data were collected from affected plots and healthy plots, and field investigations were conducted. Rice spectrum were collected using ASD. The experiment selected sunny and cloudless weather, and canopy spectrum in field was collected on October 8 and 9, 2008 during 10 to 12 o'clock local time. For each infested plot, two clusters were randomly selected to investigate infested situation, and the total number of leaves, infested leaves and white spots on leaves were recorded. The field measurement parameters and measurement specifications synchronized with the above experiments are listed as follows. ✧ Leaf spectral data Method 1: A Li-1800 external integrating sphere (Li-Cor Inc., Lincoln, Nebraska, USA) coupled with a FieldSpec® UV/VNIR spectrometer (ASD Inc., Boulder, Colorado, USA) was used to observe the leaf spectral data. The wavelength range was 350–1050 nm, and the spectral resolution was 3 nm. To avoid the band with low signal-to-noise ratio, the spectrum in the range of 450–950 nm was intercepted for follow-up research. According to the distribution of infested spots on the leaf, each leaf was measured 10–15 different positions (avoid the veins) and then taken the average to represent the leaf spectrum. The spectrum of the reference plate was recorded every 10 leaves measured, and the leaf reflectivity was calculated by the radiance of the blade and the radiance of the reference plate. Method 2: A blade clamp coupled with an ASD FieldSpec Pro FR (350–2500 nm) spectrometer was used for the measurement. Its spectral resolution was 3 nm in the range of 350–1000 nm, and 10 nm in the range of 1000–2500 nm. The experiment chose to avoid the blade base and tip for spectrum measurement. Considering the size of the leaf and the measuring area of the leaf clip, 5 different parts of each leaf were selected (avoid the veins). After measuring 5 times of spectrum for each part, the spectrum representing the leaves were averaged. ✧ Canopy spectral data The ASD FieldSpec Pro FR (350–2500 nm) spectrometer was used to measure the canopy spectrum. Its spectral resolution is 3 nm in the 350–1000 nm range, and 10 nm in the 1000–2500 nm range. During the observations, the spectrometer probe is vertically downward, the height is always 1.3 m above the ground, and the field of view of the probe is 25 °. Each quadrat was measured 20 times, before and after each measurement, a standard reference plate was used to calibrate the reflectance, and the spectral curve was resampled to 1 nm. All spectroscopy measurements were performed from 10 to 14 o'clock (local time) under clear and cloudless weather conditions.

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✧ Pigment content Before pigment content determination, the leaf samples were soaked in 80% acetone solution at 25 °C for 24 h in a dark environment. The content of chlorophyll a, chlorophyll b and carotenoids were calculated with reference to Lichtenthaler and other methods. The specific principles and calculation methods were listed as follows. In the visible wavelength range, the absorption spectrum of chlorophyll a and chlorophyll b are different. In the acetone solution, the maximum absorption peaks of chlorophyll a and b in the visible range are located at 663 and 645 nm, respectively. The optical density at each specific peak wavelength can be measured, and the pigment concentration can be calculated based on the extinction coefficient of the pigment molecule at that wavelength. CA ¼ 9:784  D663  0:990  D645

ðII:1Þ

CB ¼ 21:462  D645  4:650  D663

ðII:2Þ

CA þ B ¼ CA þ CB ¼ 5:134  D663 þ 20:436  D645

ðII:3Þ

CC ¼ 4:695  D440  0:268  CA þ B

ðII:4Þ

where CA and CB are the concentrations of chlorophyll a and b, respectively, CA+B is the total concentration of chlorophyll a and b, and CC is the carotenoid concentration in mgL−1. The pigment content per unit weight or unit area of the measured material can be calculated by the following formula:
 C V ðII:5Þ Pigment content mgg1 or mgdm2 ¼ A  10000 where, C is the pigment concentration (mgL−1); V is the volume of the extract (ml); A is the leaf fresh weight (g) or area (dm2). The NBI is determined by the Dualex 4 plant NBI measuring instrument successfully developed by French Force-a company using plant fluorescence technology. NBI is an important indicator of plant nitrogen assessment, and the correlation with plant chlorophyll content can reach more than 90%. Research has shown that the correlation between Chl and chlorophyll concentration is extremely significant (R2 = 0.95). The relative content of chlorophyll is measured by SPAD-502 chlorophyll measuring instrument. The conventional method of using SPAD to determine the chlorophyll content of leaves is as follows. Each single leaf is divided into three parts, i.e., the tip, the middle and the base. Each part is measured three times, with a total of nine times. The average value of these nine measurements was used as the final SPAD chlorophyll content value of the leaf. ✧ Wheat pests and diseases The severity of crop diseases is often described by DI. The severity of the disease at the leaf scale is mainly reflected in the degree of infection of the normal tissues of

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the leaves by the pathogen, that is, the ratio of the area of the infested spot to the leaf area, so the coverage ratio of the infested spot on the leaf is judged as a measurement of the severity of the leaf diseases. Before completing the leaf spectrum measurement and measuring the biochemical parameters, a photo of each leaf is firstly taken, and then the reader will judge the disease degree based on the photo to shorten the leaf placement time. To reduce the error caused by human judgment, the lesion ratio is usually divided into several intervals for judgment. DI at canopy scale refers to the National Rules for the Investigation and Forecasting of Crop Diseases (GB/T 15795-1995 for wheat yellow rust, NY/T 613-2002 for wheat powdery mildew, GBT15796-2011 for wheat Fusarium head blight), and each leaf of the selected plant is grouped into one of different disease severities. For yellow rust, a total of 9 severity levels are included, i.e. 0%, 1%, 10%, 20%, 30%, 45%, 60%, 80%, and 100%. For wheat powdery mildew, a total of 10 severity levels are included, i.e. 0 (0%), 1 (1%–10%), 2 (11%–20%), 3 (21%–30%), 4 (31%–40%), 5 (41%–50%), 6 (51%–60%), 7 (61%–70%), 8 (71%–80%), and 9 (81%–100%). Then, by counting the incidence of all wheat leaves within 1 m2, the DI was calculated by using the formula as follows. P l ði  li Þ ;F ¼ ðII:6Þ DIð%Þ ¼ F  D  100; D ¼ L L where F is the incidence of quadrats and represents the percentage of the number of infested leaves in the total number of investigated leaves, D is the average severity of the quadrats, i is the severity level, li is the total number of leaves associated with each severity level, L is the total number of leaves investigated, and l is the number of infested leaves. The severity of wheat Fusarium head blight is determined by referring to the incidence area ratio and the main indicator of the final infested ear rate in the local area. The severity of occurrence is divided into 6 levels, i.e. 0 (0%), 1 (0.1%–10%), 2 (10.1%–20%), 3 (20.1%–30%), 4 (30.1%–40%), and 5 (>40%). The degree of aphid damage is a description of the severity of aphid damage. Damage at leaf scale is often measured by the amount of aphid infestation. For damage at canopy scale, a method of investigating or converting the number of aphids on 100 wheat plants is used. The aphid densities were then estimated using the formula, aphid density = total aphid amount / plants number within 1 m2. Then, according to the Rules for the Investigation and Forecast of Wheat Aphides (NY/T 612-2002), six aphid damage severities were defined: 0 (the amount of aphids in 100 wheat plants is 0), 1 (1–500), 2 (501–1500), 3 (1501–2500), 4 (2501–3500), and 5 (> 3500). ✧ Rice leaf roller For the rice leaf roller severity levels at canopy scale, it can refer to the National Rules for the Investigation and Forecasting of Crop Diseases (GB/T 15793-2011). We use ASD spectrometer to measure spectral reflectance of rice canopy in selected 1 m2 to investigate the severity. And, six levels are used as 0 (the rolling leaf rate is 0%), 1 (0.1%–5%), 2 (5.1%–20%), 3 (20.1%–35%), 4 (35.1%–50%), 5 (>50%).

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For each victimized community, two clusters were randomly selected for manual enumeration, which included investigating the damage of the rice leaf roller, and recording the total number of leaves LT, damaged leaves La and white spots Lb, so as to count the damage in each district. The total damage rate CT of rice leaf roller can be obtained by adding the rolling leaf rate Ca and white spot rate Cb. Ca and Cb of the affected rice are calculated by using formulas as follows. Ca ¼ La 

100 LT

ðII:7Þ

Cb ¼ Lb 

100 LT

ðII:8Þ

Chapter 3 Crop Disease Monitoring Spectral response mechanism and extracted spectral features are the basis for remote sensing monitoring of crop diseases. In this chapter, wheat yellow rust, Fusarium head blight and powdery mildew are monitored at leaf/ear scale and canopy scale by using remote sensing technology. Hyperspectral data of wheat ears, leaves and canopies are used to extract the spectral features of diseases. Studies on crop disease monitoring at the leaf and ear scales focus on finding the spectral differences between healthy and infested samples, and disease spectral features are extracted and used as input for disease monitoring models. The spectral information of wheat canopy is a combination of several factors such as soil, canopy structure, leaves, wheat ears, etc. Spectral features at leaf/ear scale are not fully applicable at canopy scale, so the spectral features need to be re-extracted for modeling at canopy scale. The main idea of disease monitoring at canopy scale is to re-extract and re-establish sensitive spectral features, and then to establish a canopy-scale disease monitoring model. The research results in this chapter are the basis for subsequent field-scale and at regional-scale disease remote sensing monitoring.

3.1 3.1.1

Wheat Yellow Rust Monitoring Monitoring at Leaf Scale

Wheat yellow rust caused by Puccinia striiformis seriously threatens the yield and quality of wheat (Zheng et al., 2018; Moshou et al., 2004). This disease occurs in more than 60 countries and regions worldwide, and it is also one of the most important wheat diseases in China (Wan et al., 2007). The disease is a typical long-distance airborne wheat disease in China. It normally occurs in large areas with quickly spreading (Zheng et al., 2018). It mainly damages leaves and sheaths, robs nutrients and water, and destroys chlorophyll (Shi et al., 2017). The disease can also cause a decline in the thousand-grain weight, generally reducing the yield by 5%–10%, and in the worst case, almost no grains (Shi et al., 2018). The promotion of DOI: 10.1051/978-2-7598-2659-9.c003 © Science Press, EDP Sciences, 2022

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disease-resistant varieties in China is limited. Once the climatic conditions are suitable and there is an appropriate source of bacteria, wheat yellow rust often spreads across the country and causes serious losses (Su et al., 2019). Figure 3.1 shows the pictures of healthy wheat leaves and the ones infested by yellow rust. The main spectral differences between healthy wheat leaves and yellow rust infested wheat leaves are as follows. The reflectance of healthy wheat leaves is higher, which is near 550 nm, followed by higher reflectance of infested wheat leaves near 680 nm. Finally at 770–1000 nm, the reflectance of healthy wheat leaves is higher. It can be seen from figures 3.2 and 3.3 that the wheat has no obvious symptoms within two weeks after inoculation with pathogenic bacteria. 15–20 days after inoculation, the yellow rust appears in the wheat leaves. 20–30 days after inoculation, a layer of yellow rust spores appears on the surface of the leaves. 30 days after inoculation, yellow rust spores appears on the front and back of the leaves. The leaf physiological differences (Bürling et al., 2013), i.e., chlorophyll index (CHL), NBI, anthocyanin index (ANTH), and percentile dry matter (PDM), in the development of yellow rust are shown in figure 3.4. For CHL, a significant decrease has been observed from the 21st day after inoculation. After the 34th day, CHL reached the lowest level, which is 35.7% lower than the healthy samples on average. Similarly, in the early stage of infection (7–21 days), the NBI changes between healthy leaves and infested leaves were synchronized, and from the 28th day, the diversity became obvious. On the 34th day, the largest averaged difference 32.3% was achieved. For PDM, although the growth trends of healthy and disease samples were similar, the PDM growth of the infested group was significantly lower than that

FIG. 3.1 – Healthy wheat leaves (left) and yellow rust infested leaves (right).

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FIG. 3.2 – Spectral curves of healthy wheat leaves (a) and infested wheat leaves (b) at different times after inoculation.

FIG. 3.3 – Photos of infested wheat leaves at different times after inoculation with pathogenic bacteria, and spectral curves of healthy and infested wheat leaves.

FIG. 3.4 – Values of chlorophyll index, NBI, anthocyanin index and percentile dry matter (PDM) of healthy leaves and infested leaves at different times after inoculation with pathogenic bacteria.

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of the healthy group. After the 31st day, it was at the level of moderate to severe disease. The dry matter accumulation of rust-infested leaves was about 9.8% lower than that of healthy leaves. Finally, the most significant physiological difference was found in ANTH. Starting from the second week (day 14) after inoculation, the anthocyanin content of the rust infection group increased significantly, and reached a peak on the 34th day, which was higher than that of healthy flowers. The content of penicillin was nearly 8 times higher. Based on continuous wavelet transformation (CWT) (Shi et al. 2017; Zhang et al., 2014; Cheng et al., 2010), the relevant scale map is shown in figure 3.5. It is worth noting that although there are noise interference and design spectral resolution between different sensors (1 nm for ASD spectrometer and 1.4 nm for Headwall spectrometer), the position and proportion of the sensitive area are similar (Orange part in figure 3.5. We summarize the intersection of wavelet features selected from the top 5% of the relevant scale maps in the ASD and Headwall data sets, and extracted the features in 470–480 nm, 520–600 nm, and 630–760 nm. Five characteristic areas which were sensitive to the formation of rust were identified (scales 2– 5). Here, wavelet-based rust spectral feature set (WRSFs) are represented as a set of individual wavelet features, i.e. WF01, WF02, WF03, WF04, and WF05. Table 3.1 lists the details of the position and proportion of each functional component. Figure 3.6 shows the correlation analysis results of each wavelet feature with the chlorophyll index, NBI, anthocyanin index, and percentile dry matter. It can be found that there was a significant linear correlation between WF01 and PDM (R2 = 0.82, p < 0.05). The biophysical properties of WF02 and WF03 were similar, partly because their center wavelengths 545 nm and 571 nm, and the corresponding scales 22 were similar. The R2 values of CHL are 0.77 and 0.79 respectively, and the R2 values of ANTH were 0.68 and 0.74 respectively. For WF04, a linear relationship that was highly correlated with changes in NBI and PDM has been determined, with R2 values at 0.71 and 0.72, respectively. Finally, the correlation between NBI and WF05 was considered statistically significant (R2 = 0.76).

FIG. 3.5 – Correlation scalogram for WRSFs extraction. The horizontal axis is the wavelength and the vertical axis is the scale. The 5% part with the highest correlation is highlighted in orange.

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TAB. 3.1 – Specific information of the five wavelet features. Wavelet features WF01 WF02 WF03 WF04 WF05

Wavelength/nm 486 545 571 685 746

Scale 25 22 22 24 24

R2 0.93 0.94 0.90 0.92 0.94

FIG. 3.6 – Correlation analysis results of each wavelet feature with the chlorophyll index, NBI, anthocyanin index, and percentile dry matter. The extracted WRSFs and VIs were used as the input features of the linear discriminant analysis (LDA) and SVM models (Bandos et al., 2009; Hearst et al., 1998), and the constructed models were compared and analyzed based on the spectral differences between healthy and infested leaves. All the spectral data obtained from the experiment were used as verification dataset. The results showed that the LDA and SVM classifiers developed using WRSFs performed better than the classifiers using VIs, and the overall accuracy increased by 10.4% and 7.8%, respectively. Figure 3.7 further illustrates the comparison of the progressive rust classification results between the models based on WRSFs and VIs. These results showed that the classification of WRSF-based models was always higher than that of VI-based models. In addition, compared with the linear discriminant analysis (LDA) classification framework, the classification accuracy of the SVM model based on kernel techniques was always higher. Specifically, for early observation (7–21 days), the

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FIG. 3.7 – Comparison of classification results between the models based on WRSFs and VIs.

infested leaf area was almost 4% to 19%, and the average classification accuracy of LDA and SVM based on WRSF was about 67.6% and 72.6%, respectively, which were 14.2% and 12.9% larger than the VI-based model. As the rust disease progresses (day 21–day 41), the visible symptoms of infested leaves provide more information on the reliability of the classification results. The average accuracy of LDA and SVM classifiers based on WRSFs has rapidly improved, and the coverage of rust pathogens has reached 20% to 40%. When it exceeded 40% (after the 31st day), the classification accuracies of LDA and SVM classifiers based on WRSFs reach the highest level at 83.3% and 89.3%, respectively. Table 3.2 lists the details of the comparison of leaf classification results between the models based on WRSFs and VIs. These results showed that the overall classification accuracy of models based on WRSFs was always higher than that of models based on VIs. In addition, compared with the LDA, the classification accuracy of the SVM was always higher. Specifically, within three weeks after leaves were inoculated with pathogenic bacteria, the area of rust fungus in infested leaves was 4%–19%. The overall classification accuracies of LDA and SVM based on WRSFs were 67.6% and 72.6%, respectively, which were 14.2% and 12.9% higher than the model based on VIs. During 21st to 41st days after inoculation, the area of rust fungus was further expanded, and the coverage rate reached 20%–40%. The overall classification accuracy of LDA and SVM classifiers based on WRSFs increased rapidly. Over 31 days after inoculation, the coverage rate of rust fungus exceeded 40%, and the overall classification accuracies of LDA and SVM classifiers based on WRSFs reached 83.3% and 89.3%, respectively. Since SVM is more efficient in classification, the study also compared the effects of SVM classifiers based on WRSFs and VIs in monitoring wheat yellow rust with hyperspectral images. The monitoring results are shown in figure 3.8, and the monitoring accuracy is shown in table 3.3. The results showed that from the 7th day to the 21st day after inoculation, the accuracy of SVM based on WRSFs in identifying yellow rust was 84.2%–95.2%, and the accuracy of SVM based on VIs was 79.8%–84.8%. From the 21st day after inoculation, the classification accuracy of the

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TAB. 3.2 – Classification results of the LDA and SVM classifiers developed using WRSFs and VIs. Method

Input VIs

LDA WRSFs

VIs SVM WRSFs

Yellow rust Healthy P/% Yellow rust Healthy P/% Yellow rust Healthy P/% Yellow rust Healthy P/%

Yellow rust 106 41 72.10 122 25 83.00 116 31 78.90 126 21 85.70

Healthy 28 66 70.20 17 67 79.80 18 66 78.60 10 74 88.10

U/% 79.10 61.70

OAA/%

Kappa

71.40

0.79

87.80 72.80

81.80

0.84

86.60 68.00

78.80

0.81

92.60 77.90

86.60

0.86

Note: P = producer’s accuracy, U = user’s accuracy, OAA = overall accuracy.

FIG. 3.8 – Monitoring results of wheat yellow rust on hyperspectral images based on SVM classifier developed using WRSFs (A) and VIs (B). TAB. 3.3 – Evaluation results of SVM classifier based on WRSFs and VIs. Classification accuracy/% Feature WRSFs VIs

State Healthy Disease Healthy Disease

7 dai 88.7 84.2 73.5 80.5

14 dai 92.4 90.1 81.2 84.8

21 dai 97.5 95.3 88.6 79.8

28 dai 99.2 97.9 95.4 92.7

31 dai 98.8 100 96.8 98.2

34 dai 96.7 100 95.2 98.4

41 dai 98.9 98.2 96.1 98.5

two classifiers improved steadily. Throughout the experiment, the classification accuracy of SVM was almost consistent with or higher than visual recognition. The highest accuracies of SVM based on WRSFs and VIs for detecting rust infections were 100% and 98.5%, respectively.

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3.1.2

Monitoring at Canopy Scale

The spectral response characteristics of wheat canopy to fungal stress are of great significance to the use of hyperspectral remote sensing data for precise disease control (Singh et al., 2020). Figure 3.9 shows pictures of healthy and yellow rust infested wheat canopy. Figure 3.10 shows the average spectral curves of healthy and infested wheat canopy at different growth stages. It can be seen from the figure that healthy wheat canopy at each growth stage have higher reflectance in the near infrared region. In the five growth stages in the figure, the shape of healthy wheat canopy reflectance curve was basically similar to infested canopy counterpart. As wheat grows, the reflectance of the canopy spectrum in the visible light region increased, when the reflectance in the near-infrared region first increased and then decreased (Feng et al., 2017). Compared with healthy wheat canopy, in the same growth period, the spectral reflectance of infested wheat canopy in the visible region was higher, while the spectral reflectance in the near-infrared region, in comparison, lower. On the 225th days after sowing (DAS), 230 DAS and 238 DAS, the reflectance of infested wheat canopy in 500–700 nm was higher than that of healthy wheat canopy, while the reflectance of near-infrared region was lower than that of healthy wheat canopy. The dynamic changes of wheat canopy spectrum in different growth stages laid the foundation for establishing and analyzing the quantitative relationship between severity of yellow rust and wheat canopy spectrum characteristics (Sankaran et al., 2010). In this study, within the range of 350–1000 nm, independent t-test was used to extract the significant different bands between healthy and infested wheat canopies in the five growth stages. The analysis results are shown in figure 3.11. It can be seen that there was no sensitive band in 207 DAS, because the symptoms of yellow rust have not yet appeared at this stage. In the other four growth periods, the green band, red band and red edge band showed significant differences between healthy and infested wheat canopies. In addition, in 230 DAS and 238 DAS, the

FIG. 3.9 – Healthy wheat canopy (left) and yellow rust infested canopy (right).

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FIG. 3.10 – Average spectral curves of healthy and infested wheat canopies at different growth stages.

FIG. 3.11 – The significant different bands between healthy and infested wheat canopies in different growth stages. differences in near-infrared band were also very significant. Through this analysis, the study determined the sensitive bands of wheat yellow rust at different stages, i.e. at 216 DAS and 225 DAS, the sensitive bands were located at 694–711 nm and 519–720 nm. At 230 DAS, the sensitive bands were located at 554–717 nm and 772–936 nm. At 238 DAS, the sensitive bands were located at 594–701 nm and 731–1000 nm. The analysis results showed that although there were differences in the sensitive bands of wheat yellow rust at different stages, some sensitive bands kept unchanged in a certain period of time. The sensitive bands of 216 DAS and 225 DAS were in the visible region, and the sensitive bands of 230 DAS and 238 DAS were in the visible and near-infrared region. In the late growth stage of healthy wheat, the leaves began to wither and the nutrients began to concentrate on the wheat ears, resulting that the spectrum of healthy canopy in the late growth stage was similar to the spectrum of infested canopy in the early growth stage. To reduce the influence of healthy wheat wilt on

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disease identification, the study merged the four growth stages into two stages, with the first stage included 216 DAS and 225 DAS, and the second stage included 230 DAS and 238 DAS. Based on the above results, the correlation analysis method was used to analyze the correlation between the spectral reflectance of wheat canopy and the severity of yellow rust. Figure 3.12 shows the correlation analysis results and table 3.4 presents the sample size and disease severity under different experimental conditions. It can be found that in the early and middle stages, the spectral reflectance of 460–720 nm is highly correlated with the severity of yellow rust, especially in the green and red edge regions. In the middle and late stages, the spectral reflectance of 568–709 nm and 727–1000 nm is highly correlated with the severity of yellow rust, especially in the red edge and near-infrared region. This is due to the time lag between yellow rust infection and internal structure damage at the initial stage (Devadas et al., 2009), which leads to the bands of near-infrared region being insensitive at this stage, but more sensitive in the middle and late stages of growth. Spectral indices are widely used in the retrieval of vegetation parameters. Table 3.5 shows the correlation results between commonly used spectral indices and severity of yellow rust in different growth stages. It can be found that except for transformed chlorophyll absorption in reflectance index (TCARI), the other spectral indices are significantly correlated with the severity of yellow rust in each growth period. However, these spectral indices are not applicable to all growth stages of wheat. For example, TCARI, physiological reflectance index (PhRI), ratio vegetation structure index (RVSI) and MCARI are more sensitive to yellow rust in the early growth period, but the correlation is poor in the middle and late stages. Yellow rust index (YRI) is most sensitive to yellow rust in the early growth period with

FIG. 3.12 – Correlation analysis results of reflectance and severity of yellow rust.

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TAB. 3.4 – Sample size and disease severity under different experimental conditions. Experiments

DI range

Experiment 1 (2003)

Experiment 2 (2018)

30% 30%

Number of samples in different growth stages Early-mid growth stage 21 29 12 22 17 5

Mid-late growth stage 10 3 48 14 1 29

TAB. 3.5 – Correlation results between commonly used spectral indices and severity of yellow rust in different growth stages. Spectral indices SIPI PRI TCARI NDVI NPCI PSRI PhRI ARI MSR RVSI MCARI YRI GI TVI NRI

Response to yellow rust at different growth stages Early-mid growth stage 0.52*** 0.65*** 0.30*** 0.40*** 0.52*** 0.53*** 0.48*** 0.50*** 0.36*** 0.42*** 0.58*** 0.001 0.29*** 0.10** 0.27***

Mid-late growth stage 0.60*** 0.78*** 0.005 0.62*** 0.79** 0.68*** 0.07 0.81*** 0.68*** 0.05 0.06 0.36*** 0.71*** 0.54*** 0.65***

All growth stage 0.67*** 0.83*** 0.07* 0.72*** 0.80*** 0.74*** 0.19*** 0.85*** 0.73*** 0.40*** 0.29*** 0.47*** 0.67*** 0.64*** 0.66***

*

Indicates the correlation is significant at the 0.950 confidence level. **Indicates the correlation is significant at the 0.990 confidence level. ***Indicates the correlation is significant at the 0.999 confidence level.

R2 = 0.65. And ARI is most sensitive to yellow rust in the middle and late stages with R2 = 0.81 (Devadas et al., 2015; Huang et al., 2007). Among all the spectral indices, SIPI, PRI, NDVI, PSRI, ARI, modified simple ratio (MSR), GI and nitrogen reflectance index (NRI) have their own advantages and can be used for follow-up research. The sensitive band of disease contains the important spectral information for constructing the disease identification index (Zhang et al., 2019). The sensitive

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spectral bands of wheat yellow rust at different growth stages are different and need to be discussed separately. Considering that PRI includes two green bands and ARI includes green and red bands, this study attempts to propose a three-band index based on the disease-sensitive bands. The new three-band index is expressed as equations (3.1) and (3.2). Rk1  Rk2 Rk1 þ Rk3

ð3:1Þ

1 1  Rk1  Rk2 Rk2  Rk3

ð3:2Þ

PRIðk1; k2; k3Þ ¼

ARIðk1; k2; k3Þ ¼

where Rλ1, Rλ2, and Rλ3 are the spectral reflectance of wavelengths from the sensitive bands at different growth stages, and λ1 ≠ λ2 ≠ λ3. Sensitive bands at different growth stages can be used for the construction of the new index. Hyperspectral data provide many possible combinations for the new index. Therefore, the study samples the bands at a certain interval. According to the research results of Thenkabail et al. (2011), this study used 3 nm as the step size to sample the bands, and combined all the sampling bands in sequence to develop the three-band index. With the severity of yellow rust as the independent variable, and all possible three-band indices as the dependent variables, the correlations between indices and the severity of yellow rust were calculated by using correlation analysis to evaluate the monitoring effect of indices on the disease severity. Figure 3.13 shows the results of the correlation analysis. The x, y, and z axes in the figure represent the wavelength regions that are sensitive to the discrimination of yellow rust disease. Comparing the values of correlation coefficients between all possible indices and the severity of yellow rust, it can be found that in the early to mid-term of wheat growth, PRI (λ1, λ2, λ3) is the most sensitive index with R2 = 0.67. In the middle to late stages of wheat growth, ARI (λ1, λ2, λ3) is the most sensitive index with R2 = 0.83. In this study, PRI (λ1, λ2, λ3) and ARI (λ1, λ2, λ3) were selected for subsequent research on the identification of infested wheat in the early to middle and middle to late stages of wheat growth, respectively. Based on the previous conclusion, in the early growth stage of wheat, the three bands in the best index PRI (λ1, λ2, λ3) use the green bands and the red bands. In the middle and late growth stages of wheat, the three bands in the best index ARI (λ1, λ2, λ3) use the red edge bands and the near-infrared bands. In this study, the central wavelength of most correlated range was selected as the composition band of new index. Finally, for the early stage of wheat growth, the three selected bands are 570 nm, 525 nm and 705 nm. For the middle and late stages of wheat growth, the three selected bands are 860 nm, 790 nm and 750 nm. Since the spectral characteristics of the infested canopy with DI < 0.05 are similar to those of healthy canopy, the samples with DI < 0.05 are regarded as healthy samples in this experiment. In this study, spectral data of healthy and infested wheat canopy samples collected in the experiment were used as validation set to evaluate the performances of the newly constructed indices in yellow rust

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FIG. 3.13 – Correlation analysis results between the severity of yellow rust and the new three-band indices.

monitoring. Table 3.6 shows the performances of the newly constructed indices based on the LDA model. It can be found that the overall monitoring accuracy of PRI (570, 525, 705) in the early stage of wheat growth is 80.6%, and the kappa coefficient is 0.61. The overall monitoring accuracy of ARI (860, 790, 750) in the middle and late stages of wheat growth is 91.9%, and the kappa coefficient is 0.75. The study further evaluated and compared the performances of the two newly constructed indices and the commonly used vegetation indices on yellow rust monitoring in the early, middle and late stages of wheat growth. The results are shown in table 3.7. It can be found that PRI (570, 525, 705) has the highest monitoring accuracy in the early and middle stages of wheat growth (OAA = 79.0%). In the middle and late stages of wheat growth, ARI (860, 790, 750) has the highest monitoring accuracy (OAA = 91.9%), followed by PRI and NPCI with the OAA at 87.5%, 79.0%. In general, PRI (570, 525, 705) and ARI (860, 790, 750) have better performance in wheat yellow rust monitoring than other vegetation indices. Section 3.1 describes the monitoring methods for wheat yellow rust at leaf scale and canopy scale. CHL, NBI, ANTH and PDM were found to show different characteristics at leaf scale as the disease progressed, and the section extracted wavelet features and constructed yellow rust monitoring model using SVM, which achieved good monitoring results. At canopy scale, Section 3.1 analyzes the bands and band combination forms that are sensitive to yellow rust. PRI and ARI were

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TAB. 3.6 – Overall performances of the newly constructed indices on wheat yellow rust monitoring. U/%

OAA/%

Kappa

20

Yellow rust 11

64.50

80.60

0.61

1

30

96.80

95.20 10

73.20 5

66.70

91.90

0.75

0

47

100

100

90.40

Healthy

Early-mid growth stage PRI (570, 525, 705)

Mid-late growth stage ARI (860, 790, 750)

Healthy Yellow rust P/% Healthy Yellow rust P/%

Note: P = producer’s accuracy, U = user’s accuracy, OAA = overall accuracy.

TAB. 3.7 – Comparison of the performances of PRI (570, 525, 705), ARI (860, 790, 750), and commonly used vegetation indices on yellow rust monitoring. Early-mid growth stage (216 DAS, 225 DAS) Index

PRI (570, 525, 705) ARI (860, 790, 750) PRI ARI SIPI NDVI GI MSR PSRI NRI

Overall classification accuracy/%

Recall Healthy/%

80.6

Mid-late growth stage (230 DAS, 238 DAS)

Yellow rust/%

Overall classification accuracy/%

Recall Healthy/%

Yellow rust/%

95.2

73.2

/

/

/

/

/

/

91.9

100.0

90.4

79.0 79.0 77.4 77.4 74.2 71.0 77.4 69.4

90.5 81.0 81.0 76.2 66.7 71.4 81.0 76.2

73.2 78.0 75.6 78.0 78.0 70.7 75.6 65.9

87.5 77.4 58.1 79.0 69.4 71.0 77.4 64.5

100.0 100.0 100.0 100.0 100.0 80.0 100.0 100

84.6 73.1 50.0 75.0 63.5 69.2 73.1 57.7

found to be more effective and a yellow rust monitoring index was constructed accordingly. The experimental results showed that the newly constructed index achieved good monitoring results.

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Wheat Powdery Mildew Monitoring Monitoring at Leaf Scale

The experiment was conducted at the Xiaotangshan National Precision Agriculture Demonstration Base in Changping District, Beijing (40°10.6′N, 116°26.3′E). The Chinese Academy of Agricultural Sciences (39°58.2′N, 116°20.1′E) and Beijing Academy of Agriculture and Forestry Sciences (39°54.3′N, 116°19.7′E) were in a filling season of winter wheat in 2002, 2003, 2005 and 2012. In 2002 and 2003, winter wheat leaves were infested with yellow rust and powdery mildew, while in 2005, the wheat were only inoculated with wheat rust. For 2012, we only collected the spectral data of leaves inoculated with powdery mildew. Table 3.8 gives details of the experimental schedule. We chose two varieties “98 to 100” and “Jingdong 8” for experiments, due to their high sensitivity to yellow rust and powdery mildew. The nutrient composition of the topsoil (depth 0–30 cm) at the test site is as follows, i.e. soil organic matter 1.41%–1.47%, nitrogen 0.07%–0.11%, effective phosphorus content 20.5–55.8 mg/kg, and fast effective potassium 116.6–128.1 mg/kg. According to the national plant protection standards, winter wheat “98 to 100” and “Jingdong 8” were inoculated against yellow rust and powdery mildew through spore inoculation in early April. The ASD FieldSpec spectrometer (Analytical Spectrum Devices, Inc., Boulder, Colorado) equipped with a Li Cor 1800-12 integrating sphere (Li Cor, Inc., Lincoln, Nebraska) was utilized to measure the spectral reflectance of the leaf. The spectrometer is equipped with a bare fiber with a 25° field of view and could get the spectral reflectance in range of 350–2500 nm. The sampling interval between 350–1050 nm is 1.4 nm, and the sampling interval between 1050–2500 nm is 2 nm (Gholizadeh et al., 2020). We used DI to describe the severity of wheat disease and the part of vacation covered by disease pustules (Graeff and Claupein, 2007). All sampled leaves were inspected in accordance with the National Crop Disease Investigation and Forecast Rules (GB/T 15795-1995). Due to the difficulty of accuracy assessment, sampling

TAB. 3.8 – Basic information of disease inoculation experiments. Year 2002 2003 2012

Inoculations

Experimental site

Powdery mildew Powdery mildew Powdery mildew

Xiaotangshan National Experiment Station for Precision Agriculture Chinese Academy of Agricultural Science Beijing Academy of Agriculture and Forestry Science

Measurement date

Sample size

May 23

50

May 22

56

May 13

52

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leaves with lesion coverage less than 1% were classified as healthy. Due to their strong absorption by water vapor and weak spectral signal (Tafintseva et al., 2020), we removed the spectral wavelength ranges between 1330–1450 nm, 1770–2000 nm and 2400–2500 nm. All experiments were strictly performed, therefore, only the atmospheric conditions of the measurement location and date were regarded as the key factors affecting the measurement. To suppress these differences in illumination caused by atmospheric differences, we matched the data in 2003, 2005, and 2012 with the year 2002 data by dividing the annual ratio spectrum curve. A total of three ratio curves were generated (figure 3.14). As a result, all hyperspectral data collected in 2003, 2005, and 2012 were divided by the corresponding ratio curve to generate a set of normalized spectra. Therefore, in 2003, the difference in illuminance between 2005 and 2012 was relatively suppressed to a level similar to the experiment in 2002. This processing step eliminated possible noise caused by differences in measurement locations and dates, thereby improving the comparability between annual data sets without changing the internal relationships reflected by the original data (Zhang et al., 2012; Poudel et al., 2005). The ultimate goal of this pre-processing is to eliminate the changes in lighting conditions caused by the differences between the experimental location and the date. The annual ratio is the average spectral curve of healthy samples in the year of 2002 experiment divided by the average curve of healthy samples in the years of 2002, 2003, 2005, and 2012 experiments. CWT is an excellent signal analysis tool for remote sensing data processing. It provides a powerful method to detect and analyze weak signals of various scales and resolutions, as well as to analyze multi-dimensional signals (such as image cubes) in a continuous range. The main purpose of this section is to explore the potential of wavelet feature in disease detection and discrimination. A series of wavelet features

FIG. 3.14 – Ratio curves for data normalization between different years.

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FIG. 3.15 – Visualization of correlation scalograms of CWT produced with the powdery mildew databases of (a) Year 2002, (b) Year 2003, and (c) Year 2012. The R2 at each wavelength and scale was displayed as brightness. The orange area encompasses the features with R2 > 0.85.

were extracted by using the CWT method, including the generation of wavelet power scale maps, the calculation of related scale maps and the identification of orthogonal WF. Figure 3.15a and c show the relative proportions generated by the powdery mildew infested dataset in the years of 2002, 2003, and 2012 cases. For the year 2002 case, in the blue edge (350–480 nm), red band (620–670 nm) and near-infrared region, a threshold of R2 = 0.85 determined a total of 10 highlighted features from the region 760 nm to 1150 nm and 2200 nm to 2350 nm. For 2003 and 2012 cases, 17 and 13 features in 350 nm to 980 nm, 1100 nm to 1300 nm, and 2200 nm to 2350 nm were respectively identified. The intersection of the WFs selected from the correlation scale map and the three-year data indicated that the training data affected by powdery mildew is related to the blue edge, near-infrared and SWIR area in 21–24 wavelet power. Table 3.9 summarizes the position and proportion of the center WF selected from the intersection of the scale bars of powdery mildew cases (the maximum R2 in the characteristic area).

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TAB. 3.9 – Summary of central bands of WFs selected from the intersection of correlation scalograms for powdery mildew datasets. WFs WF01 WF02 WF03 WF04 WF05 WF06 WF07 WF08 WF09 WF10

3.2.2

Wavelength/nm 366 438 621 795 932 978 1159 1259 2234 2347

Scale 24 22 22 26 25 23 24 23 22 24

R2 0.99 0.96 0.97 0.98 0.98 0.94 0.96 0.95 0.94 0.97

Monitoring at Canopy Scale

Compared with leaf-scale monitoring, the canopy spectral response characteristics of wheat powdery mildew is of greater significance for conducting optical remote sensing monitoring in the field. The canopy spectral data and disease severity data of wheat powdery mildew measured in 36 plots were selected to study the canopy spectral response of wheat powdery mildew. From the spectral reflectance curve (figure 3.16), there is a clear difference between the spectra of healthy and infested wheat. With the improvement of the disease level (from 20% to 80%), the powdery mildew canopy spectrum showed an increase in reflectance in the visible light band of 350–700 nm, and a reflectance in the near infrared part of 700–1350 nm began to appear.

FIG. 3.16 – Species curve of wheat powdery mildew and healthy canopy with different severity (filling stage).

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A staggered trend, that is, the spectrum of healthy wheat is higher than the infested spectrum before 1000 nm, and then the disease spectral reflectance starts to exceed that of healthy wheat in the long wavelength direction, and the spectral reflection is maintained in the short-wave infrared range of 1350–2500 nm. The rate tends to increase with the severity of the disease. The change in trend of the above reflectance can also be clearly observed in the spectral ratio curve in figure 3.17. In figure 3.17, the ratio curve has a larger oscillation amplitude after 2000 nm, which is due to the decrease of the signal-to-noise ratio in this band. From figure 3.16, it can be observed that the smoothness of the spectrum in this band is significantly reduced. The above observations of wheat powdery mildew spectral morphology are generally consistent with the results of Huang et al. (2019). To further understand and extract the spectral features used to monitor powdery mildew, we used correlation analysis to understand the correlation between spectral features and disease severities. In addition to test the 32 spectral characteristics on the leaves above, 15 spectral characteristics which are suitable for canopy-scale remote sensing observations have also been added (table 3.10). These features

FIG. 3.17 – Canopy spectral ratio curve of wheat powdery mildew of different severities (disease/healthy). TAB. 3.10 – Spectral characteristics of canopy-scale disease monitoring. Group Broadband vegetation index Hyperspectral vegetation index

Definition Shortwave Infrared Water Stress Index, SIWSI Disease Water Stress Index, DSWI Moisture Stress Index, MSI

Formula (RNIR − RSWIR)/ (RNIR + RSWIR) (R802 + R547)/ (R1657 + R682) R1600/R819

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include indicators such as SAVI and OSAVI that are suitable for reducing the impact of soil background; indicators, such as SIWSI, DSWI, and MSI, are used to monitor vegetation moisture content. The latter two types of functions are added here because the spectrometer used for leaf scale researches does not include the band beyond 1050 nm. From the results of correlation analysis (table 3.11), among the 47 spectral characteristics tested, except SDy, Db, Area1070–1320, Area920–1120, ARI, λb, Wid1070–1320, TCARI, λy and the PhRI were not significantly correlated with DI (p-value < 0.05), and the remaining 37 spectral features were significantly correlated with powdery mildew disease. Among them, a total of 30 features’ R2 were higher than 0.6, 26 features’ R2 were higher than 0.7, and 12 features’ R2 were higher than 0.8. It is worth noting that the feature with the highest correlation coefficient was the wide-band feature SR, with its R2 reaching 0.891. And other wide-band features included RG, NLI, SIWSI, RDVI, RNIR, and DI had their R2 greater than 0.5 (except that RNIR was 0.498). Therefore, it showed that the broad band index has high potential for powdery mildew monitoring, which provided conditions for the application of spaceborne multispectral data in disease TAB. 3.11 – Correlation analysis of canopy-scale spectral characteristics and disease severity. Order 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Name SR MSR PRI RVSI RR DSWI SIPI NDVI OSAVI GNDVI SAVI NBNDVI MCARI RG CARI Dr NLI PSRI MSI SIWSI GI λr Area550-750 RDVI

R2 0.891 0.874 0.861 0.861 0.845 0.821 0.817 0.815 0.815 0.814 0.814 0.814 0.799 0.785 0.780 0.750 0.750 0.743 0.741 0.734 0.733 0.731 0.726 0.724

p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.719 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Order 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Name NRI NPCI Dep550–750 WI SDr Wid550–750 TVI SDb Wid920–1120 RNIR Dep920–1120 Dep1070–1320 Dy SDy Db Area1070–1320 Area920–1120 ARI λb Wid1070–1320 TCARI λy PhRI

R2 0.719 0.709 0.692 0.692 0.689 0.646 0.584 0.563 0.503 0.498 0.375 0.316 0.284 0.277 0.264 0.238 0.223 0.209 0.192 0.066 0.038 0.032 0.022

p-value 0.000 0.000 0.000 0.000 0.000 0.001 0.001 0.002 0.005 0.005 0.020 0.036 0.050 0.054 0.060 0.077 0.088 0.100 0.118 0.374 0.501 0.542 0.613

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monitoring. Comparing the response differences of each spectral index between the leaves and the canopy, it is found that the broad-band index performs better at two different scales, but the sequence of the response intensity of each spectral index is quite different. For example, GNDVI, RG, etc. show higher correlation coefficients at leaf scale, and the correlation coefficients are not the highest at canopy scale. SR and MSR, which have high correlation coefficients at canopy scale, are not the best features at leaf scale. This difference can find some clues on the characteristics of disease spectrum curves at leaf and canopy scales. The reduction of the disease spectrum in the near-infrared part is more obvious. Therefore, the response of the spectral characteristics to disease on the canopy scale is stronger than the response on the leaf scale as a whole (R2 is generally higher). This may be due to the fact that the disease canopy may have features similar to the leaf size, such as leaf lesions, as well as overall plant structure and morphology. For example, when the plant is infested by a disease, there could be changes such as leaf withering leaf inclination and plant clearance. The great change in the canopy level of the disease spectrum in the near infrared band may be related to this. In Section 3.2, the spectral characteristics of wheat powdery mildew at the leaf scale were analyzed, and then the spectral features which were sensitive to the disease were extracted by using wavelet transform. These wavelet features were found to be related to the blue edge, near-infrared and SWIR. At canopy scale, this section first analyzed the spectral characteristics of powdery mildew, and then extracted a variety of spectral features, such as continuous removal feature, broadband vegetation index and hyperspectral vegetation index, and correlated these spectral features with powdery mildew severity.

3.3 3.3.1

Wheat Fusarium Head Blight Monitoring Monitoring at Ear Scale

Figure 3.18a and b shows the spectral reflectance curves and the spectral reflectance ratio curves of healthy wheat ears and wheat ears infested by Fusarium head blight. Figure 3.18c shows the curves of correlation coefficient between the spectral reflectance and the severities of Fusarium head blight. It can be found that the spectral reflectance of wheat ears infested by Fusarium head blight increased in the range of 350–517 nm, 580–716 nm and 1162–2350 nm, while the changes were not obvious at 518–579 nm and 717–1161 nm. Wheat ears infested with Fusarium head blight initially had water-stained points at the base, then they gradually turned green and faded into brown lesions, and finally a layer of obvious pink mold grew at the joints of the glumes. When a spikelet was infested by Fusarium head blight, the spore could not only spread upwards and downwards, harming adjacent spikelets, but also extend into the cob, causing the cob to become brown and necrotic; and the upper non-infested spikelet would turn yellow and withered due to water shortage (Ma et al., 2020; Parry et al., 2010). In the later stage, purple-black coarse particles

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FIG. 3.18 – (a) The spectral reflectance curves of healthy wheat ears and wheat ears infested by Fusarium head blight; (b) the spectral reflectance ratio curves of healthy wheat ears and wheat ears infested by Fusarium head blight; (c) the curves of correlation coefficient between the spectral reflectance and the severity of Fusarium head blight. appeared in the infested area (Rojas et al., 2020; Bai and Shaner, 2004). The lack of water and the destruction of cell structure made the spectral reflectance of infested wheat ear infested by Fusarium head blight in the visible and SWIR region higher than that of healthy wheat ear (Bauriegel et al., 2011). In addition, it can be seen from figure 3.18c that the spectral reflectance of the visible and SWIR region has a high correlation with the severity of the disease, which illustrates the potential of hyperspectral data in identifying Fusarium head blight. Based on CWA, we obtained the correlation coefficient R2 between wavelet features and the severity of wheat Fusarium head blight at different wavelengths and scales, and the results are shown in figure 3.19. It can be seen that the range of R2 was 0–0.602. This study first selected the top 5% wavelet features with the strongest correlation, and arranged these features in descending order. We represent these features in orange in figure 3.19, and believed that these features were sensitive to Fusarium head blight. In addition, there were some sensitive wavelet features in the near-infrared region after continuous wavelet transformation, which showed that continuous wavelet transformation enhances the sensitivity of the spectral reflectance.

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FIG. 3.19 – Correlation coefficient R2 between wavelet features and the severity of wheat Fusarium head blight at different wavelengths and scales.

TAB. 3.12 – Correlation analysis of canopy-scale spectral characteristics and disease severity. Wavelet features WF02 WF06 WF09 WF11 WF13 WF21

Wavelength/nm 471 696 841 963 1069 2272

Scale 24 21 24 23 23 24

R2 0.539 0.602 0.441 0.548 0.422 0.544

Significance of T-test *** *** *** *** *** ***

Note: ***indicates that the significance reaches 0.001 significant level.

The wavelet feature with the highest R2 was selected in each sensitive interval, and 21 wavelet features were selected in the end. After that, the correlation analysis of all wavelet features was performed, and the wavelet features of high correlation were removed, with 6 wavelet features left in the end. The specific information of these wavelet features is shown in table 3.12. Among these wavelet features, four features i.e. (471 nm, 24), (696 nm, 21), (841 nm, 24) and (963 nm, 23) mainly contained information related to wheat ear pigments. The other two wavelet features i.e. (1069 nm, 23) and (2272 nm, 24) mainly contained information related to water content and internal cell structure. In addition, (696 nm, 21) had the strongest correlation with the severity of Fusarium head blight, indicating that the bands in red edge region were important for Fusarium head blight monitoring. The section uses the selected wavelet features as the input of the fisher linear discriminant analysis model, and then evaluates the monitoring accuracy of the model based on the experimental data. The monitoring accuracy is shown in table 3.13. It can be seen that the overall monitoring accuracy of the model was 88.7%, and the Kappa coefficient was 0.775. For healthy wheat ears and infested wheat ears, the PA of this model was 86.1% and 91.4%, and the UA was 91.2% and 86.5%, respectively. In addition, in order to evaluate the performance of the wavelet features in the SWIR region in Fusarium head blight monitoring, the study also developed a Fisher linear discriminant analysis model based on the four wavelet features in the SWIR region. The results showed that the overall monitoring accuracy of the model constructed based on the four wavelet features in the SWIR region

100

Validation

Field truth

Wavelet features

Six wavelet features in the whole spectral wavelength range

Four wavelet features concentrated in the range of 400–1000 nm

31

Fusarium head blight 3

5

32

37

36 86.1 29

35 91.4 3

71 32

90.6

7

32

39

82.1

36 80.6

35 91.4

71

Healthy Healthy Fusarium head blight Sum PA/% Healthy Fusarium head blight Sum PA/%

Sum

UA/%

OA/%

34

91.2

88.7

Kappa coefficient 0.775

86.5

85.9

0.719

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TAB. 3.13 – Monitoring accuracy of the Fisher linear discriminant analysis model based on all wavelet features and the four wavelet features in the SWIR region.

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was 2.8% lower than that of the model constructed based on all wavelet features. The above results indicated that the wavelet features can be used in the monitoring of wheat Fusarium head blight.

3.3.2

Monitoring at Canopy Scale

During the sampling process, a rectangular frame surrounded by UPVC pipes was used to delimit the canopy plot range. The size of the rectangular frame was 60 cm × 60 cm. The selected plots were located in the center of the field to ensure the uniformity of the wheat in the plots. At the same time, the wheat leaf area index (LAI) in the selected plots was relatively high, and the influence of other factors such as soil background can be ignored (Liu et al., 2020). The canopy-scale hyperspectral data were acquired on May 8, 2018. The wheat canopy non-imaging experiment was induced by natural light, and the ASD FieldSpec spectrometer was used to collect the canopy non-imaging spectral data. At the same time, the experiment also calculated the DI of the plot. The calculation method of DI refers to the National Standed of the People's Republic of China, Rules for Monitoring and Forecast of the Wheat Head Blight (GB/T 15796-2011). Figure 3.20 shows the photos of wheat canopies with different DIs. A total of 53 plots were surveyed. Figure 3.21 shows the average canopy spectrum curves of healthy, slightly infested, and severely infested plots. It can be seen from the figure that for healthy plots, the spectrum curve had obvious “green peaks” and “red valley” in the visible region. This phenomenon was similar to the characteristics of a typical plant spectrum curve (Xue and Su, 2017). For the slightly infested plots, the spectral curve in the red region had the same reflectance as the healthy plots, but the reflectance in the green was slightly decreased. For which, when the severity of Fusarium head blight was low, the spores firstly appeared at the bottom of the wheat ear and gradually infested adjacent wheat ears (Jin et al., 2018). At this time, the area covered by the spores on the wheat ear was smaller. Most wheat ears still had sufficient water and pigment, so the spectrum of healthy canopy and slightly infested canopy were similar in the visible region. Different from healthy plots and slightly infested plot, the spectral reflectance of severely infested plots had a greater increase in the red region, and the reflectance in the near-infrared region continues to decrease. For which, when the Fusarium head blight was more serious, most areas of the wheat ears were infested and symptoms such as dryness and fading appear (Rojas et al., 2020). The water loss of a large number of wheat ears and the destruction of the tissue structure directly leaded to changes in the overall structure and color of the canopy, which in turn lead to greater changes in the spectral reflectance. Figure 3.21b is the spectral curve of wheat canopy infested with different severity of Fusarium head blight after converted to Sentinel-2 multi-band reflectance. It can be seen from the figure that for healthy wheat canopy, the curve had higher reflectance in the red-edge 2 band, red-edge 3 band and near-infrared band, while the reflectance of the infested wheat canopy in these bands was lower, and the higher the severity of disease, the lower the reflectance. All plots had similar reflectance in

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FIG. 3.20 – Photos of wheat canopies with different DIs.

FIG. 3.21 – Average hyperspectral reflectance and simulated Sentinel-2 multispectral reflectance of healthy (green) and slightly (purple) and severely (red) infested wheat canopies.

the red-edge 1 band, while plots severely infested had higher reflectance in the red band. This may because that the center wavelength of red-edge 1 band was 705 nm, and it was located in the region where the factors determining spectral reflectance converted from pigment to canopy structure. In general, the curve of Sentinel-2 multi-band reflectance had a consistent trend with the curve of hyperspectral reflectance. Based on two forms of difference and ratio, the study combined the simulated Sentinel-2 bands in pairs, and calculated some vegetation indices including the

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difference vegetation index (DVI) (Jordan, 1969) and RVI (Ren and Zhou, 2019). Besides, ordinary least squares method was used to construct the wheat canopy DI monitoring model based on each index. The leave-one-out cross-validation method was used to verify the model accuracy, and the correlation coefficient between predicted DI and measured DI was also calculated. At the same time, the study also calculated the root mean square error (RMSE) between the predicted DI and the actual DI (Zhang et al., 2014). The calculation formula is as follows: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 Pn  i¼1 yest;i  yobs;i RMSE = ; ð3:3Þ n where n is the sample size, y est is the estimated DI, and y obs is the observed DI. The accuracy verification results of the models constructed based on all indices are shown in figure 3.22. From the figure, it can be seen that the models constructed based on NIR-R and RE3-R has the largest R2 and the smallest RMSE. Based on the above analysis results, this research proposed a Red-Edge Head Blight index (REHBI). REHBI indicated the area of a triangle formed by the reflectance in the red band, red-edge 3 band and near-infrared band (figure 3.23). REHBI was formulated as follows. REHBI ¼

ð842  665Þ  ðRRE3  RR Þ  ð783  665Þ  ðRNIR  RR Þ 2

ð3:4Þ

Among all parameters, RR , RRE3 , and RNIR were the reflectance of the red band, red-edge 3 band and near-infrared band. 665 nm, 783 nm, and 842 nm were the

FIG. 3.22 – Model verification with spectral indices.

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FIG. 3.23 – Physical significance of REHBI. center wavelengths of these bands. When wheat was infested by Fusarium head blight, the pigment content of wheat ears would be reduced, and the tissue structure of the wheat ears would be destroyed, which would lead to an increase of the reflectance in the red band and a decrease of the reflectance in red-edge 3 band and the near-infrared band. Eventually, the value of REHBI was decreased. The wheat Fusarium head blight monitoring models were developed based on REHBI and 14 vegetation indices, and the monitoring accuracies of these models were assessed by using validation dataset. The results are shown in table 3.14. It can be found that the R2 of monitoring models developed using traditional vegetation indices ranged from 0.29 to 0.77. RDVI preformed best among these vegetation indices with R2 of 0.77, and it was followed by OSAVI with R2 of 0.74. It should be noted that RDVI and OSAVI are two indices optimizing the NDVI to reduce the influence of soil. Considering the performance of NDVI is poor, it can be assumed that the soil background still has some effect on the canopy spectrum curve and will directly affect the monitoring accuracy of wheat Fusarium head blight. R2 of monitoring models developed using vegetation indices containing red-edge bands ranged from 0.21 to 0.53. PSRI1 preformed best among these vegetation indices with R2 of 0.53, and it was followed by NREDI1 with R2 of 0.46. NREDI1, NREDI2, and NREDI3 had similar formulas with NDVI, in which used the red-edge bands instead of the near-infrared band and red-band. The poor performance of these indices indicated that it is not enough to monitor Fusarium head blight by only using red-edge bands. In general, the monitoring model developed using REHBI had the best performance with R2 of 0.82 and RMSE of 10.1. This index combines the information of visible band, red-edge band and near-infrared band. The better

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TAB. 3.14 – Model evaluation with each index. Group New index

Conventional VIs

Red-edge VIs

Title REHBI HBI NDVI RGR VARIgreen OSAVI SR MSR GNDVI RDVI NDVIre1 NREDI1 NREDI2 NREDI3 PSRI1

R2 0.82 0.60 0.44 0.49 0.49 0.74 0.38 0.41 0.29 0.77 0.37 0.46 0.42 0.21 0.53

RMSE 10.1 15.3 18.1 17.2 17.2 12.4 19.1 18.6 20.3 11.5 19.1 17.8 18.4 21.5 16.5

performance of this index indicated that it is feasible to use REHBI, which developed based on the unique spectral characteristics of disease, to accurately monitor disease. At wheat ear scale, this section analyzed the spectral characteristics of Fusarium head blight and extracted the wavelet features using wavelet transforms, and finally constructed a disease monitoring model based on fisher linear discriminant analysis. At canopy scale, the REHBI was constructed based on the spectral characteristics of Fusarium head blight. The REHBI has been tested and found to be a good index for monitoring Fusarium head blight severity. In this chapter, wheat yellow rust, Fusarium head blight and powdery mildew were monitored by using remote sensing technology at leaf/ear scale and canopy scale. This chapter analyzes the characteristics and associations of spectral features of diseases at leaf/ear scale and canopy scale based on non-imaging hyperspectral data.By applying cutting-edge technologies such as correlation analysis and deep learning, the monitoring of the development process of major crop diseases in the temporal and spatial dimensions is realized at leaf/ear scale and canopy scale. The research results in this chapter provide a reference for the selection of spectral features for diseases monitoring on a large scales, and are the basis for subsequent diseases differentiation, and monitoring using imaging remote sensing data.

References Bai G., Shaner G. (2004) Management and resistance in wheat and barley to fusarium head blight. Annu. Rev. Phytopathol. 42, 135. Bandos T. V., Bruzzone L., Camps-Valls G. (2009) Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Trans. Geosci. Remote Sens. 47, 862.

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Bauriegel E., Giebel A., Geyer M., et al. (2011) Early detection of Fusarium infection in wheat using hyper-spectral imaging. Comput. Electron. Agric. 75, 304. Bürling K., Cerovic Z. G., Cornic G., et al. (2013) Fluorescence-based sensing of drought-induced stress in the vegetative phase of four contrasting wheat genotypes. Environ. Exp. Bot. 89, 51. Cheng T., Rivard B., Sánchez-Azofeifa A. (2010) Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens. Environ. 115, 659. Devadas R., Lamb D. W., Backhouse D., et al. (2015) Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat. Precis. Agric. 16, 477. Devadas R., Lamb D. W., Simpfendorfer S., et al. (2009) Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precis. Agric. 10, 459. Feng W., Qi S., Heng Y., et al. (2017) Canopy vegetation indices from in situ hyperspectral data to assess plant water status of winter wheat under powdery mildew stress. Front. Plant Sci. 8, 1219. Gholizadeh A., Neumann C., Chabrillat S., et al. (2020) Soil organic carbon estimation using VIS – NIR – SWIR spectroscopy: the effect of multiple sensors and scanning conditions. Graeff S., Claupein W. (2007) Identification and discrimination of water stress in wheat leaves (Triticum aestivum L.) by means of reflectance measurements. Irrigation Sci. 26, 61. Hearst M. A., Dumais S. T., Osuna E., et al. (1998) Support vector machines. IEEE Intell. Syst. App. 13, 18. Huang L., Ding W., Liu W., et al. (2019) Identification of wheat powdery mildew using in-situ hyperspectral data and linear regression and support vector machines. J. Plant Pathol. 101, 1. Huang W., Lamb D. W., Niu Z., et al. (2007) Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precis. Agric. 8, 187. Jin X., Jie L., Wang S., et al. (2018) Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sens. 10, 395. Jordan C. F. (1969) Derivation of leaf-area index from quality of light on the forest floor. Ecology. 50, 663. Liu L., Dong Y., Huang W., et al. (2020) A disease index for efficiently detecting wheat Fusarium head blight using sentinel-2 multispectral imagery. IEEE Access. 8, 52181. Ma Z., Xie Q., Li G., et al. (2020) Germplasms, genetics and genomics for better control of disastrous wheat Fusarium head blight. Theor. App. Genet. (Supplement 6). Moshou D., Bravo C., West J., et al. (2004) Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Comput. Electron. Agric. 44, 173. Parry D. W., Jenkinson P., Mcleod L. (2010) Fusarium ear blight (scab) in small grain cereals – a review. Plant Pathol. 44, 207. Poudel U. P., Fu G., Ye J. (2005) Structural damage detection using digital video imaging technique and wavelet transformation. J. Sound Vibr. 286, 869. Ren H., Zhou G. (2019) Estimating green biomass ratio with remote sensing in arid grasslands. Ecol. Ind. 98, 568. Rojas E. C., Sapkota R., Jensen B., et al. (2020) Fusarium head blight modifies fungal endophytic communities during infection of wheat spikes. Microb. Ecol. 79, 397. Sankaran S., Mishra A., Ehsani R., et al. (2010) A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72, 1. Shi Y., Huang W., González-Moreno P., et al. (2018) Wavelet-based rust spectral feature set (WRSFS): a novel spectral feature set based on continuous wavelet transformation for tracking progressive host–pathogen interaction of yellow rust on wheat. Remote Sens. 10, 525. Shi Y., Huang W., Zhou X. (2017) Evaluation of wavelet spectral features in pathological detection and discrimination of yellow rust and powdery mildew in winter wheat with hyperspectral reflectance data. J. Appl. Remote Sens. 11, 026025. Singh P., Pandey P. C., Petropoulos G. P., et al. (2020) Hyperspectral remote sensing in precision agriculture: present status, challenges, and future trends. Hyperspectral remote sensing. Elsevier, pp. 121–146.

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Su J., Liu C., Hu X., et al. (2019) Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery. Comput. Electron. Agric. 167, 105035. Tafintseva V., Shapaval V., Smirnova M., et al. (2020) Extended multiplicative signal correction for FTIR spectral quality test and pre‐processing of infrared imaging data. J. Biophotonics. 13, e201960112. Thenkabail P., Lyon J., Huete A. (2011) Advances in hyperspectral remote sensing of vegetation and agricultural croplands. Wan A. M., Chen X. M., He Z. (2007) Wheat stripe rust in China. Aust. J. Agric. Res. 58, 605. Xue J., Su B. (2017) Significant remote sensing vegetation indices: a review of developments and applications. J. Sens. 1353691. Zhang J., Huang Y., Pu R., et al. (2019) Monitoring plant diseases and pests through remote sensing technology: a review. Comput. Electron. Agric. 165, 104943. Zhang J. C., Pu R. L., Wang J. H., et al. (2012) Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements. Comput. Electron. Agric. 85, 13. Zhang J., Pu R., Yuan L., et al. (2014a) Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery. PLoS One. 9, e93107. Zhang J., Yuan L., Pu R., et al. (2014b) Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat. Comput. Electron. Agric. 100, 79. Zheng Q., Huang W., Cui X., et al. (2018a) Identification of wheat yellow rust using optimal three-band spectral indices in different growth stages. Sens. (Basel). 19, 35. Zheng Q., Huang W., Cui X., et al. (2018b) New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery. Sens. (Basel). 18, 868.

Chapter 4 Crop Pest Monitoring The above chapters focused on the application of crop disease remote sensing monitoring technology, indicating the potential of remote sensing technology in disease monitoring. The remote sensing monitoring in pests and diseases have similarities and differences. Taking crop pests as study object, this chapter analyzes the spectral characteristics of crop infested by pests, uses feature extraction and modeling methods to extract the features closely related to pests, construct hyperspectral remote sensing monitoring model, and explain the non-imaging remote sensing monitoring technology of crop pests. First, we take wheat aphid and rice leaf roller as example to analyze the spectral response when crop infested by pests at leaf and canopy scales and clarify the spectral differences between healthy crop and infested crop. Then, we use vegetation index, sensitivity analysis, continuous wavelet, and other feature extraction methods to extract the sensitive characteristics of specific pests, which provides input parameters for the remote sensing pest monitoring model. Finally, we construct a monitoring model through linear regression, PLS and other model construction methods, and provide the ideas for crop pests hyperspectral monitoring. This chapter introduces the non-imaging remote sensing monitoring mechanism of crop pests, analyzes the spectral response characteristics of pests, extracts sensitive spectral characteristics, and constructs the model between the severity and the spectral characteristics. The chapter provides reference for crop pests monitoring using imaging remote sensing technology.

4.1

Wheat Aphid Monitoring

Wheat aphid is one of the main pests in China, which seriously affects vegetation photosynthesis, thereby reducing wheat production. To effectively control wheat aphid, timely and accurately regional monitoring and prediction of its occurrence is an important premise. In Section 4.1.1, we mainly introduce spectral response and pest extraction. In Sections 4.1.2 and 4.1.3, we mainly introduce aphid monitoring at leaf and canopy scales with hyperspectral remote sensing.

DOI: 10.1051/978-2-7598-2659-9.c004 © Science Press, EDP Sciences, 2022

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4.1.1

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

Spectral Response

Wheat leaf is the most important component of wheat, which plays a vital role in wheat photosynthesis, growth, and nutrition supply. However, the main infested part of wheat aphid is wheat leaf. At leaf scale, spectral characteristics of leaf is not affected by soil, atmosphere, vegetation geometry, coverage, etc., so it is easier to understand their pure spectral characteristics. Moreover, the study of the leaf spectral characteristics and mechanisms with aphid can provide a theoretical basis for further study of aphid monitoring with satellite remote sensing. To extract the location and range of aphid sensitive spectral bands, correlation analysis is carried out between bands and aphid quantity, with the results shown in figure 4.1. The bands with its significance test at 0.05 level are 380–730 nm, 1405–1502 nm, 1868–2500 nm, and 751–1156 nm. The bands with its significance test at 0.01 level are 387–427 nm, 469–729 nm, 1881–2065 nm, 2272–2305 nm, and 760–1000 nm. Among them, sensitive bands in visible and short-wave infrared regions positively correlate with aphid quantity, while sensitive bands in near infrared region negatively correlate with aphid quantity. In addition, correlation coefficient of visible sensitive band is the largest (R = 0.504), followed by near-infrared band (R = −0.369), and short-wave infrared band has the lowest correlation. Figure 4.2 is the first order differential spectral curve of leaf infested by aphid and healthy leaf within 450–950 nm. According to figure 4.2, spectral reflectance differences and change rates of red bands 704–720 nm between leaf infested by aphid and healthy leaf are the largest, indicating that red band range has a strong response to aphid. Besides, in the band region of 510–530 nm and 680–760 nm, corresponding to green edge and red edge, there are significant differences in first order differential spectrum between healthy and infested leaf. At the red edge, “blue shift”

FIG. 4.1 – Correlation coefficient curve of aphid quantity and band.

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FIG. 4.2 – First order differential spectra of healthy and aphid infested wheat leaves. phenomenon of first order differential is very obvious, which means that red edge can be used to detect vegetation state (Miller et al., 1991; Baret et al., 1994).

4.1.2

Monitoring at Leaf Scale

This section uses commonly hyperspectral data analysis methods (e.g., differential spectrum, continuum removal method, vegetation index method) and data mining methods (e.g., continuous wavelet analysis method) to extract wheat aphid sensitive spectral features. Then, the multivariate inversion model of aphid quantity is established with these selected sensitive features as input by using PLS method, and then model validation and evaluation is conducted. 1. Spectral feature extraction Spectral differential technology is a basic hyperspectral analysis technology, which can reduce background noise and improve resolution of overlapping spectrum. Spectral differential feature focuses on reflecting amplitude and position of vegetation spectral curve within specific region. From spectral response mechanisms of aphid, it can be known that the impact on biochemical components and physiological structure of leaf will be reflected in amplitude and position of changes in some specific region of the spectrum. Therefore, hyperspectral parameters after spectral differential transformation including positions, areas, ratios, and normalized index parameters, are used to extract the spectral features of aphid damage. We selected all spectral differential parameters in effective reflectance region of 450–950 nm, including maximum values of first order differential in blue edge (490–530 nm),

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yellow edge (550–582 nm) and red edge (670–737 nm), and their corresponding wavelengths, and first order differential sums, reflectance and position of green peak, ratio of above parameters and normalized spectral indices, in which totally 20 spectral differential parameters are shown in tables 1.1 and 4.1.

TAB. 4.1 – Hyperspectral differential features for wheat aphid. Type Spectral area features

Features SDg SDr/SDb

SDr/SDy

SDr/SDg

SDy/SDb

SDg/SDb Spectral index features

(SDr − SDb)/ (SDr + SDb) (SDr − SDy)/ (SDr + SDy) (SDr − SDg)/ (SDr + SDg) (SDy − SDb)/ (SDy + SDb) (SDg − SDb)/ (SDg + SDb)

*

Definition The sum of the first-order differential band values in the green-side wavelength range The ratio of the sum of the first order differential in the red edge to the sum of the first order differential in the blue edge The ratio of the sum of the first order differential in the red border to the sum of the first order differential in the yellow border The ratio of the sum of the first order differential in the red border to the sum of the first order differential in the green border The ratio of the sum of the first order differential in the yellow border to the sum of the first order differential in the blue border The ratio of the sum of the first-order differential in the green border to the sum of the first order differential in the blue border The normalized value of the sum of the first order differential in the red border and the sum of the first order differential in the blue border The normalized value of the sum of the first order differential in the red border and the sum of the first order differential in the yellow border The normalized value of the sum of the first-order differential in the red border and the sum of the first order differential in the green border The normalized value of the sum of the first order differential in the yellow edge and the sum of the first order differential in the blue edge The normalized value of the sum of the first order differential in the green border and the sum of the first order differential in the blue border

Note: Codes and definitions in the table refer to Hyperspectral Remote Sensing and its Applications (2000) 205–206 (Pu and Gong, 2000).

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TAB. 4.2 – Correlation between features and aphid quantity based on differential spectrum. Spectral features Db Dy Dr Λb Λy Λr

R

R2

p-value

0.634 0.506 −0.693 −0.460 −0.108 −0.589

0.402 0.256 0.480 0.211 0.012 0.347

0.000 0.002 0.000 0.005 0.529 0.000

SDb

0.707

0.500

0.000

SDy

−0.542

0.294

0.001

SDr

−0.416

0.173

0.012

SDg

0.708

0.501

0.000

Spectral features SDr/SDb SDr/SDy SDr/SDg SDy/SDb SDg/SDb (SDr − SDb)/ (SDr + SDb) (SDr − SDy)/ (SDr + SDy) (SDr − SDg)/ (SDr + SDg) (SDy − SDb)/ (SDy + SDb) (SDg − SDb)/ (SDg + SDb)

R

R2

p-value

−0.778 0.728 −0.784 0.781 −0.627 −0.778

0.606 0.530 0.615 0.610 0.393 0.605

0.000 0.000 0.000 0.000 0.000 0.000

0.727

0.528

0.000

−0.785

0.616

0.000

0.745

0.553

0.000

−0.623

0.388

0.000

Correlation analysis method is performed on spectral differential parameters and aphid quantity. Table 4.2 shows correlation analysis results, including correlation coefficient, determination coefficient, and p-value of F test. It shows that most of the spectral differential parameters are significantly correlated with aphid quantity except λy. Db, Dy, SDb, SDg, SDr/SDy, SDy/SDb, (SDr − SDy)/(SDr + SDy), and (SDy − SDb)/(SDy + SDb) have significant positive correlation with aphid quantity, and other features have significant negative correlation with aphid quantity. Among them, first order differential spectral position features have lower correlation with aphid quantity than area parameters, ratios, and normalized differential spectral characteristics, with the highest position features Dr (R2 = 0.480). While for determination coefficients of SDb and SDg in area features are higher than 0.5. Correlation between area feature ratio, normalized parameter and aphid quantity is extremely significant, except that SDg/SDb and (SDg − SDb)/ (SDg + SDb) are lower than 0.5 (R2 = 0.39 and R2 = 0.388, respectively), and determination coefficients of other features are all greater than 0.5, and SDr/SDg, SDy/SDb, (SDr − SDb)/(SDr + SDb) and (SDr − SDg)/(SDr + SDg) are higher than 0.6. The above results show that most of spectral features extracted after differential transformation can better extract aphid quantity information. Among them, area features, ratios and normalized parameters are more effective in characterizing aphid quantity than position features. The ratio of area features and normalized features is the best for extracting aphid information. 2. Monitoring with continuum removal method The absorption features of spectrum can be used for some crop biochemical component inversion. The continuum removal is an effective method for absorbing

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TAB. 4.3 – Spectral absorption characteristic list based on continuum removal method. Variable H W1 W2 S1 S2 λ1 λ2 λ3

Definition Absorption characteristic depth relative to envelope within the band range of 550–750 nm The band width to the left of the absorption feature The band width to the right of the absorption feature The absorption area to the left of the absorption feature The absorption area to the right of the absorption feature The position of the band from which the absorption feature begins The position of the band at the end of the absorption feature The band position of the absorption feature

valley features information extraction with hyperspectral data. It is mainly used to extract absorbing valley features formed by chlorophyll and water absorption, including absorption wavelength positions, depths, widths, slopes, areas, etc. This method has been widely used in research of crop pests and diseases spectral feature extraction (Broge and Mortensen, 2002). Based on spectral response characteristics of wheat aphid, we extract continuum removal characteristics of chlorophyll absorption region in red band. The specific names and definitions of features are shown in table 4.3. Table 4.4 shows correlation between absorption features and aphid quantity. From table 4.4, it can be known that correlation between each absorption features and aphid quantity are significant when p-value is less than 0.05. Among them, S1, S2, λ2, and H have negative correlation with aphid quantity, and W1, W2, λ1, and λ3 have positive correlation with aphid quantity. S2, W2, and λ2 are significancetly related to aphid quantity, but their determination coefficients are all lower than 0.5, with the maximum is W2 as R2 = 0.429. TAB. 4.4 – Correlation between absorption features and aphid quantity. Spectral features S1 W1 λ1 λ3

R

R2

p-value

−0.278 0.393 0.518 0.510

0.077 0.154 0.269 0.260

0.050 0.009 0.001 0.001

Spectral features S2 W2 λ2 H

R

R2

p-value

−0.645 0.655 −0.561 −0.367

0.416 0.429 0.314 0.135

0.000 0.000 0.000 0.014

3. Monitoring with vegetation indices Vegetation index is the most basic and common information extraction method in remote sensing monitoring. We tried to extract aphid information through common vegetation index. Based on spectral response characteristics of wheat

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aphid, and with reference to application of various vegetation index to monitor pests and diseases, 16 vegetation indices were constructed including PRI, MCARI, AI, TVI, DSSI1, SIPI, NPCI, PSRI, NDWI, GNDVI in table 1.1 and others in table 4.5. Table 4.6 shows correlation analysis results between vegetation index and aphid quantity. It can be seen that correlation between other vegetation index and aphid quantity are of significance with p-value < 0.05, except ARI, DSSI2, PhRI, and NRI. AI, DSSI2, PRI, NDWI, and NBNDVI are negative correlated with aphid quantity, and the others are positive correlated between vegetation indices and aphid quantity. The determination coefficients R2 of AI, GNDVI and RVSI are all greater than 0.5, which are 0.580, 0.500 and 0.608, respectively. TAB. 4.5 – List of vegetation indices. Spectral index Anthocuanin Reflectance Index, ARI

Calculation formula (R550)−1 − (R700)−1

Narrow-Band Normalized Difference Vegetation Index, NBNDVI Nitrogen Reflectance Index, NRI

(R850 − R680)/(R850 + R680)

References Gitelson et al. (2001) Thenkabail et al. (2000) Filella et al. (1995) Mirik et al. (2007) Gamon et al. (1997) Merton (1998)

(R570 − R670)/(R570 + R670) (R747 − R901 − R537 − R572)/ (R747 − R901 + R537 − R572) (R550 − R531)/(R550 + R531)

Damage Sensitive Spectral Index2, DSSI2 Physiological Reflectance Index, PhRI Red-edge Vegetation Stress Index, RVSI

[(R712 + R752)/2] – R732

TAB. 4.6 – Correlation between vegetation index and aphid quantity. Spectral index AI ARI DSSI1 DSSI2 PRI TVI NDWI GNDVI

2

R

R

−0.750 0.132 0.614 −0.229 −0.572 0.410 −0.525 0.707

0.580 0.017 0.378 0.053 0.328 0.223 0.276 0.500

p-value 0.000 0.443 0.000 0.179 0.000 0.000 0.001 0.000

Spectral index NBNDVI PSRI SIPI MCARI PhRI NPCI NRI RVSI

2

R

R

−0.554 0.324 0.588 0.640 0.137 0.416 0.350 0.780

0.307 0.105 0.345 0.410 0.019 0.173 0.123 0.608

p-value 0.307 0.105 0.345 0.410 0.019 0.173 0.123 0.608

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4. Monitoring with continuous wavelet algorithm In wavelet analysis, CWA based on continuous wavelet transform can decompose the entire spectral curve at continuous wavelengths and scales to quantitatively analyze some fine parts of spectral information (Addison, 2005; Bruce et al., 2000; Bruce and Li, 2001; Cheng et al., 2010; Cheng et al., 2011; Farge, 1992; Mallat, 1991). CWA has been paid more attention on some issues of hyperspectral information extraction. Figure 4.3 shows correlation coefficient matrix of 36 sample aphids and wavelet energy coefficients obtained after CWA. The determination coefficient R2 is within the range of 0–0.654. The red area in this figure is feature region with the highest correlation coefficient between wavelet energy coefficient and aphid quantity. Based on the sample size and characteristics of determination coefficient matrix, wavelet region of first 5% of R2 was selected as wavelet feature region of aphid, and R2 threshold was finally obtained as 0.48 as shown in figure 4.4. From determination coefficient matrix, it can be seen that, except for the first two scales, which are relatively unstable, wavelet feature region is relatively stable at other scales. Therefore, wavelet characteristic regions finally extracted are 484–552 nm, 609–619 nm, 637–651 nm, 718–770 nm, and 1673–1713 nm. These features bands are, respectively, located in the green band of strong reflection of chlorophyll and red band of strong absorption, and short-wave infrared with strong absorption of water (Pu et al., 2004). Table 4.7 shows wavelet band position, scale, and correlation coefficient of the best wavelet features. Among them, wavelet features WF4 (750 nm, Scale = 2) and WF5 (1690 nm, Scale = 6) have the best correlation with aphid quantity (R2 = 0.654 and R2 = 0.583). WF4 at red edge is the strong absorption region of chlorophyll, and WF5 in short-wave infrared region is a strong absorption region of water vapor, which is closely related to moisture content of leaf. WF1, WF2, and WF3 were also located in pigment absorption positions of visible light. Among selected five features, except WF3 has low correlation (R2 = 0.480), determination coefficients of the other wavelet features are higher than 0.5. Comparing with aphid spectral features extracted by other methods, the CWA method has a stronger ability to extract information infested by aphids. It may be because wavelet analysis considers the matching degree among local shapes of the whole spectral curve. The advantage of CWA is that it can achieve position and scale extraction at the same time, which can combine details of waveform and target characteristics

FIG. 4.3 – Correlation coefficient matrix and wavelet characteristics.

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FIG. 4.4 – Frequency distribution and threshold selection of R2. TAB. 4.7 – Position and scale parameters of wavelet feature set. Features WF1 WF2 WF3 WF4 WF5

Position Wavelength 491 617 639 750 1690

Scale 3 3 3 2 6

2

R

R

p-value

−0.759 0.725 0.693 0.808 −0.764

0.577 0.526 0.480 0.654 0.583

0.000 0.000 0.000 0.000 0.000

at different scales and positions. Thus, the extracted spectral information is also optimized to a certain extent to highlight the change of spectral position and intensity caused by aphids. Therefore, in research of aphid infested information extraction, it has stronger extraction capabilities and potentiality than other methods. 5. Monitoring with PLS To realize remote sensing monitoring of aphid quantity, in addition to extract spectral features which are sensitive to wheat aphid, it is necessary to select appropriate algorithms and use extracted sensitive spectral features to construct monitoring models. The current models are mainly divided into two categories, i.e., when the object is relatively continuous variable, the estimation model is established by selecting regression analysis, and when the object is discrete variable, it is often constructed by some non-linear algorithm’s models, such as discriminant analysis, principal component analysis, ANNs, and support vector machines. We use two indicators for evaluation of aphid quantity and aphid level, in which aphid quantity

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can be regarded as relatively continuous evaluation indicator, and aphid level can be considered as discrete evaluation indicator. We choose the regression analysis method to establish aphid quantity estimation model. We tried to construct a multiple variable aphid quantity estimation model using PLS. First, we need to select variables. Based on above research, a total of 35 remote sensing features have significant correlation with aphid quantity (p-value < 0.001). Considering requirements for model accuracy, we selected characteristic variable with determination coefficient R2 greater than 0.5 as selected variable for constructing a multiple variable model in this section (table 4.8). Multiple regression analysis is an important method to establish relationship between remote sensing data and field survey data. However, as the number of variables increases, it will be affected by collinearity between variables, which makes the model’s accuracy and stability lower. PLSR can eliminate the influence of collinearity, but too many variables will not only increase difficulty of calculation, but also cover physical meaning of variables, for which making model’s interpretability worse. Therefore, when modeling with the PLS method, we need not to involve all variables in establishment of the model, but extract some components that meet conditions to build the model with good stability and high reliability. Using VIP criteria brings not only fewer variables, but also higher model accuracy compared with the full model,which helps to improve estimation accuracy. We select several variables from 17 variables through variable projection importance criterion as selected variables for PLS.

TAB. 4.8 – Spectrsl features of model. Spectral features SDr/SDb SDr/SDy SDr/SDg SDy/SDb (SDr − SDb)/(SDr + SDb) (SDr − SDy)/(SDr + SDy) (SDr − SDg)/(SDr + SDg) (SDy − SDb)/(SDy + SDb) SDb SDg AI GNDVI RVSI F1 F2 F4 F5

R −0.778 0.728 −0.784 0.781 −0.778 0.727 −0.785 0.745 0.707 0.708 −0.750 0.707 0.780 −0.759 0.725 0.808 −0.764

R2 0.606 0.530 0.615 0.610 0.605 0.528 0.616 0.553 0.500 0.501 0.580 0.500 0.608 0.577 0.526 0.654 0.583

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Variable importance in projection is the importance and ability of independent variable xj in explaining dependent variable Y. The calculation formula is as follows. VIPj ¼

m pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X 2 p=Rd ðY : t1 ; t2 ; t3    tm Þ Rd ðY : th Þwhj

ð4:1Þ

h¼1

VIPj represents projection importance index of independent variable j, m is the number of components extracted from original variable, p is the number of independent variables, th is the principal component h, Rd (Y: th) is component explain ability of th to dependent variable Y. Rd (Y: t1, t2, t3, …, tm) is cumulative explain ability of components t1, t2, t3, …, tm to dependent variable Y, w 2hj is j component of axis wh, which is used to measure marginal contribution of xj to structural component th, that is, for any h = 1, 2, 3, …, m. p X

2 whj ¼ whT wh ¼ 1

ð4:2Þ

j¼1

According to VIP calculation formula, principal component must be determined firstly. The principle of determining number of principal components is not only to ensure that extracted components have the strongest ability to explain system, but also to overcome problem of multicollinearity between variables. We use the cross-validation method to determine principal component and calculate cross-validity of component th to variable y, referring to the method of PLS and selection rules of component numbers, calculating component numbers with Matlab. So we take components t1 of PLS to explain 99% of dependent variable information. Then, principal components are determined, using the 17 variables as independent variables to calculate VIP. The results are shown in figure 4.5. We use VIP indicator values to screen variables. If VIP value of independent variable is greater than 1, it indicates that the independent variable has a more important role in explaining dependent variable. If VIP range is between 0.5–1, the importance of explaining dependent variable is not very clear, and need to

FIG. 4.5 – VIP of features. Note: D1 in figure: (SDr − SDb)/(SDr + SDb), D2: (SDr − SDy)/(SDr + SDy), D3: (SDr − SDg)/(SDr + SDg); D4: (SDy − SDb)/(SDy + SDb), B1: SDr/SDb, B2: SDr/SDy, B3: SDr/SDg, B4: SDy/SDb.

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increase samples or judge according to other conditions. If VIP is less than 0.5, the importance of explaining dependent variable is basically meaningless. According to VIP distribution graph of spectral variables, VIP of all variables is greater than 0.5. Among them, wavelet feature variables WF1, WF4, and WF5 have the highest VIP values as 1.029, 1.061, and 1.067, respectively. This aspect illustrates that the CWA method has a better performance than other methods in extracting aphid information from the spectrum. WF1 (490 nm, scale = 23), WF4 (750 nm, scale = 22), and WF5 (1650 nm, scale = 26) can be used to better explain aphid quantity. This scale and location have the strongest information characterization ability on aphid. In addition, RVSI (VIP = 1.002) and SDy/SDb (VIP = 1.011) also have clear explanation for aphid levels. The high VIP indicates that red edge also has strong ability to aphid, and aphid also has a strong response on yellow and blue edges of spectrum, which means the strongest ability to explain aphid quantity. Therefore, VIP value of variables can be used to filter the variables that contribute more to the model. Usually, the independent variable with VIP greater than 1 is always selected to participate in the modeling. In this study, VIP values of RVSI, SDy / SDb, WF1, WF4, and WF5 greater than 1 were selected with the strongest explaining ability. Based on above analysis, RVSI, SDy/SDb, WF1, WF4, and WF5 are selected as independent variables, and aphid quantity Y is dependent variable for iterative calculation of PLS. Finally, the estimated model of aphid quantity based on PLS is as follows. Y ¼ 0.315  RVIS þ 0.198  (SDy/SDb) þ 0.197  WF1  0.287  WF4 þ 0.287  WF5 þ 139.025 ð4:3Þ Finally, inversion accuracy of the model is evaluated. We use RMSE of complex correlation coefficient to evaluate model. The better regression equation fits, the smaller residual squares sum, and R2 is closer to 1. RMSE calculation formula is as follows. sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n 1X RMSE ¼ ð4:4Þ ðb y  yi Þ2 n i¼1 i Among them, b y i and yi are estimated and measured values of the model for each sample, and n is the sample size. Figure 4.6 shows correlation between measured value of aphid quantity and estimated value of model. Its determination coefficient R2 is 0.677 and root mean square error is 18.277.

4.1.3

Monitoring at Canopy Scale

At canopy scale, canopy spectrum is a mixed spectrum of ground objects. The environment of field hyperspectral remote sensing is close to airborne remote sensing, but it overcomes limitations and difficulties in time, space, calibration, and spectral resolution of satellite remote sensing data. Therefore, it is of great

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FIG. 4.6 – Correlation between measured and estimated values of aphid quantity. significance to study canopy spectral response characteristics of aphid and construct an aphid monitoring model based on canopy spectrum, which can provide theoretical basis for satellite remote sensing and airborne remote sensing to monitor wheat aphid. 1. Spectral response at canopy scale The field survey found that the aphid outbreak period in study area in 2010 was from May 25 to June 10, which was during mid-late stage of wheat filling in study area. The measurement time of canopy spectrum in study area was June 7, 2010. Due to previous experiments-controlled aphid in study area with different drug concentrations, there were samples with different severity of aphid in study area to support the study. For design of experiment and data acquisition, refer to the third part. A total of 26 survey points for different severity of aphid were obtained in the experiment, including samples at levels 0–6. To directly and clearly reflect response characteristics of different levels on canopy spectrum, we preliminarily processed sample, and divided them into 4 levels, according to aphid level and sample sizes. For absence, slight, moderate and severe, specific treatment methods are as follows: (1) set level 0 as absence, (2) set level 1 and 2 as slight, (3) set level 3 and 4 as moderate, (4) set level 5 and 6 as severe. To understand response characteristics of wheat canopy spectrum after aphid infection, we first analyzed and compared wheat canopy spectrum of different levels. Figure 4.7 shows wheat canopy spectral curves of four levels. As the level increased, spectral reflectance in visible light and near-infrared has gradually decreased.

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FIG. 4.7 – Wheat canopy spectrum of different aphid levels. Previous studies have shown that in early stages of wheat infested by aphid, canopy spectral reflectance gradually rose with the increase of the number of aphid quantity in visible band (560–670 nm), and the canopy spectral reflectance gradually decreased with the increase of aphid quantity in near infrared band. Leaf response spectrum showed that in visible band, chlorophyll content of leaf would decrease, resulting absorption capacity of chlorophyll in blue and red bands is weakened and reflection capacity enhanced, especially for that the red band reflectance was increased. Through field investigation and analysis, we initially believed that it was due to that honeydew secreted by aphids may be attached to leaf surface. In mid-late stages of wheat filling, the number of aphids increased rapidly in a short period of time, and honeydew secreted by aphid attached to leaf surface, which affected healthy photosynthesis and respiration of leaf, and affected by environment caused leaf to appear moldy black, during flourishing period. If control measures were not conducted on time, as aphid quantity increased, mold black spots on leaf would become much severer. The light absorption effect is greater than light reflection effect caused by changing in chlorophyll content. Leaves are the most important component in field canopy observation, so the darkening of leaf appearance would eventually lead to corresponding canopy change. Therefore, as the level of infested increases, spectral reflectance would gradually decreases. This conclusion also showed that spectral response characteristic of aphid during its peak stage in visible band were different from early stages, and monitoring model in early stage may not be suitable for monitoring aphid in peak stage.

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To quantitatively analyze response characteristics and mechanism of different aphid levels on canopy spectrum, we calculated the difference and change rate of canopy spectrum of absence and severe wheat, as shown in figure 4.8. It can be seen from figure 4.8 that canopy spectral reflectance of wheat infested by aphid was lower than healthy wheat, and changes in near-infrared band were the biggest, followed by short-wave infrared. The visible band changes were relatively small. For the change rate curve, the biggest change rate is in near-infrared band, followed by visible light band, and the change rate in short-wave infrared band was relatively small. The reasons for the difference in the near-infrared band mainly lies in two aspects. On the one hand, leaf is the largest component in canopy observation, leaf reflectance mainly depends on cell structure in near-infrared band. Aphid destroys leaf cell structure by absorbing juice, which leads to reflectance of wheat leaf infested by aphid lower than healthy leaf. On the other hand, according to literatures and field experiments, it can be known that aphids cause leaf to curl, and curling inevitably cause canopy structure to change, then LAI will decrease. Therefore, reflectance in near-infrared band will decrease with level of aphid increasing.

FIG. 4.8 – Differences in canopy spectrum between wheat aphid infested leaf and healthy leaf. 2. Construction of spectral index Correlation analysis of aphid level and reflectance in band range of 350–2500 nm is shown in figure 4.9. Most of visible, near infrared and short-wave infrared bands have extremely significant negative correlation. Among them, near-infrared band had the strongest correlation, which was consistent with above conclusion. In visible region (350–740 nm), due to honeydew secreted by aphid, canopy reflectance of

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FIG. 4.9 – Correlation analysis between aphid level and spectral reflectance.

wheat infested by aphid was lower than healthy wheat, with the most correlated band at 551 nm (R = −0.74). In the range of (760–1300 nm), spectral reflectance had significant negative correlation, where the highest correlation was 823 nm (R = −0.87). In short-wave infrared band (1550–1750 nm), canopy reflectance of wheat canopy infested by aphid was lower than healthy wheat, and spectral reflectance and aphid level showed significant negative correlation, with the highest correlation band at 1654 nm (R = −0.67). The above analysis showed the wheat aphid level response to visible, near infrared and shortwave infrared bands. If only sensitive bands with the best correlation in near-infrared band were selected to establish a single factor inversion model, the interpretability of the model would be weakened, so do its stability and reliability. The three most relevant bands, i.e., 551 nm, 823 nm, and 1654 nm, in visible, near-infrared, and short-wave infrared regions were selected, and contribution of change rate of each band was used as weight coefficient to define the spectral index aphid damage spectral index (ADSI). We used obtained spectral data to construct hyperspectral index of aphid, and the hyperspectral index could be obtained in each sample. Then, the statistical correlation analysis was performed between the indices and aphid levels to establish remote sensing inversion model. The statistical analysis results are shown in figure 4.10. It can be found that there was an extremely significant correlation between hyperspectral index and aphid level (R2 = 0.84, n = 26). 3. Aphid level inversion The correlation fitting model is proposed after examining measured spectral characteristics of vegetation and non-vegetation, it is used for vegetation target classification, identification, and related information extraction. Both theoretical

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FIG. 4.10 – Correlation between ADHI and aphid levels. derivation and experimental verification have proved that this model has many advantages. This method provides another classification method different from conventional generalized distance method and generalized angle method. It can even do identification or unsupervised classification. According to previous research and definition of correlation fitting analysis method, the main concluions are as follows. (1) If two spectral curves are identical, the correlation fitting curve must be a straight line with a slope of 1 and an intercept of 0 (correlation straight line) in the correlation graph. (2) The spectral correlation curve of same object should be concentrated around y = x, and the correlation curve of different spectrum will deviate from the straight-line y = x. The greater difference, the greater deviation. If correlation curves of different objects are coincident, they should be indivisible in spectrum. (3) For same object’s spectrum, its linear influence factor independent of wavelength only causes change of slope and intercept of relevant straight line, but correlation curve is still a straight line. The change of slope is only caused by multiplicative factor, and additive factor only causes change of intercept. After vegetation is infested to different stresses, it will cause corresponding changes in visible and near-infrared. According to characteristics of wheat canopy spectra of different aphid levels, there are common similarities in shape of wheat canopy spectra with different levels, and intensity change is regular. We tried to use the correlation fitting analysis method to quantitatively discuss regular of spectral changes in different levels. Figure 4.11 shows correlation fitting line that canopy spectral reflectance of different level were relative to healthy wheat at (a) 400–1000 nm, (b) 400–950 nm, and (c) 400–900 nm. No matter which band, the starting point was from the origin, and the end point of fitted line appeared when

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FIG. 4.11 – Corresponding fitting lines of canopy spectra for different aphid level. reflectance was between 35% and 40%. According to reflectance region of canopy spectra in near-infrared and visible light region, the study found that 400–900 nm was more suitable for correlation analysis than other two regions. That is, when the selected band region was larger than this region, the data created redundancy. From figure 4.11c, we have the following conclusions. (1) The relevant fitting curves of aphid with different levels are basically straight lines (R2 > 0.99), but their slopes are significantly different. This conclusion is consistent with previous research conclusions. (2) At both ends of the relevant straight line, data points are dense, the low-end dense area corresponds to blue and red reflectance region, and high-end dense area corresponds to near-infrared reflectance region. (3) With the increase of wheat aphid level, slope of fitted straight line of correlation curve gradually decreases. The deviation that correlation curve of different aphid level from straight line y = x reflects different effects of aphid level on wheat. This effect is represented by slope of fitted curve. From the above analysis and conclusions, it is known that with the increase of aphid level, fitted curve’s slope of different level relative to basic spectrum shows a decreasing trend. Therefore, fitted curve’s slope can be established by the estimated model of aphid level. Screening of band region will affect slope of fitted line to certain extent. Although the region we initially selected is 400–900 nm in previous researches, it remains to be further studied whether it is optimal band region for correlation fitting method.

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FIG. 4.12 – Distribution of linear slope and determination coefficient (R2) of aphid level.

In 400–900 nm, starting with 400 nm, every 5 nm was taken as an selected band range. Matlab was used to calculate slope of curve fitting line of 25 sample points relative to basis spectrum as well. Each sample would generate 100 band regions and corresponding 100 slopes (k). A total of 100 groups of slopes k would be obtained form 25 samples, through correlation analysis with slopes of each group of samples and aphid level. We can get determination coefficient (R2). Figure 4.12 is distribution graph of determination coefficient (R2) between slope of fitting line and aphid level which obtained by using the correlation curve fitting method with starting wavelength of 400 nm and every 5 nm as a selected band region. It can be seen from figure 4.12 that determination coefficient varies with termination band. When termination band is 810 nm, determination coefficient reaches maximum (R2 = 0.899). When band is increased, determination coefficient changes smoothly and does not increase. That is, when band is selected from 400 to 810 nm, the correlation is maximum, which can be used to construct the wheat aphid level model (figure 4.13). Therefore, in flourishing period of wheat aphid, using the correlation fitting analysis method can extract aphid level of wheat canopy. The slope characteristics can be extracted by the correlation fitting analysis method to invert aphid level.

4.2

Rice Leaf Roller Monitoring

Rice leaf roller is one of the main pests for rice in China. Infection at seedling stage affects healthy growth. Infection at tillering stage to jointing stage will result delay growth. Infection at heading stage affects flowering and fruiting. In Section 4.2.1, we mainly introduce spectral characteristic analysis. In Section 4.2.2, we mainly introduce remote sensing monitoring at canopy scale.

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FIG. 4.13 – Correlation between slope of correlation fitting straight line and aphid level.

4.2.1

Spectral Response

When crop is stressed by pests, it may bring leaf spots, rolled leaf, fallen leaf, etc., leading to changes in shape of canopy. Physiological changes are manifested in destruction of chlorophyll tissue, photosynthesis, nutrient water absorption, transformation and other functional decline, affecting healthy growth, which eventually lead to crop yield loss, quality loss and even produce toxins that endanger human health. In addition, a large amount of pesticides sprayed to prevent pests will also damage ecological environment (Gong et al., 2002). In figure 4.14, the comparison of rice SPAD and spectral reflectance at four levels of rice leaf rollers (absence, slight, moderate, severe) are shown. It can be seen that with the increase of severity of pest, SPAD shows gradual decline trend, and decline from moderate to severe is significantly higher than other levels. Observing four levels of spectral reflectance (figure 4.14b), it can be clearly found that the shape of rice canopy spectral curve of four levels is similar, all of which shows reflectance characteristics of typical vegetation, i.e., green peak, red valley, and high near infrared reflectance. From another aspect, it can also be indirectly reflected that rice in this growth period does not suffer from severe rice leaf rollers. Otherwise, reflectance of rice canopy under severe stress should show large difference from healthy rice reflectance or be close to curve characteristics of soil. Although reflectance curves of different levels are similar in shape, they can still distinguish level in near-infrared spectra. It is difficult to distinguish different levels in visible and short-wave infrared regions. Comparing with visible and near-infrared regions, because influence of water vapor absorption, spectral curve in shortwave infrared range is greatly jittery and shows severe jagged shape. Therefore, smoothing

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FIG. 4.14 – SPAD values (a) and spectral reflectance (b) of rice chlorophyll infested by rice leaf roller.

processing is required to eliminate effects of outliers when analyzing spectral characteristics of short-wave infrared region. In shortwave infrared region, there are three water vapor absorption ranges, i.e., 1340–1450 nm, 1780–2000 nm, and 2350–2500 nm. In this section, we remove three reflectance regions. Figure 4.15 shows spectral characteristics of different pest levels. Compared with figure 4.15a and b, it can be found that after data smoothing, spectral curve has been improved, especially in short-wave infrared region of 2000–2350 nm. Figure 4.15a is normalized spectral curve of data. Compared with figure 4.15b, the overall shape of the curve is not changed, but flattened and pulled up, especially at visible and near infrared of 350–1350 nm. The most obvious difference is that the spectral curve of healthy rice is clearly separated from other three levels, especially in near-infrared and shortwave infrared spectral regions. Comparing reflectance curves in visible and near-infrared spectral regions of figure 4.15c and d, it is found that they show opposite trends. In figure 4.15c, the reflectance increases with the increase of severity; on the contrary, figure 4.15d shows opposite trend.

4.2.2

Monitoring at Canopy Scale

1. Monitoring with pest index To highlight spectral differences between healthy rice and rice infested by pest, we plot difference and change rate curves of rice spectral reflectance with two levels, i.e., absence and severe. Observing spectral difference curve, we can find that the value in visible light region of 350–691 nm is negative, indicating that reflectance of healthy rice in this region is smaller than rice infested by pests. In contrast, in near-infrared range of 692–1349 nm, spectral differences are positive and maximum value is 8.56 at 1104 nm, indicating that reflectance of healthy rice is greater than

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FIG. 4.15 – Comparison of spectral characteristics of different pest levels: (a) standardized reflectance curve; (b) curve after the original spectrum is smoothed; (c) curve after the visible light band is smoothed; (d) near-infrared band smoothed curve.

rice infested by pests. In shortwave infrared region from 1451 nm to 1779 nm and 2001 nm to 2349 nm, the trend of spectral difference curve is similar to near-infrared region. For change rate curve, maximum value was 18.2% at 1341 nm in near-infrared region, followed by 14.34% at 2333 nm in shortwave infrared region, and minimum value was −0.37% at 558 nm in visible light region (figure 4.16). To assess pest severity, it is necessary to use hyperspectral reflectance to construct inversion index. We collected 18 rice samples include pest levels and spectral reflectance in field investigation (3 absence samples, 6 slight samples, 6 moderate samples, and 3 severe samples). Correlation analysis was performed between pest levels and reflectance, and the correlation analysis results are shown in figure 4.16b. From correlation results, we can find that it shows significant negative correlation in region of 400 nm to 1300 nm. In entire correlation curve, there are three extreme points, i.e., 424 nm (R = −0.802), 758 nm (R = −0.916), and 1141 nm (R = −0.895). The curve is divided into three sections as 400–720 nm, 720–1115 nm and 1115–1300 nm. Therefore, by using three extreme points, we can construct hyperspectral pest index for rice leaf folder (HIIRLF).

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FIG. 4.16 – (a) Difference and change rate curve of health and pest infection; (b) change curve with wavelength of reflectance and pest. R424normal  R424pest R758normal  R758pest þ 0:25  þ 0:35 R424normal R758normal R1141normal  R1141pest  R1141normal ð4:5Þ

HIIRLF ¼ 0:40 

In formula, R424normal, R758normal, and R1141normal represent spectral reflectance of healthy rice canopy at 424 nm, 758 nm, and 1141 nm, respectively. R424pest, R758pest, and R1141pest represent spectral reflectance of severe rice canopy

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at 424 nm, 758 nm, 1141 nm, respectively. According to the contribution rate of three bands in change rate curve (figure 4.17a), we can get corresponding weight coefficients. To verify efficiency of HIIRLF, we compared linear correlation between HIIRLF, measured chlorophyll SPAD and pest grade with 18 samples. Then we obtained linear regression equation and determination coefficient R2 (figure 4.17).

FIG. 4.17 – Linear equations and determination coefficients of chlorophyll and HIIRLF indices reflecting pest severity.

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2. Monitoring with SVM Based on analysis of hyperspectral characteristics of healthy rice leaf and rice leaf infested by rice leaf roller, we used continuum removal to extract spectral absorption characteristic parameters as input variables to construct a rice leaf roller monitoring model with SVM. Identification of rice leaf roller using hyperspectral data is mainly based on selection of spectral absorption characteristic parameters. Spectral absorption characteristic parameters mainly include wave depth and slope of absorption bands. Spectral absorption characteristic parameters can be obtained by continuum removal. According to spectral characteristics analysis, we use continuum removal with bands near chlorophyll absorption peaks at 470 nm and 670 nm. The specific parameters of continuum removal are shown in table 4.9. With continuum removal, reflectance spectra of rice leaf in bands of 430–530 nm and 560–730 nm were normalized to same baseline, and it expanded characteristics of chlorophyll blue and red absorption valley. The data in table 4.10 show that the absorption depth of rice leaf infested by rice leaf rollers in blue and red absorption valley are less than healthy leaf, which are 29.3% and 51.1% of healthy leaf, respectively. The slope of absorption characteristic parameter between 430 nm and 530 nm has no big changes. However, near-infrared steep slope effect of rice leaf infested by the rice leaf roller is weak, and the slope of absorption band of Chla centered on 670 nm is 51.7% of healthy leaf. The selected spectral absorption Characteristic parameters are used for hyperspectral identification of rice leaf roller, avoiding computational difficulties caused by amount of hyperspectral data. We used spectral data of 108 samples to build the model, where 70 samples were used as training set and 38 samples as test set. Based on analysis of spectral characteristics after continuum removal process, we selected four feature values of absorption peak band depth BD1 and BD2 and slopeed K1 and K2, then obtained 70 training matrixes and 38 test matrixes. We normalized input vectors to the interval of [0,1] to reduce computational complexity. Cross-validation was used to filter parameters, using the optimal parameters to establish SVM model. The classification result is shown in table 4.11. The result shows that SVM can accurately identify test samples, which indicates that the model for identifying rice leaf roller using hyperspectral data has a good classification effect. This chapter constructs crop pest monitoring models, including wheat aphid monitoring model and rice leaf roller monitoring model. Pest sensitive spectral features are extracted and pest monitoring models are constructed based on non-imaging hyperspectral data. We analyze non-imaging remote sensing monitoring mechanisms of crop pests, and use vegetation index, correlation analysis, TAB. 4.9 – Parameter settings for continuous system removal/nm. Characteristics absorption peak 470 670

Start of continuum 430 560

Center of wave depth 490 670

End of continuum 530 730

134

Parameter statistics

Min Max Std Mean

430–530 nm Wave depth Healthy leaf 0.4416 0.5484 0.0274 0.4881

Infested leaf 0.02795 0.35672 0.06475 0.14299

560–730 nm Slope

Healthy leaf 0.00078 0.00123 0.00011 0.00097

Wave depth Infested leaf 0.00079 0.00120 0.00010 0.00102

Healthy leaf 0.84264 0.88430 0.00843 0.85998

Infested leaf 0.21312 0.73508 0.11191 0.43916

Slope Healthy leaf 0.00123 0.00169 0.00009 0.00152

Infested leaf 0.00047 0.00109 0.00014 0.00077

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

TAB. 4.10 – Statistical analysis of spectral absorption characteristic parameters of rice leaf.

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135 TAB. 4.11 – Classification results of SVM. SVM

Healthy Pest Total

Healthy 19 0 19

Pest 0 19 19

Classification accuracy Total 19 19 38

100 100 100

continuous wavelet algorithm to extract sensitive remote sensing features of pests. Then, we use PLS and SVM to construct the relationship between severity and sensitive remote sensing features. The results illustrate that hyperspectral remote sensing technology has potential in crop pests monitoring. The study in this chapter can provide theoretical basis and technical support for large area crop pests monitoring with imaging remote sensing technology.

References Addison P. S. (2005) Wavelet transforms and the ECG: a review. Physiol. Meas. 26, 155. Baret F., Vanderbilt V. C., Steven M. D., et al. (1994) Use of spectral analogy to evaluate canopy reflectance sensitivity to leaf optical properties. Remote Sens. Environ. 48, 253. Broge N. H., Mortensen J. V. (2002) Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sens. Environ. 81, 45. Bruce L. M., Li J. (2001) Wavelets for computationally efficient hyperspectral derivative analysis. IEEE Trans. Geosci. Remote Sens. 39, 1540. Bruce L. M., Li J., Huang Y. (2000) Automated detection of subpixel hyperspectral targets with adaptive multichannel discrete wavelet transform. IEEE Trans. Geosci. Remote Sens. 40, 977. Cheng T., Rivard B., Sánchez-Azofeifa A. (2011) Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens. Environ. 115, 659. Cheng T., Rivard B., Sánchez-Azofeifa A., et al. (2010) Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sens. Environ. 114, 899. Farge M. (1992) Wavelet transforms and their applications to turbulence. Annu. Rev. Fluid Mech. 24, 395. Filella I., Serrano L., Serra J., et al. (1995) Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci. 35, 1400. Gamon J. A., Serrano L., Surfus J. S. (1997) The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types and nutrient levels. Oecologia. 112, 492. Gitelson A. A., Merzlyak M. N., Chivkunova O. B. (2001) Optical properties and nondestructive estimation of anthocyanin content in plant leaves. J. Photochem. Photobiol. 74, 38. Gong P., Pu R., Heald R. C. (2002) Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia. Int. J. Remote Sens. 23, 1827. Mallat S. (1991) Zero-crossings of a wavelet transform. IEEE Trans. Inf. Theory. 37, 1019. Merton R. N. (1998) Monitoring community hysteresis using spectral shift analysis and the red-edge vegetation stress index. Proceedings of the Seventh Annual JPL Airborne Earth Science Workshop. pp. 12–16. Miller J. R., Wu J., Boyer M. G., et al. (1991) Season patterns in leaf reflectance red edge characteristics. Int. J. Remote Sens. 12, 1509.

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Mirik M., Michels G. J., Kassymzhanova-Mirik S., et al. (2006) Using digital image analysis and spectral reflectance data to quantify damage by greenbug (Hemitera: Aphididae) in winter wheat. Comput. Electron. Agric. 51, 86. Mirik M., Michels Jr. G. J., Mirik S. K., et al. (2007) Spectral sensing of aphid (Hemiptera: Aphididae) density using field spectrometry and radiometry. Turkish J. Agric. For. 30, 421. Pu R., Foschi L., Gong P. (2004) Spectral feature analysis for assessment of water status and health level in coast live oak (Quercus agrifolia) leaves. Int. J. Remote Sens. 25, 4267. Pu R., Ge S., Kelly N. M., et al. (2003) Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves. Int. J. Remote Sens. 24, 1799. Pu R. L., Gong P. (2000) Hyperspectral remote sensing and its applications. Higher Education Press, Beijing. Thenkabail P. S., Smith R. B., Pauw E. D. (2000) Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ. 71, 158.

Chapter 5 Crop Pest and Disease Differentiation The previous chapters focused on the application of remote sensing technology in pest and disease monitoring, showing the potential of remote sensing technology in pests and diseases monitoring. Crop may suffer a wide variety of stresses, such as pests, diseases, nutrient in farmland. Since most stresses cause external morphological changes, such as leaf green loss, plant wilting, plant yellowing, stresses could be easily confused (Sankaran et al., 2010). Moreover, the prevention and control of crop pests and diseases are mainly through the application of pesticides and fungicides. In lack of accurate information of pest/disease types, damaged location and severity, excessive and non-accurate applications of pesticides and fungicides are normally conducted (Ahern, 1988). For which, it not only could not control the pest/disease spread, but also bring drug damage, soil and groundwater pollution etc. So, stress differentiation is very important for improving the disaster especially the pest/disease monitoring and prevention. Nutrient stress, including excessive and insufficient irrigation and fertilization, is a common type of stresses in field management. For crop pests, diseases and nutrient stresses, the prevention strategies are different. If spraying nutrient-stressed crops with pesticides or applying water and fertilizer to pest/disease infested crops, it could not alleviate symptoms, and even result in more serious consequences. Therefore, how to differentiate crop pests and diseases from nutrient stresses becomes an important issue in automation and modernization management. This chapter mainly introduces the differentiation of crop pests and diseases and nutrient stresses as well as the differentiation of multiple crop pests and diseases.

5.1 5.1.1

Wheat Yellow Rust and Nutrient Stress Spectral Response

The spectral features used in this section include seven commonly used vegetation indices. These indices are sensitive to pigment changes, plant water conditions, canopy architecture and light efficiency and photosynthetic activity, respectively DOI: 10.1051/978-2-7598-2659-9.c005 © Science Press, EDP Sciences, 2022

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(Kim et al., 2006; Mahlein et al., 2013; Merzlyak et al., 1999; Naidu et al., 2009). To study the variety of commonly used spectral features in depth, a series of spectral indicators through derivative transformation, and continuous spectrum removal transformation were also considered. Finally, total 38 spectral features were selected, including 9 derivative transformed spectral variables, 9 continuous removal transformed spectral features, and GI, NDVI, NBNDVI, NRI, PRI, SIPI, PSRI, PhRI, ARI, TVI, CARI, MCARI, WI in VI-Based Variables in tables 1.1 and 4.5, and others shown in table 5.1. Correlation analysis was used to study the sensitivity of the 38 selected features (table 5.2) with DI, M, and N concentrations as sensitive variables on five measurement dates to all forms of stresses. For characteristic that are always correlated to yellow rust (significantly related to yellow rust in most of the growth period), the difference among different stresses and healthy treatments was investigated by independent t-test. The differences between different nutritional stresses and different levels of yellow rust were also investigated. To facilitate analysis, in addition to healthy samples, each stress was set two levels using their corresponding in indicators. For yellow rust, the yellow rust class 1 was sorted with the DI from 0 to 0.3 (including 0.3), and the yellow rust class 2 was sorted with the DI from 0.3 to 1.0. For nitrogen and water stresses, the plot wheat samples treated with the recommended amount of nutrient were taken as the healthy group. The thresholds for plant water content (PWC) concentrations an N concentration at slight and severe stressed levels were defined according to the mean and standard deviation of the healthy group. The samples with nutrient characteristics ranging average normal −3 × SD normal to average normal −SD normal were divided into minor stress group (water stress class 1, nitrogen stress class 1), and samples with nutrient properties under average normal −3 × SD normal were divided into severe stress group (water stress class 2, nitrogen stress class 2). Additionally, some wheat samples with nutrient properties exceeded the defined limit, were not included because they did not exhibit stress performance even when stress treatment was performed. TAB. 5.1 – Spectral features. Features VI-based variables Modified Simple Ratio, MSR Transformed Chlorophyll Absorption in Reflectance Index, TCARI Normalized Pigment Chlorophyll Index, NPCI Disease Stress Water Index, DSWI Moisture Stress Index, MSI Shortwave Infrared Water Stress Index, SIWSI

Descriptions (R800/R670 − 1)/(R800/R670 + 1)1/2 3 × ((R700 − R670) − 0.2 × (R700 − R550) × (R700/R670)) (R680 − R430)/(R680 + R430) (R802 + R547)/(R1657 + R682) R1600/R819 (R860 − R1640)/(R860 + R1640)

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139

TAB. 5.2 – Sensibilities of spectral features for yellow rust at five growing periods. Sensibilities to yellow rust on days after sowing Spectral features

207 days after sowing

DEP550–770 AREA550–770 WID550–770 AREA920–1120 Db λb SDb Dy λy SDy Dr λr SDr MSR NDVI NBNDVI PRI TCARI SIPI PSRI PhRI NPCI ARI TVI CARI RVSI MCARI

216 days after sowing

c

225 days after sowing c c b c c a

230 days after sowing

233 days after sowing

c

c b

b c a

c

c c

b

c c c c c c

c a c c c c a c c a b b

c c c c c

c b c a

a a a

b

c c b b c

a means the significance level of p-value lower than 0.001, b means the significance level of p-value lower than 0.01, and c means the significance level of p-value lower than 0.05.

5.1.2

Rust and Nutrient Stress Differentiation

In total, 36 features are sensitive to water stress at least in one growing period, and 28 features for yellow rust, 18 features for nutrient stress. Table 5.2 summarizes the response characteristics of all features to yellow rust in 207 days,

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216 days, 225 days, 230 days and 233 days after sowing. Among them, the response characteristics began to show correlationship with yellow rust in ‘216 days after sowing’, which matched the booting stage, while the strongest spectral response to yellow rust happened in ‘225 days after sowing’ matching flowering stage. For DEP550–770, AREA920–1120, SDr, NDVI, SIPI, and RVSI, they were sensitive to yellow rust in 225 days after sowing. The results are consistent with the development of yellow rust, which has the most obvious symptoms occurring during flowering stage. However, in order to obtain appropriate indicator for yellow rust investigation, the features must be consistent in the response to yellow rust in the key periods. Thus, among these five growing periods, four features, i.e., PRI, PhRI, NPCI, and ARI, sensitive to yellow rust were selected as the disease detection indicators. In particular, the four indicators were evaluated with Independent t-test, as shown in table 5.3. The validation with data in 216 days after sowing, 225 days after sowing, 230 days after sowing, and 233 days after sowing were conducted. Since the slight infestation of disease appeared in the early period, then developed into severe infestation in the later period, no samples belonged to yellow rust class 2 in 216 days after sowing, and yellow rust class 1 in 233 days after sowing. Table 5.4 summarizes the response of the four indicators to all stresses. Both the Independent t-test result and the feature changing trend were provided. As shown in table 5.4, the four indicators correlated differently to the stresses during the four growing periods. Compared to healthy samples, more vegetation indices displayed stronger responses to yellow rust class 1 in 216 days after sowing than in 225 days and 230 days after sowing. For yellow rust class 2, all four indicators displayed obvious responses in 225 days and 233 days after sowing. Four indicators showed high correlations to water stress class 2 in all growing periods, except PhRI in 225 days and 233 days after sowing. Compared with water stress class 2, the four indicators showed fewer responses to water stress class 1, nitrogen stress class 1, and nitrogen stress class 2. Except the comparisons with healthy samples, table 5.4 also illustrates the spectral separability among the three stresses. Generally, the spectrum changes between water-stress and yellow rust were larger than that between nitrogen-stress and yellow rust. The spectral change between yellow rust class 2 and all nutrient-stress levels was stronger than that between yellow rust class 1 and nutrient-stress. According to the response to the four indicators, it showed that ARI, PRI, and NPCI were sensitive to both yellow rust and nutrient-stress to a certain extent (compared to healthy wheat samples). However, PhRI was the only vegetation index that was sensitive to all three stresses in 225 days and 233 days after sowing. The difference of PhRI between different disease classes and nutrient-stress classes was significant, which further demonstrated the discriminability of the index.

Experiments

Disease

Stressed levels Healthy Yellow rust class 1 Yellow rust class 2 Healthy

Nutrient stresses

Nitrogen stress class 1 Nitrogen stress class 2 Water stress class 1 Water stress class 2

216 days after sowing

225 days after sowing

230 days after sowing

233 days after sowing

Value range DI < 0.05

n 21

Value range DI < 0.05

n 9

Value range DI < 0.05

n 9

Value range DI < 0.05

n 9

DI:0.05–0.3

15

DI: 0.05–0.3

18

DI: 0.05–0.3

6

DI: 0.05–0.3

0

DI > 0.3

0

DI > 0.3

9

DI > 0.3

21

DI > 0.3

27

Nitrogen%: 3.80 ± 0.09 Water%: 84.67 ± 0.72 Nitrogen%: 3.51–3.70 Nitrogen % < 3.51 Water%: 82.4–83.94 Water% < 82.49

9

8 29 10 26

Nitrogen%: 2.56 ± 0.11 Water%: 75.04 ± 0.94 Nitrogen%: 2.24–2.45 Nitrogen % < 2.24 Water%: 72.22–74.10 Water% < 72.22

9

11 15 20 6

Nitrogen%: 2.26 ± 0.04 Water %:66.76 ± 1.15 Nitrogen%: 2.14–2.22 Nitrogen % < 2.14 Water%: 63.32–65.61 Water% < 63.32

9

5 35 14 19

Nitrogen%: 1.57 ± 0.07 Water%: 68.97 ± 1.82 Nitrogen%: 1.34–1.49 Nitrogen % < 1.34 Water%: 63.52–67.16 Water% < 63.52

Crop Pest and Disease Differentiation

TAB. 5.3 – Summary for different classes of different stresses.

9

7 34 20 18

141

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

TAB. 5.4 – Independent t-tests for the chosen vegetation indices in different days after sowing. Water stress Water stress Nitrogen Nitrogen class 1 class 2 stress class 1 stress class 2 (a) 216 days after sowing PRI + +a + +b b PHRI + + + + Healthy −c −a NPCI − −a ARI − −a −b −b b b Yellow PRI + − − − −c PHRI −b +b +a +a +a rust class 1 NPCI −c +c − + + +c + + + ARI −c (b) 225 days after sowing + +c PRI + +b PHRI + + + + Healthy − − NPCI − −a − − ARI − −b Yellow PRI + − + − + +b +a +b +a rust class 1 PHRI −c a NPCI + − − − − − − ARI − + −b −a −a −a −a Yellow PRI +a PHRI −a +a +a +a +a rust class 2 c c NPCI − + − + + +c − + + ARI −c (c) 230 days after sowing PRI + +a +c + PHRI + + + + Healthy NPCI − −b − − − − ARI − −b + + Yellow PRI − + +c − − PHRI + − −b rust class 1 − − NPCI + − −b − −a ARI + − −b Yellow PRI +a −a −b −b −a PHRI −a +a +a +a + rust class 2 +b + + + NPCI −c +a + + + ARI −c (d) 233 days after sowing PRI + +a + +c PHRI + + + + Healthy − −c NPCI − −a ARI − −a − − a a − −c −a −a Yellow PRI + PHRI −b +a +a +a +a rust class 2 b a b NPCI − + − + + +a + +b +c ARI −a + represents the mean value of spectral feature for both disease and nutrient-stress samples higher than that of healthy samples, or the mean value of spectral feature for both disease and nutrient-stress samples lower than that of healthy samples; − represents the mean value of spectral feature for disease samples higher than value of healthy samples while the mean value of indicator for nutrient influenced samples lower than value of healthy samples and vice versa; a means the significance level of p-value lower than 0.001; b means the significance level of p-value lower than 0.01; and c means the significance level of p-value lower than 0.05. Stresses

Vegetation indices

Healthy

Crop Pest and Disease Differentiation

5.2

143

Wheat Disease and Nutrient Stress

Multiple wheat diseases and nutrient stress are main causes of wheat yield loss. Therefore, the quantitative identification of multiple diseases and nutrient stress has guiding significance for the application of reasonable fungicides and fertilizers. Powdery mildew and yellow rust are the two most common wheat diseases in China. The performance of CWA in identifying yellow rust, powdery mildew, and nutrient stress by using hyperspectral data are explored in this section.

5.2.1

Spectral Response

Aiming to differentiate powdery mildew, yellow rust and nutrient stress, 16 vegetation indices (CARI, MCARI, TCARI, SIPI, NDVI, GI, PRI, NDWI, OSAVI in table 1.1, SAVI in table 2.1, NRI, ARI, RVSI, PhRI in table 4.5, TCARI in table 5.1 and others in table 5.5), associated with chlorophyll content, water content, canopy structure, were selected (Penuelas et al., 1994, 1995, 1997). Among them, CARI and TCARI are closely correlated to chlorophyll content, SAVI and OSAVI can reduce the NDVI sensitivity to the soil background (Rondeaux et al., 1996), and PhRI and PRI can efficiently assess the solar utilization efficiency of the crop development. Figure 5.1 shows the sensitive spectral bands. The results illustrated that visible region included the most sensitive spectral bands, and the wavelengths in the ranges from 615 to 521 nm, 693 to 696 nm were sensitive for all the three stresses. Then, six indices, i.e., ARI, MCARI, NPCI, PhRI, PRI, and TCARI, were selected. ARI, NPCI, PhRI and PRI were always highly related to yellow rust during the entire winter wheat growing periods (table 5.6). Both ARI and PRI demonstrated potentials in crop disease monitoring. PhRI is potential in crop disease infestations and abiotic stresses differentiation due to its specific disease response. Additionally, the leaf chlorophyll content changes caused by crop disease damage and nutrient stress can be efficiently captured by MCARI and TCARI (Daughtry et al., 2000). Compared with Fourier transform process, the continuous wavelet transform contains both the local time domain and frequency domain. Mexican Hat was chosen as the mother wavelet’s basis due to its similar shape with the absorption characteristics (Pu et al., 2003, 2004). The raw spectral reflectance can be transformed to several continuous wavelet signals to highlight disease spectral subtle information. The continuous wavelet transform converted one-dimensional reflectance into two-dimensional wavelet signal scalogram to show location and scale dimensions. To facilitate the calculation and not to influence the CWA precision, wavelet coefficient was adopted. Scale 2n (n = 1, 2,…, 10) was the wavelet coefficient decomposition.

TAB. 5.5 – Vegetation index summary for differentiating powdery mildew, yellow rust, and nutrient stress. Vegetation index Normalized Pigment Chlorophyll Index, NPCI

Definitions (R680 − R430)/(R680 + R430)

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FIG. 5.1 – Spectral sensitivities to different stresses combinations.

TAB. 5.6 – Index selection for the identification of powdery mildew, yellow rust, and nutrient stress. VI NRI NPCI ARI CARI MCARI TCARI SIPI RVSI NDVI GI PhRI PRI WI NDWI SAVI OSAVI

Powdery mildew & nutrient stress ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Yellow rust & powdery mil dew ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Nutrient stress & yellow rust ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓

To reduce the redundancy among the used three type features, a feature selection flow is needed to determine the most prominent feature used to differentiate different crop stresses (Kira and Rendell, 1992). To receive bands and indices with differences between powdery mildew & yellow rust, yellow rust & nutrient stress, nutrient stress & powdery mildew, Independent t-test analysis was conducted. Both Independent t-test and correlation analysis were used to identify correlated (figure 5.2a–c) and discrepant features (figure 5.2d–f). Furthermore, the Independent t-test result and correlation analysis result showed the difference and correlation significance between stresses. The intersections as shown in (figure 5.2g) were finally taken as the optimal spectral bands or vegetation indices. For different

Crop Pest and Disease Differentiation

145

FIG. 5.2 – Optimal wavelet feature selection for differentiation of different stresses. stresses, the optimal wavelet features were not only significantly correlated with the stresses, but also independent to each other (figure 5.2g). As shown in figure 5.2g, a series of significant regions on p-value maps were formed in p-value maps by the wavelet feature selection based on overlapping processes. Since the features of each region were derived from the position and scale of the sequence and carried the information on the redundant wavelet, it is determined that the features with the highest relation and the most obvious change in each region characterize the spectral signals of the specific region (table 5.7). The optimal wavelet features were distributed in visible region, red-edge region, and near-infrared region. The wavelet features were mainly in the scales of 21 to 24, and the remaining five wavelet features (including wavelet feature 01 at 525 nm, wavelet feature 03 at 573 nm, wavelet feature 04 at 615 nm, wavelet feature 07 at 719 nm, and wavelet feature 10 at 809 nm) were in the high scales of 25 and 26. These features distributed in green peak region, red valley region, and high reflection platforms, capturing the amplitude variations of leaf spectrum in a wide spectrum range. The location of low-scale wavelet features was widely distributed, in which wavelet feature 12 and wavelet feature 13 reflected the plant internal structure differences. Most of the wavelet features were located in the strong chlorophyll absorption in the visible region. Wavelet feature 08, wavelet feature 09, and wavelet feature 10 were distributed in the red edge region; its movement indicates the crop growth condition. Wavelet feature 11, wavelet feature 12, and wavelet feature 13 were located in the near-infrared shoulder region, which represented the plant structure. This result showed that reflectance spectrum decomposition with CWT efficiently reduced the impact of leaf structure changes. Wavelet feature 13 was correlated to water absorption (1204 nm), which may indicate obvious water stress. Other features were mainly located at green peak region and red valley region.

146

Crop Pest and Disease Remote Sensing Monitoring and Forecasting TAB. 5.7 – Optimal wavelet features for stresses differentiation.

Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet

features feature 01 feature 02 feature 03 feature 04 feature 05 feature 06 feature 07 feature 08 feature 09 feature 10 feature 11 feature 12 feature 13

Scales 25 22 26 25 24 24 25 23 24 25 24 21 22

Wavelengths/nm 525 548 573 615 652 669 719 758 778 809 839 958 1204

p-value 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Wavelet feature 05 and wavelet feature 06 were located at chlorophyll absorption peak regions. Wavelet feature 07 was weakly correlated to water absorption. Crop stresses would result in biophysical and biochemical differences, i.e., pigment, water content, and canopy structure. It can be seen that the sensitive wavelet features could capture the changes of wheat under stresses.

5.2.2

Disease and Nutrient Stress Differentiation

To compare and evaluate the performance of raw spectral bands, traditional vegetation indices, and extracted wavelet features for the differentiation of different stresses, Fisher’s linear discriminate analysis (FLDA) and SVM were selected. The goal of FLDA is, by using the covariance matrix, to find the right projection direction to obtain the greatest degree of differentiation of each stress type. SVM is a statistical theory-based machine learning method which is suitable to solve nonlinear problems. Raw spectral bands, traditional vegetation indices, and extracted wavelet features were used in FLDA and SVM to differentiate different stresses. 60% of the dataset was used as calibration dataset, and 40% of the remaining dataset was used as testing dataset. According to the stress differentiation results of raw spectral bands, traditional vegetation indices and extracted wavelet features, the optimal one of these three spectral features was selected to assess wheat disease DI. Considering the possible variable multi-correlation, PLSR was used to build DI monitoring model. The model validation adopts leave-one-out cross validation method. CWT was used to analyze spectrum and select wavelet features to identify different stresses. Relying on the raw spectral bands, traditional vegetation indices, and extracted wavelet features, the differentiation model accuracies were illustrated in table 5.8. It shows that the wavelet features in stress differentiation with an overall accuracy of 0.91 and a kappa coefficient of 0.86 in the FLDA model and an overall

FLDA

Spectral bands

Vegetation indices

Wavelet features

Powdery mildew Yellow rust Nutrient stress Sum Producer’s accuracy/% Powdery mildew Yellow rust Nutrient stress Sum Producer’s accuracy/% Powdery mildew Yellow rust Nutrient stress Sum Producer’s accuracy/%

SVM

Sum

User’s accuracy/%

Overall accuracy/%

7

32

75.0

72.0

Powdery mildew

Yellow rust

Nutrient stress

24

1

Kappa

Powdery mildew

Yellow rust

Nutrient stress

Sum

User’s accuracy/%

Overall accuracy/%

Kappa

0.58

9

2

21

32

28.1

67.0

0.5

65.0

0.48

79.0

0.68

9

37

0

46

80.4

2

37

7

46

80.4

17

1

27

45

60.0

7

1

37

45

82.2

50

39

34

123

18

40

65

123

48.0

94.9

79.4

50.0

92.5

56.8

20

1

11

32

62.5

20

0

12

32

62.5

72.0

0.59

9

37

0

46

80.4

10

33

3

46

71.7

13

0

32

45

71.1

17

1

27

45

60.0

42

38

43

123

47

34

42

123

47.6

97.4

74.4

42.6

97.1

64.3

25

0

7

32

78.1

18

0

14

32

56.3

1

44

1

46

95.7

2

41

3

46

89.1

2

0

43

45

95.7

7

0

38

45

84.4

28

44

51

123

27

41

55

123

89.3

100

84.3

66.7

100

69.1

91.0

0.86

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TAB. 5.8 – Confusion matrix and classification accuracy.

147

148

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accuracy of 0.79 and a kappa coefficient of 0.68 in SVM model, respectively. The accuracies of raw spectral bands and traditional vegetation indices were relatively low. For these two differentiation models, the FLDA model is generally superior to SVM in stress differentiation, which may be related to the choice of SVM parameters. For different stresses, yellow rust can be successfully detected by using raw spectral bands and extracted wavelet features; and the User’s accuracy was up to 80.4% in FLDA. However, the User’s accuracies of yellow rust and nitrogen-water stress based on wavelet features were all 95.7%, while for powdery mildew was 89.3%. The results illustrated that the differentiation accuracy of powdery mildew, yellow rust, and nutrient stress based on wavelet features was higher and more reliable. Wavelet features were more suitable for the differentiation of wheat stresses. For further analysis, wavelet features were divided into low-scale (21 to 24) features and high-scale (25 and 26) features. The low-scale wavelet features, high-scale wavelet features, and total wavelet features were respectively applied as input variables to differentiate multiple stresses (table 5.9). For yellow rust infested wheat, nutrient stressed wheat and powdery mildew infested wheat, the differentiation accuracies based on the low-scale wavelet features were higher than those based on the high-scale wavelet features. The differentiation results using FLDA based on the canonical differentiation function were illustrated in figure 5.3. PLSR was used to estimate the severity of powdery mildew and yellow rust to further evaluate application ability of the optimal wavelet features. Satisfactory retrieval accuracy was achieved in both results, with RMSE less than 0.15 (figure 5.4). In addition, the accuracy of yellow rust had an enhanced reliability level, with R2 at 0.83, indicating that it was achievable to assess the wheat infestation severity by yellow rust.

TAB. 5.9 – Differentiation results for different stresses based on different wavelet feature combinations.

High-scale

Low-scale

All

Stresses Powdery mildew Yellow rust Nutrient stress Powdery mildew Yellow rust Nutrient stress Powdery mildew Yellow rust Nutrient stress

Sample points 11 15 15 11 15 15 11 15 15

Correct points 7 9 9 9 10 11 10 12 14

Accuracy/% 63.6 60.0 60.0 81.8 66.7 73.3 90.9 80.0 93.3

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149

FIG. 5.3 – Different stresses distributions based on the different wavelet features combinations.

FIG. 5.4 – Fitting results between estimated DI and measured DI based on wavelet features.

150

5.3 5.3.1

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Crop Pest and Disease Stress Differentiation at Leaf Scale

1. Differentiation with traditional spectral indices Considering that crop biophysical variations will influence the highly non-linear distribution of absorption features of hyperspectrum, Kernel discriminant analysis is used to identify crop stresses especially for the detection of plant pests and diseases. However, traditional calculation of projection characteristics generated from hyperspectrum is usually influenced by the redundant information between wavelength bands, resulting in dimension reduction. To solve this problem, we developed a crop pests and diseases identification method based on transitional spectral indices at leaf scale. 1) Selection of existing spectral vegetation indices for the differentiation of pests and diseases The main purpose is to find the spectral vegetation index correlated to physiological and biochemical changes of different stresses. Specifically, 14 candidate spectral vegetation indices were used as biophysical indicators (Daughtry et al., 2000), including NDVI, PRI, SIPI, MCARI, HI, AI, MSR in table 1.1, PhRI, ARI, RVSI, NRI in table 4.5, NPCI in table 5.5 and others in table 5.10. To promote the selection of vegetation indices for differentiation, we used two methods to eliminate excessive correlation vegetation indices based on prior knowledge, reducing the multicollinearity effect between redundant information and candidate spectral indices. The correlation analysis between spectral vegetation indices and disease severity (DS) was used to select spectral vegetation indices significantly related to healthy, yellow rust-infested, powdery mildew-infested, and aphid-infested wheat samples. The independent t-test was adopted to select the optimal indices that showed significant difference to each stress. Table 5.11 shows the classification capability of each spectral vegetation index for mutiple pests and diseases. Table 5.12 shows the correlation analysis, Independent t-test of spectral vegetation indices. The intersection of these spectral vegetation indices, including TAB. 5.10 – Summary of the selected spectral vegetation indices. Spectral vegetation indices Yellow Rust Index, YRI (R515 Powdery Mildew Index, PMI (R400

Definitions (R515 − R698)/ + R698) − 0.5 * R738 (R400 − R735)/ + R735) − 0.5 * R403

Sensitive to Wheat disease Wheat disease

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TAB. 5.11 – Independent differentiation feasibility of the selected spectral vegetation indices. Differentiation accuracy

Vegetation indices Healthy ✓

MSR NDVI NRI PRI SIPI PhRI NPCI ARI AVSI MCARI HI YRI AI PMI

Yellow rust

Aphid

Powdery mildew

✓ ✓

✓ ✓ ✓ ✓

✓

Note: ✓ means the classification accuracy higher than 60%.

TAB. 5.12 – Optimal spectral vegetation indices for the differentiation of different stresses. Correlation Healthy MSR NDVI NRI PRI SIPI PhRI NPCI ARI AVSI MCARI HI YRI AI PMI

Yellow rust

+ +

Independent t-test

Aphid

Powdery mildew

+

+

Healthy & others

Yellow rust & others

Aphid & others

Powdery mildew & others

+

+

+

+

+ +

+

+

+

+

+ +

+ + +

+

Note: + means optimal spectral vegetation indices, which have a p-value lower than 0.05 for both correlation analysis and Independent t-test.

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PRI, MSR, HI, SIPI, YRI, and PMI, represents an ideal function and contributes the most in distinguishing healthy samples and stressed samples. 2) Identification of healthy and stressed samples To better evaluate the identification feasibility under the three different infestation severities, three severity levels were constructed, namely slight infestation, moderate infestation, and severe infestation. Table 5.13 lists the standard identification coefficients. The results illustrated that SIPI, MSR, PMI dominated the first canonical identification function. PRI, SIPI dominated the second canonical identification function. HI and YRI dominated the third canonical identification function. For slight class, canonical identification function 1 was 59.1% of the change, and canonical identification function 2 was 28%, that is, the cumulative contribution achieved 87.1%. For moderate class, canonical identification function 1 accounted for 67.1% of the variation, and canonical identification function 2 also accounted for 30.8% of the variation (i.e. the cumulative contribution reached 94.9%). For severe class, canonical identification function 1 was 74.2%, while canonical identification function 2 was 21.3%. The previous two canonical identification functions were used to construct the projection spread of the discriminating score (figure 5.5). Obviously, for slight class, canonical identification function 1 can distinguish aphids-infested leaves from other categories. However, by adding canonical identification function 2, the distance between categories was enlarged (figure 5.5a). For moderate class, canonical identification function showed excellent performance in detecting all infested samples, while canonical identification function was able to distinguish healthy samples (figure 5.5b). In the same way, for severely infested class, the combination of canonical identification function 1 and canonical identification function 2 performed better at distinguishing the difference between healthy and infested ones (figure 5.5c). Table 5.14 shows the classification confusion matrix based on cross-validated samples. The confusion matrix clearly showed that the overall accuracies of the three DI classes were 82.9%, 87.9%, and 89.2%, and the appearance level of these three kappa coefficients was higher than 0.8. And for slightly class, the identification error was the highest among samples damaged by aphids. For moderate class and severe class, the main classification error was generated of yellow rust-infested samples and powdery mildew-infested samples. The empirical regression model was constructed according to the discriminant score calculated by canonical identification function 1, and table 5.15 lists the obtained prediction equations. The results showed that for slight class, there was an exponential relationship between DIs and scores, and the optimal value of R2 was higher than that of the linear regression and polynomial regression. For moderate class and severe class, the linear model was better than other two models in DI identification. Figure 5.6 shows the scatter plots between measured DI and estimated DI. The results illustrated that the regression formula was more reliable. The values of R2 of the three stresses of slight class were 0.78, 0.82, 0.83, and RMSEs were 2.79, 2.02,

Standardized canonical coefficients Vegetation indices

Canonical identification function 1

Canonical identification function 2

MSR PRI SIPI HI YRI PMI

0.354 −1.22 3.382 −3.336 −1.073 2.62

−3.677 2.802 2.511 −3.092 −3.271 −1.896

MSR PRI SIPI HI YRI PMI

0.072 −0.314 0.845 −0.171 0.179 0.22

−0.747 0.722 0.627 −0.159 −0.695 −0.237

MSR PRI SIPI HI YRI PMI

−1.267 5.665 −4.764 2.36 2.431 −2.763

−4.507 3.951 2.841 4.48 −1.216 −1.738

Canonical identification function 3 Slight 2.526 1.104 1.095 −14.558 1.491 0.889 Moderate 0.513 0.285 0.274 −0.747 0.561 −0.801 Severe −0.273 −2.559 −2.345 10.72 1.86 −0.107

Correlation coefficients Canonical identification function 1

Canonical identification function 2

Canonical identification function 3

0.96 −0.171 0.62 0.179 −0.107 0.575

0.181 0.457 −0.695 −0.237 0.122 0.158

−0.045 0.444 0.0561 −0.801 0.612 0.174

−0.766 0.309 0.543 0.183 0.192 0.472

0.053 0.653 −0.616 −0.154 0.114 0.168

0.395 −0.294 −0.407 0.66 0.515 −0.174

0.881 −0.223 −0.514 0.058 0.125 −0.557

0.093 −0.612 0.576 0.322 −0.109 −0.153

−0.163 0.098 −0.341 0.798 0.493 −0.183

Crop Pest and Disease Differentiation

TAB. 5.13 – Standardized norm coefficients and correlation coefficients of identification canonical functions established by the chosen spectral vegetation indices.

153

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FIG. 5.5 – Identification results of different stresses at (a) slight, (b) moderate and (c) severe classes.

and 2.3, respectively (figure 5.6a–c). R2 values of moderate classes were 0.86, 0.79, 0.85, and RMSEs were 2.4, 3.47, 2.28, respectively (figure 5.6d–f). R2 of severe class values were 0.88, 0.89, and 0.79, and RMSEs were 4.49, 3.34, 3.58, respectively (figure 5.6g–i). These results demonstrated that it is potential to use the normative identification scores to construct the identification model to estimate the severity of the three stresses. The k-fold cross-validation, in which k = 5, are shown in table 5.16. For the verification of the DS differentiation model, figure 5.7 shows R2 value of the predicted DS of (a) yellow rust, (b) aphid, and (c) powery mildew based on different sample dataset at leaf scale. For the provided kernel discriminant approach, the accuracy of classification and estimation of DS only varied with the DS within a limited range. Furthermore, from the perspective of small sample training, we tested different training sample populations for observing the variable feasibility to assess its impact on predicted DS. It can be seen from figure 5.7 that the determination coefficient R2 under the different scales increased in an alike logarithmic function, which revealed that the convergence speed of R2 was coincident. When the mean training quantity exceeded 40, R2 was higher than 0.8. 2. Differentiation with new spectral indices The traditional vegetation indices based on hyperspectral data are efficient for the indirect identification of crop pests and diseases (Broge and Leblanc, 2001). However, for the differentiation of crop stresses, the traditional indices are limited. Several new spectral indices are developed, which will be helpful for the identification of different crop stresses (Ma et al., 2019). Three crop stresses, i.e., yellow rust, powdery mildew, and aphid of winter wheat, were analyzed. 1) New spectral indices construction Four new spectral vegetation indices were constructed, which can differentiate specific crop stresses quantitatively. The response of a single band to different stresses has its own characteristics, especially in the later development stages of the stress. However, the normalized difference between the two bands is very sensitive to the differences in hyperspectral data caused by different stresses such as powdery mildew, yellow rust, and aphid. Thus, new spectral indices, combined with single

Field investigations Differentiation

Slight 0% ≤ DS ≤ 20%

Moderate 20% < DS ≤ 45%

Severe DS > 45%

125 2 4 3

Yellow rust 5 71 1 6

12 8 48 4

Powdery mildew 3 7 4 42

93.3

85.5

66.7

75.0

130 2 2 1

3 69 1 4

10 3 46 1

2 2 4 44

96.3

89.6

76.7

84.6

138 2 2 1

2 36 0 2

4 1 24 1

1 5 1 21

97.2

90.0

80.0

75.0

Healthy Healthy Yellow rust Aphid Powdery mildew Producer’s accuracy/% Healthy Yellow rust Aphid Powdery mildew Producer’s accuracy/% Healthy Yellow rust Aphid Powdery mildew Producer’s accuracy/%

Aphid

User’s accuracy/%

Overall accuracy/%

Kappa coefficient

86.2 79.8 84.2 76.3

82.9

0.81

89.6 90.8 86.8 88.0

89.2

0.87

95.2 81.8 88.9 84.0

87.9

0.83

Crop Pest and Disease Differentiation

TAB. 5.14 – Performance of the spectral vegetation indices-based kernel discriminant approach at leaf scale.

155

156

Slight class Yellow rust Aphid

Powdery mildew

Identification formula DS = 0.9338 * x + 0.6796 DS = 1.732 * e0.75*x DS = 0.0382 * x2 + 0.2337 * x + 0.5024 DS = 0.8111 * x + 1.5573 DS = 0.21 * e0.49*x DS = 0.0291 * x2 + 0.2308 * x + 0.3043 DS = 0.9495 * x + 4.447 DS = 0.0323 * e0.22*x + 0.0131 DS = 0.0287 * x2 + 0.2423 * x + 0.1129

Moderate class R2 0.695 0.726 0.701 0.612 0.719 0.704 0.696 0.721 0.696

Identification formula DS = 0.966 * x + 0.6571 DS = 0.4104 * e0.344*x + 0.145 DS = 0.1057 * x2 + 1.2548 * x + 1.0134 DS = 1.0213 * x + 1.6314 DS = 0.9843 * e0.4373*x DS = 0.016 * x2 + 0.1572 * x + 0.1042 DS = 0.9531 * x + 1.1606 DS = 0.0052 * e0.552*x + 0.0166 DS = 0.0004 * x2 + 0.0042 * x + 0.0017

Severe class R2 0.681 0.559 0.492 0.675 0.657 0.440 0.728 0.624 0.677

Identification formula y = 0.9338 * x + 5.002 y = 1.4087 * e0.707*x + 0.0148 y = 0.4119 * x2 + 1.2548 * x + 1.0134 y = 0.9531 * x + 1.1606 y = 0.6883 * e0.6379*x + 1.1587 y = 0.0089 * x2 + 0.1169 * x + 0.1471 y = 0.8738 * x + 10.6368 y = 0.0114*e0.1755*x + 0.0727 y = 0.01 * x2 + 0.1267 * x + 0.1223

R2 0.689 0.672 0.615 0.655 0.534 0.537 0.710 0.641 0.545

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TAB. 5.15 – Relationship statistical analysis of canonical identification scores (x) and the identified stress DS.

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FIG. 5.6 – Scatter plots of measured DI and estimated DI by spectral vegetation indices-based kernel discriminant approach of different seventies: (a–c) slight class, (d–f) moderate class, (g–i) severe class. TAB. 5.16 – Performance of the two different SVM classifiers. Disease severity 0% ≤ DS < 20% 20% ≤ DS < 45% DS ≥ 45%

Classification state Optimal Worst Optimal Worst Optimal Worst

Classification accuracy/% Yellow rust 79.8 67.1 90.8 83.3 89.4 82.1

Aphid 84.2 61.3 86.8 64.7 88.9 80.8

Powdery mildew 76.3 63.1 88.0 76.8 86.0 84.9

Recall accuracy/% Yellow rust 85.5 81.2 89.6 83.5 90.8 83.3

Aphid 72.7 63.4 76.7 67.5 80.0 68.2

Powdery mildew 75.0 70.2 84.6 79.2 85.4 83.5

FIG. 5.7 – R2 value of the predicted DS of (a) yellow rust, (b) aphid, and (c) powdery mildew based on different sample dataset at leaf scale.

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

band response and the change of normalized difference, can identify specific crop stresses effectively. ReliefF was adopted to select the optimal combination of single band response and the change of normalized difference (Robnik-Šikonja and Kononenko, 2003). The raw ReliefF was designed to assess the attribute quality based on their values differences between instances. This method not only can solve dichotomy problems, but has strong robustness when processing noisy or incomplete data. ReliefF algorithm was used to find the two nearest neighbors of a given sample. Each neighborhood was made up of k samples. The sets of k-nearest neighbors of the same category were considered “hit”. For a given k, and the neighbors from different categories were considered “miss”. For a specific index, ReliefF is used to select the most relevant single band, which belongs to the best weighted bands (20%). Two bands are required to normalize the band difference, for which one is from 10% of best weighted single bands and the other from the 10% of worst weighted single bands. All possible combinations would be searched firstly, and a stopping rule of the distance of less than 50 nm between the two wavelengths was used. The ReliefF method was finally adopted again to search the optimal normalized wavelength difference. Furthermore, the possible weight of a single wavelength was set from −0.5 to 0.5. The spectrum in the wavelength range from 400 to 1000 nm was selected. The correlation coefficient between different wavelengths was calculated (figure 5.8).

FIG. 5.8 – Contour map to visualize the correlation of narrow wavelengths of 400–1000 nm.

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159

The result illustrated that the closer wavelength bands were significantly correlated. The variables of the spectral features were described by the normalized wavelength difference. It was inappropriate for combining with the two highly correlated wavelengths. Thus, the minimum distance in the wavelengths was set as 50 nm. Due to the obvious similarity (correlation coefficient was close to 0.9) of the near-infrared bands of 750–1000 nm (figure 5.8), the ReliefF method was only applied in the wavelength bands of 400–800 nm. Before establishing new spectral indices, the single wavelength which mostly relevant to the disease was calculated based on the ReliefF method (figure 5.9). For the healthy samples, the optimal single wavelengths were around 400 nm (figure 5.9a). The best and worst single wavelengths were at 400 nm and 750 nm (figure 5.9a). The single wavelengths significantly correlated with powdery mildew were around 400 nm, 500 nm, and 750 nm. The normalized difference wavelengths for powdery mildew were around 500 nm, 680 nm, and 750 nm (figure 5.9b). The single wavelengths associated with yellow rust infestation were around 540 nm and 730 nm. The normalized difference wavelengths for yellow rust were around 430 nm and 670 nm (figure 5.9c). For aphid, the single wavelength was around 400 nm, and the normalized difference wavelengths were around 720 nm and 780 nm (figure 5.9d).

FIG. 5.9 – Significance of single spectral bands for different crop stresses based on ReliefF method.

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

According to the ReliefF algorithm, the highly correlated single wavelength and the normalized reflectance difference were extracted, and the possible combinations and weights of spectral bands were tested. Based on the difference between the reflectance of 403 nm and the normalized spectral reflectance from 402 nm to 739 nm, the HI was finally built (equation (5.1)). Based on the difference between the reflectance of 738 nm and the normalized spectral reflectance from 515 nm to 698 nm, PMI was built (equation (5.2)). Based on the difference of 736 nm and the normalized spectral reflectance from 419 nm to 730 nm, YRI was built (equation (5.3)). Based on the difference of 403 nm and the normalized spectral reflectance from 400 nm to 735 nm, AI was built (equation (5.4)). HI ¼

R739  R402  0:5  R403 R739 þ R402

ð5:1Þ

PMI =

R515  R698  0:5  R738 R515 þ R698

ð5:2Þ

YRI =

R730  R419  0:5  R736 R730 þ R419

ð5:3Þ

AI ¼

R400  R735  0:5  R403 R400 þ R735

ð5:4Þ

2) Stresses differentiation Figure 5.10 shows the stresses differentiation ability of the new spectral indices. The better separation was optimized by using threshold method. The differentiation accuracies of HI, YRI, PMI, and AI were 86.5%, 91.6%, 85.2%, and 93.5%, respectively. The kappa coefficients of the HI, YRI, PMI, and AI were 0.73, 0.83, 0.57, and 0.75, respectively. The results showed that the new spectral indices performed well in identifying different crop stresses. Compared to traditional vegetation indices, the proposed new spectral indices had the higher differentiation accuracies. The differentiation accuracy of HI at 86.5% was the highest for differentiating healthy and other damaged wheat, then followed by PRI (84.9%), NDVI (83.6%), and MSR (82.9%). AI was the best for aphid damaged wheat differentiation, which with an identification accuracy of 93.5%. Then PRI (85.3%), MSR (84.7%), and ARI (82.0%) followed. For winter wheat, YRI and PMI were not useful for identifying diseases with the identification accuracy of all applied indices was 50%. The statistical correlation analysis between PMI and DI was carried out, and the remote sensing inversion model of DI was constructed. The relationship between PMI and DI was significantly positive, indicating that PMI can identify powdery mildew severity potentially.

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FIG. 5.10 – Differentiation results of different stresses based on the HI, PMI, YRI, and AI. 3. Differentiation with wavelet transform Hyperspectral absorption characteristics are important parameters for characterizing crop biophysical variables to automatically identify crop stresses. CWA is a progressive spectral analysis method for the extraction of absorption characteristics. However, few studies have summarized specific wavelet features and their relationship with pathological features caused by different stress (Bruce and Li, 2001; Cheng et al., 2010). This section aims to identify the optimal wavelet features to explore specific pathological changes of wheat powdery mildew and yellow rust. Continuous wavelet transform is an outstanding signal analysis tool for hyperspectral data processing. It can detect and analyze weak signals of various resolutions and scales, and analyze multi-dimensional signals of continuous scales, such as image cubes. The potential of wavelet features in stress differentiation is explored. Using continuous wavelet transform method, a variety of wavelet features were extracted, which included the wavelet power generation, the correlation calculation, and the orthogonal wavelet feature identification. Figure 5.11a–c illustrates the relative proportions produced by the yellow rust datasets in 2002, 2003, and 2005. For the dataset in 2002, 13 prominent feature areas which were highly sensitive to the severity of yellow rust were identified with R2 = 0.85. These feature areas were at the scales of 21 to 27, which distributed in the green peak from 520 nm to 600 nm, the red-edge from 630 nm to 760 nm, and the shortwave infrared from 1450 nm to 2300 nm (figure 5.11a). Besides, for the dataset

162

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

FIG. 5.11 – Visualization map of correlation analysis of CWA generated with the yellow rust datasets in (a) 2002, (b) 2003, and (c) 2005. in 2003, the infested samples were closely related to several narrow bands in visible region from 530 nm to 720 nm, and the shortwave infrared region from 1450 nm to 2350 nm, at the scales from 21 to 27 (figure 5.11b). Finally, 12 of them were selected in the highlighted feature areas. For the dataset in 2005, the positions of the selected features were similar to those in the year of 2002, but more wavelet features at correlated low scales were selected. Finally, 13 outstanding features were selected with the scales from 21 to 25 (figure 5.11c). Figure 3.15 illustrates the relative proportions generated by the powdery mildew datasets in 2002, 2003, and 2012. For the dataset in 2002, 10 significant feature areas at scales from 22 to 26 were obtained with R2 > 0.85. These features distributed in the blue edge from 350 nm to 480 nm, the red visible region from 620 nm to 670 nm, the near infrared region from 760 nm to 1150 nm, and the shortwave infrared region from 2200 nm to 2350 nm (figure 3.15a). For the dataset in 2003, total 17 feature regions were obtained (figure 3.15b). For the dataset in 2012, total 13 feature regions were obtained (figure 3.15c). Finally, the wavelet features of the intersection of the relevant correlation maps obtained based on the datasets in these three years indicated that, the appearance of yellow rust led to the wavelet feature difference in green peak region, red-edge region, and shortwave region in the scales from 21 to 25, and the appearance of powdery mildew led to the wavelet feature difference in blue-edge region, near infrared region, and shortwave region in the scales from 21 to 24. These results obtained using CWA and statistical analysis indicated the possibility of differentiating powdery mildew and yellow rust based on the location, amplitude, and size of wavelet features. Table 5.17 illustrates the wavelengths and scales of the wavelet features with the highest R2 obtained from the relative region intersections of

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TAB. 5.17 – Optimal wavelet features for the differentiation of powdery mildew and yellow rust. Yellow rust Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet Wavelet

features feature 01 feature 02 feature 03 feature 04 feature 05 feature 06 feature 07 feature 08 feature 09 feature 10

Wavelength/nm 481 543 574 633 689 742 1292 1467 2057 2290

Scale 25 22 22 25 25 25 24 24 24 24

Powdery mildew 2

R 0.93 0.94 0.90 0.92 0.96 0.95 0.98 0.84 0.93 0.90

Wavelength/nm 366 438 621 795 932 978 1159 1259 2234 2347

Scale 24 22 22 26 25 23 24 23 22 24

R2 0.99 0.96 0.97 0.98 0.98 0.94 0.96 0.95 0.94 0.97

powdery mildew and yellow rust. There were obvious differences in the locations and scales for powdery mildew and yellow rust. 4. Comparison of spectral features and wavelet features In table 5.10, 14 commonly used hyperspectral features which have been proved significant relative to crop growth, pigment content, nitrogen and water content, photosynthetic activity, and crop stresses were adopted. Due to their significant response to crop stresses, the original green bands at the wavelength range from 533 nm to 553 nm were selected. Due to their successes in the crop stress identification in previous studies, the first derivative transformation and continuous removal transformation were also chosen, and herein which is the first derivative value at blue edge. In general, for the multiple variables-based models, there is some redundant information among spectral features or wavelet features. Thus, to eliminate the impact when establishing multiple variables-based regression models, a pre-selection process was used to test and eliminate the spectral features and wavelet features, that is, a correlation analysis was carried out for each pair of variables in both spectral features (table 5.10) and wavelet features (table 5.17). Based on the threshold of R2 > 0.85, six spectral features, three wavelet features for yellow rust dataset, and three wavelet features for powdery mildew were eliminated from the raw feature sets (tables 5.18 and 5.19). Based on conventional spectral features and identified wavelet features, SLR and PLSR regression models were constructed to evaluate and compare performance in disease identification and DI estimation. By using forward selection and compared with the spectral feature-based regression model, the wavelet feature-based regression model produced more precise estimations (table 5.20). The feasibility in stresses identification was fully considered in CWA due to the optimized location and scale for each wavelet feature. For the datasets in 2002, 2003, and 2005, the regression models based on the wavelet features obtained by using yellow rust datasets performed the best, which obtained the R2 as 0.947, 0.962, and 0.958, and RMSE

164

SDb SDy SDr MSR NRI PRI SIPI PhRI NPCI ARI RVSI MCARI HI YRI

SDb 1

SDy 0.795 1

SDr −0.585 −0.926 1

MSR −0.839 −0.929 0.747 1

NRI −0.29 −0.747 0.751 0.744 1

PRI 0.798 0.968 −0.82 −0.982 −0.779 1

SIPI 0.789 0.984 −0.898 −0.939 −0.77 0.957 1

NPCI 0.802 0.974 −0.828 −0.974 −0.766 0.998 0.661 1

ARI 0.816 0.911 −0.734 −0.983 −0.761 0.959 0.442 0.652 1

RVSI −0.837 −0.893 0.693 0.954 0.637 −0.938 −0.611 −0.536 −0.626 1

MCARI 0.705 0.911 −0.643 −0.738 −0.579 0.811 0.848 0.818 0.711 −0.701 1

HI 0.947 0.944 −0.78 −0.946 −0.562 0.941 0.935 0.945 0.928 −0.926 0.839 1

YRI −0.313 −0.464 0.408 0.547 0.537 −0.545 −0.399 −0.526 −0.43 0.396 −0.364 −0.402 1

PMI −0.84 −0.893 0.666 0.982 0.688 −0.968 −0.902 −0.965 −0.973 0.95 −0.679 −0.934 0.473 1

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

TAB. 5.18 – Correlation coefficients of the spectral features.

Yellow rust

Yellow rust

Powdery mildew

Wavelet feature 01 Wavelet feature 02 Wavelet feature 03 Wavelet feature 04 Wavelet feature 05 Wavelet feature 06 Wavelet feature 07 Wavelet feature 08 Wavelet feature 09 Wavelet feature 10 Wavelet feature 01 Wavelet feature 02 Wavelet feature 03 Wavelet feature 04 Wavelet feature 05 Wavelet feature 06 Wavelet feature 07 Wavelet feature 08

Wavelet feature 01

Wavelet feature 02

Wavelet feature 03

Wavelet feature 04

Wavelet feature 05

1

−0.234

−0.952

−0.981

1

0.453

0.286

1

Powdery mildew

Wavelet feature 06

Wavelet feature 07

Wavelet feature 08

Wavelet feature 09

−0.949

0.944

−0.995

0.308

0.863

0.378

−0.24

0.291

−0.305

−0.175

0.973

0.935

−0.927

0.972

−0.29

−0.829

1

0.903

−0.981

0.989

−0.212

1

−0.814

0.945

1

−0.951 1

Wavelet feature 10

Wavelet feature 01

Wavelet feature 02

Wavelet feature 03

Wavelet feature 04

Wavelet feature 05

0.694

0.907

−0.495

−0.05

−0.668

−0.871

−0.467 0.077

Wavelet feature 06

Wavelet feature 07

Wavelet feature 08

0.803

0.317

−0.736

−0.727

0.036

−0.694

0.63

0.579

0.299

0.833

−0.853

−0.559

−0.099

−0.786

−0.704

−0.377

0.784

0.712

−0.311

−0.743

0.167 0.7

−0.717

−0.913

−0.812

−0.324

0.748

0.726

−0.295

−0.817

0.842

−0.774

−0.593

−0.794

−0.681

−0.351

0.736

0.702

−0.313

−0.748

0.801

0.841

0.761

0.358

0.809

0.369

−0.75

−0.766

0.352

0.158

−0.198

−0.324

−0.883

−0.694

−0.394

−0.81

−0.341

0.791

0.74

−0.3

−0.647

0.768

1

0.515

0.136

−0.1

0.088

0.611

−0.693

−0.541

0.471

0.361

−0.309

1

0.61

0.218

0.834

0.424

−0.285

−0.786

0.377

0.718

−0.821

1

0.223

0.569

0.717

−0.616

−0.946

0.695

0.62

−0.59

1

0.795

0.203

−0.504

−0.633

0.261

0.713

−0.698

1

−0.115

−0.607

−0.371

−0.168

0.951

−0.937

1

−0.613

−0.869

0.972

0.066

−0.044

1

0.82

−0.468

−0.78

0.781

1

−0.844

−0.505

0.48

1

−0.028

0.062

1

−0.988

Crop Pest and Disease Differentiation

TAB. 5.19 – Correlation coefficients of the wavelet features for yellow rust and powdery mildew.

1

165

166

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

TAB. 5.20 – Validation results of the SLR and PLSR models based on optimal spectral features and wavelet features for diseases in different years. SLR Yellow rust in 2002 Yellow rust in 2003 Yellow rust in 2005 Powdery mildew in 2002 Powdery mildew in 2003 Powdery mildew in 2012

Features Spectral features Wavelet features Spectral features Wavelet features Spectral features Wavelet features Spectral features Wavelet features Spectral features Wavelet features Spectral features Wavelet features

2

PLSR 2

R 0.867

RMSE 0.0217

RMSPE/% 7.14

R 0.928

RMSE 0.0018

RMSPE/% 3.13

0.947

0.0151

5.42

0.971

0.0012

1.56

0.924

0.0265

3.58

0.897

0.0032

6.12

0.962

0.0144

3.35

0.969

0.0027

3.32

0.913

0.0347

7.86

0.915

0.0044

9.43

0.958

0.0201

5.28

0.981

0.0026

6.82

0.808

0.0524

9.14

0.891

0.0084

4.13

0.914

0.0418

7.01

0.984

0.0066

2.79

0.837

0.0474

8.43

0.921

0.0042

4.31

0.931

0.0224

4.22

0.968

0.0039

3.91

0.827

0.0532

8.27

0.917

0.0054

9.14

0.944

0.0324

5.12

0.954

0.0037

4.34

as 0.015, 0.014, and 0.02, and RMSPE as 5.42%, 1.35%, and 4.28%, respectively. These results demonstrated that models based on the wavelet features outperformed the models based on the traditional spectral indices for the identification of powdery mildew with each year dataset, with R2 at 0.914, 0.931, and 0.944, RMSE at 0.041, 0.022, and 0.032, and RMSPE at 7.01%, 4.22%, and 5.12%, respectively. The PLSR models based on spectral features and wavelet features were also constructed to remove the remaining collinearity among features (table 5.20). The PLSR model based on the wavelet features obtained higher precision in DI estimation for both powdery mildew and yellow rust than the SLR model based on the same features. For yellow rust, the mean R2 increased by 0.019; the mean RMSE decreased by 0.015; and the mean RMSPE decreased by 0.8%. For powdery mildew, the mean R2 increased by 0.039; the mean RMSE decreased by 0.027; and the mean RMSPE decreased by 1.8%. The possible reason for the results may be that the wavelet features characterized information was eliminated from the model. These results indicated that the principal component transform for the PLSR model can estimate the disease severity in detail. The scatter plots between surveyed DI and

Crop Pest and Disease Differentiation

167

FIG. 5.12 – Scatter plots between surveyed DI and estimated DI at canopy scale for yellow rust in (a) 2002, (b) 2003, and (c) 2005, and powery mildew in (d) 2002, (e) 2003, and (f) 2012. estimated DI obtained by the optimal spectral features in table 5.13 and wavelet features in table 5.14 are shown in figure 5.12. Wavelet features show the best estimation of DI for the differentiation of different stresses. Compared with the traditional spectral features, the scatter plot fitting line between surveyed DI and estimated DI was more significant with a p-value lower than 0.05, and R2 higher than 0.9. The distance and divisibility of the different diseases had been enhanced by the wavelet features, indicating the performance of continuous wavelet transform in stress differentiation. The Fisher discriminant model results are summarized in table 5.21. The overall accuracies based on traditional spectral features in 2002, 2003, and two years combined data were 74.5%, 75.9%, and 75.4%, respectively; and their corresponding kappa coefficients were 0.86, 0.82, and 0.79, respectively. The overall accuracies based on wavelet features in 2002, 2003, and combined data were, respectively, 90.2%, 91.9%, and 89.8%, and their corresponding kappa coefficients were 0.87, 0.89, and 0.84, respectively. These results showed that the wavelet features outperformed the traditional spectral features in the crop stress differentiation.

5.3.2

Differentiation at Canopy Scale

1. Differentiation with new spectral indices To further verify the proposed new spectral indices, the wheat spectral reflectance of yellow rust infestation, powdery mildew infestation, and aphid infestation at canopy scales was used. Limited by the healthy wheat samples at the same stage,

168

Spectral features

2002

2003

2002 and 2003

Yellow rust Powdery mildew Sum Producer’s accuracy/% Yellow rust Powdery mildew Sum Producer’s accuracy/% Yellow rust Powdery mildew Sum Producer’s accuracy/%

Yellow rust 38

Powdery mildew 14

Sum 52

User’s accuracy/% 73.1

12

38

50

76.0

50

52

76.0

73.1

43

13

56

76.8

14

42

56

75.0

Wavelets features Overall accuracy/% 74.5

75.9

Kappa 0.86

0.82

Yellow rust 46

Powdery mildew 6

Sum 52

User’s accuracy/% 88.4

50

92.0

4

46

50

52

92.0

88.4

51

5

56

91.1

4

52

56

92.8

57

55

55

57

75.4

76.3

92.8

91.2

117

39

156

75.0

140

16

156

89.7

158

76.0

158

89.8

75.4

0.79

40

120

16

142

157

159

154

160

74.5

75.4

90.9

88.7

Overall accuracy/% 90.2

Kappa

91.9

0.89

89.8

0.84

0.87

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

TAB. 5.21 – Differentiation results based on traditional spectral features and wavelet features.

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169

FIG. 5.13 – Differentistion results of different stresses based on the application of (a) PMI, (b) YRI, and (c) AI to hyperspectral data at canopy scale. the HI has not been used to separate different stresses. The differentiation of the other three indices is illustrated in figure 5.13. Since the experimental situations and sensor specifications were different, it was necessary to reset the threshold for each dataset. Based on the hyperspectral data at canopy scale, the identification precision of YRI, PMI, and AI were 84.7%, 82.4% and 87.6%, respectively. For the wheat canopy spectral data infested by yellow rust, DI values of 55 wheat samples were surveyed. YRI corresponding to the survey sample was calculated based on the canopy spectral data. One part of the survey DI was used for the calibration of the model, and the remaining part was used for validation. The DI estimated regression based on YRI can be represented as follows. DI ¼ 1294:9  YRI  1179:6

ð5:5Þ

A satisfied performance of the regression formula was obtained, with R2 as 0.81. The R2 between the surveyed DI and the estimated DI reached 0.86. The result demonstrated the potential using of YRI to estimate the yellow rust severity for winter wheat. 2. Differentiation with wavelet transform Based on the hyperspectral data of powdery mildew and yellow rust in winter wheat at canopy scale, the potential of wavelet features obtained using CWA was further explored. A total of 135 wheat canopy spectral samples were used, in which,

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

80 samples were randomly selected for model calibration, and the remaining 55 samples were used for model validation. Based on the thresholds for R absolute value which was higher than 0.3 and p-value lower than 0.01, the sensitive spectral bands for powdery mildew and yellow rust were selected. These sensitive regions mainly distributed in the wavelength range from 500 nm to 890 nm, which were the locations of strong absorption of pigment. Specifically, the sensitive spectral bands for powdery mildew were located at the regions from 536 nm to 566 nm and 706 nm to 734 nm; the sensitive spectral bands for yellow rust were located at the regions from 658 nm to 688 nm and 740 nm to 799 nm; and the sensitive spectral bands for both powdery mildew and yellow rust were located at the region from 850 nm to 884 nm. The optimal wavelet features for the differentiation of powdery mildew and yellow rust are shown in table 5.22. The wavelet features of the two diseases were strongly correlated with the disease severity, and the absolute value of correlation coefficient R was higher than 0.6. From the scale analysis, the wavelet features of the two diseases were mainly distributed in the low scales of 22 to 24, and there were two in the high scales of 25 to 26 respectively. Powdery mildew also contained a wavelet feature in the high scale of 27. From the perspective of characteristic distribution, yellow rust was distributed in the visible region of the strong absorption of pigments.

TAB. 5.22 – The optimal wavelet features for powdery mildew and yellow rust differentiation. Powdery mildew Wavelet features Wavelet feature 01 Wavelet feature 02 Wavelet feature 03 Wavelet feature 04 Wavelet feature 05 Wavelet feature 06 Wavelet feature 07 Wavelet feature 08 Wavelet feature 09 Wavelet feature 10

Yellow rust

Scales

Wavelengths

Threshold for absolute value of R

Scales

Wavelengths

Threshold for absolute value of R

22

749–750 nm

0.63

22

571–571 nm

0.88

22

958–961 nm

22

704–713 nm

23

476–478 nm

23

572–574 nm

23

747–748 nm

23

627–631 nm

23

1036–1037 nm

23

710–713 nm

23

1250–1252 nm

24

578–581 nm

24

1142–1148 nm

24

615–631 nm

25

516–519 nm

25

431–449 nm

26

962–979 nm

26

476–488 nm

27

738–768 nm

Crop Pest and Disease Differentiation

171

The five wavelet features of the visible region for powdery mildew were mainly distributed in green peaks and red valleys, while the other five features were in near-infrared region characterizing crop cell structure. Some studies found that continuous wavelet transform can effectively weaken the influence of leaf structure by spectral decomposition, while these features distributed in near-infrared bands were not reflected in spectral bands. Wavelet feature 05 and 09 were at strong water absorption valleys. These wavelet features could sensitively capture changes in pigment, water, morphology and structure of stressed wheat. However, the wavelet features were slightly different from the spectral band positions, and the intersection was less. The possible reason was that the continuous wavelet transform used different scales to analyze the entire spectrum at different positions. To a certain extent, the changes of target information were highlighted, and the most sensitive multi-scale and location wavelet features to different diseases were obtained. The confusion matrix and accuracy evaluation results of the Fisher80-55 model based on spectral bands, wavelet features, and combinations of spectral bands and wavelet features, and the leave-one-out cross validation models are shown in table 5.23. In addition, since the Fisher cross validation model was based on different observations, it was impossible to establish a uniform Fisher rule to discrimination scatter plots. Only the group histogram established by Fisher canonical discriminant function in Fisher80-55 models were given. Figure 5.14a–c represent the group distribution diagram of spectral bands, wavelet features, and combinations of spectral bands and wavelet features as inputs, respectively. For the overall accuracy, the differentiation accuracies of the two models based on the wavelet features with 92.7% and 90.4% were superior to the two models based on the spectral bands with 65.5% and 61.5%. The comparison between figure 5.14a and b can intuitively show the advantages of wavelet features in the differentiation of different stresses. The spectral bands for the two stresses had too much intersection, while the wavelet features for the two stresses showed different trends. These differences among wavelet features can not only consider position and scale, but also directly relate the spectral curve to the diseases. Therefore, the wavelet features can highlight the weak changes of spectral information to achieve the optimization of positions and scales. The classification results of Fisher80-55 models were slightly higher than those of leave-one-out cross validations, which may be related to the randomness of sample selection, but their accuracies did not differ greatly. It was found from the confusion matrix that the accuracy of spectral bands in identification powdery mildew was about 70%. Therefore, by combining spectral bands with wavelet features as input, it was found that the overall accuracies and kappa coefficients of these two models were improved to a certain extent. Moreover, in the Fisher80-55 model based on the combinations of spectral bands and wavelet features, the producer accuracies of powdery mildew and healthy samples increased by more than 10% compared with that of wavelet feature-based model. In figure 5.14c, it can be found more intuitively that the center of gravity distance between powdery mildew and healthy groups increased, and each class was more concentrated. When the classification accuracy of different diseases was concerned,

172

Fisher80-55 Powdery mildew

Spectral bands

Wavelet features

Combinations

Powdery mildew Yellow rust Healthy Sum Producer’s accuracy/% Powdery mildew Yellow rust Healthy Sum Producer’s accuracy/% Powdery mildew Yellow rust Healthy Sum Producer’s accuracy/%

Yellow rust

Healthy

Fisher leave-one-out cross validation Sum

Yellow rust

Healthy

Sum

User’s accuracy/%

Overall accuracy/%

Kappa

22

0

10

32

68.8

61.5

0.41

5 9 36

46 7 53

21 15 46

72 31 135

63.9 48.4

61.1

86.8

32.6

26

0

6

32

84.3

90.4

0.84

0 7 33

72 0 72

0 24 30

72 31 135

100 77.4

74.3

100

80.7

28

0

4

32

87.5

91.1

0.85

0 8 36

72 0 72

0 23 27

72 31 135

100 74.2

77.8

100

85.2

User’s accuracy/%

Overall accuracy/%

Kappa

Powdery mildew

65.5

0.47

11

0

4

15

73.3

1 3 15

17 4 21

7 8 19

25 15 55

68.0 53.3

73.3

81.0

42.1

14

0

1

15

93.3

0 3 17

25 0 25

0 12 13

25 15 55

100 80.0

74.3

100

80.7

14

0

1

15

93.3

0 2 16

25 0 25

0 13 14

25 15 55

100 86.7

87.5

100

92.9

92.7

94.6

0.89

0.92

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

TAB. 5.23 – Confusion matrix and classification accuracies.

Crop Pest and Disease Differentiation

173

FIG. 5.14 – Group distribution in Fisher80-55 models according to canonical discriminant functions.

yellow rust could be accurately identified in the models based on wavelet features, and the combination of spectral bands and wavelet features, and both user’s accuracy and producer’s accuracy reached 100%. For powdery mildew and healthy samples, in the model based on the combination of spectral bands and wavelet features, both user’s accuracy and producer’s accuracy were around 90%. Based on traditional spectral indices, wavelet features, and new indices extracted from hyperspectral data, this chapter constructs differentiation models of multiple stresses including wheat yellow rust, powdery mildew, aphids and nitrogen stress. The research results in this chapter demonstrate the potential of hyperspectral remote sensing technology in different crop stresses differentiation and support scientific field management.

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References Ahern F. J. (1988) The effects of bark beetle stress on the foliar spectral reflectance of lodgepole pine. Int. J. Remote Sens. 9, 1451. Broge N. H., Leblanc E. (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76, 156. Bruce L. M., Li J. (2001) Wavelets for computationally efficient hyperspectral derivative analysis. IEEE Trans. Geosci. Remote Sens. 39, 1540. Cheng T., Rivard B., Sánchez-Azofeifa G. A., et al. (2010) Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sens. Environ. 114, 899. Daughtry C., Walthall C., Kim M., et al. (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 74, 229. Kim M. S., Daughtry C. S. T., Chappelle E. W., et al. (2006) The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (A par), Proceedings of Symposium on Physical Measurements & Signatures in Remote Sensing Val D’isere, pp. 415– 434. Kira K., Rendell L. A. (1992) A practical approach to feature selection. Machine Learning Proceedings 1992, pp. 249–256. Ma H. Q., Huang W. J., Jing Y. S., et al. (2019) Integrating growth and environmental parameters to discriminate powdery mildew and aphid of winter wheat using bi-temporal landsat-8 imagery. Remote Sens. 11, 846. Mahlein A. K., Rumpf T., Welke P., et al. (2013) Development of spectral indices for detecting and identifying plant diseases. Remote Sens. Environ. 128, 21. Mclachlan G. J. (2004) Discriminant Analysis and Statistical Pattern Recognition. Wiley-Interscience. Merzlyak M. N., Gitelson A. A., Chivkunova O. B., et al. (1999) Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 106, 135. Naidu R. A., Perry E. M., Pierce F. J., et al. (2009) The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Comput. Electron. Agric. 66, 38. Penuelas J., Baret F., Filella I. (1995) Semi-empirical indices to assess carotenoids/chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica. 31, 221. Penuelas J., Gamon J. A., Fredeen A. L., et al. (1994) Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ. 48, 135. Penuelas J., Pinol J., Ogaya R., et al. (1997) Estimation of plant water concentration by the reflectance water index WI (R900/R970). Int. J. Remote Sens. 18, 2869. Pu R., Foschi L., Gong P. (2004) Spectral feature analysis for assessment of water status and health level in coast live oak (Quercus agrifolia) leaves. Int. J. Remote Sens. 25, 4267. Pu R., Ge S., Kelly N. M., et al. (2003) Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves. Int. J. Remote Sens. 24, 1799. Robnik-Šikonja M., Kononenko I. (2003) Theoretical and empirical analysis of ReliefF and RreliefF. Mach. Learn. 53, 23. Rondeaux G., Steven M. D., Baret F. (1996) Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 55, 95. Sankaran S., Mishra A., Ehsani R., et al. (2010) Review: A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72, 1.

Part Three

Imaging Remote Sensing Monitoring for Crop Pest and Disease

Imaging remote sensing technology is a new type of remote sensing technology developed in the early 1980s. It integrates the advantages of spectral detection and image detection, and has become a hot spot at home and abroad in recent years. It has obvious advantages in crop nutrient diagnosis, water monitoring, and pests and diseases diagnosis. In recent years, researchers have successfully used imaging remote sensing technology in the diagnosis of crop nutrients, pests and diseases, and have made preliminary progress and good results. From the perspective of spectral resolution, imaging remote sensing technologies are mainly divided into hyperspectral imaging technology and multispectral imaging technology. Due to the high cost of hyperspectral imaging technology, it seldom applies at the regional scale. In contrast, multi-spectral imaging technology, with lower cost and greater advantages, has wide application at the regional scale. With the development of aerospace technology, satellite image data are constantly enriched. How to use various satellite image data to monitor the occurrence and damage level of pests and diseases at regional scale is an important research topic now and one of the difficulties in remote sensing monitoring of pests and diseases. This section takes the main pests and diseases of wheat as examples. At the leaf scale, a series of experiments of pests and diseases monitoring have been carried out using imaging hyperspectral technology. Based on hyperspectral images, the images and subtle spectral features of pests and diseases on leaves were extracted and analyzed to provide a theoretical basis for pests and diseases monitoring using multispectral remote sensing at regional scale. In addition, multi-temporal and multispectral satellite remote sensing were used to dynamically grasp the occurrence and development of crop pests and diseases at the regional scale, providing important information for agricultural management and agricultural insurance. To carry out the monitoring of crop pests and diseases by imaging remote sensing technology, at regional scale pests and diseases observation experiments were added. The data involved in this part are as follows.

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

✧ Experiment 1: Leaf-scale imaging hyperspectral experiment of wheat powdery mildew The leaf-scale imaging hyperspectral experiment of wheat powdery mildew is an extension of the single-leaf and canopy spectrum experiment. Considering that the early filling period is an important time for the prevention and control of diseases, the experiment was carried out in the experimental field of Beijing Academy of Agriculture and Forestry Sciences on May 23, 2010 during the wheat filling period (figure III.1). In the field experiment, a total of 114 leaves with petioles were cut, including 34 healthy samples and 80 infested samples. The leaves need to be placed in ice packs immediately after they were cut to avoid water loss and physiological effects. After the sample was collected, it was immediately transferred to the room for leaf imaging spectrum measurement.

FIG. III.1 – Experimental field of wheat powdery mildew in Beijing. ✧ Experiment 2: Leaf-scale imaging hyperspectral experiment of wheat yellow rust The experiment was carried out in 2017 in Langfang City, Hebei Province (figure III.2). Seven observation tests on 20 April, 27 April, 4 May, 11 May, 15 May, 18 May, and 25 May were conducted during the critical growth periods of wheat, including jointing, heading, flowering and filling periods. The healthy and yellow rust-infested leaves with different damage levels were collected. In the first three periods (20 April, 27 April, and 4 May), 12 leaves were collected in each period, and in the last four periods (11 May, 15 May, 18 May, and 25 May), 15 leaves were collected in each period. A total of 96 leaves were obtained. Table III.1 shows the number of healthy and yellow rust-infested wheat leaves obtained during each period. Two-thirds of the leaves were used as the modeling set, and the rest as the verification set. The second leaf from the top on the wheat stalk was manually clipped for this experiment. To avoid water loss in leaves caused by high

Imaging Remote Sensing Monitoring for Crop Pest and Disease

179

temperatures during transportation, the leaves were put into a box with ice bags to keep them fresh. Subsequently, the leaves were scanned by hyperspectral imaging instruments.

FIG. III.2 – Langfang experimental site and hyperspectral imaging system.

TAB. III.1 – The number of healthy and yellow rust-infested wheat leaves obtained during each observation period.

Healthy Yellow rust Sum

20 April 8 4 12

27 April 6 6 12

4 May 6 6 12

11 May 7 8 15

15 May 7 8 15

18 May 5 10 15

25 May 6 9 15

✧ Experiment 3: Leaf-scale imaging hyperspectral experiment of wheat aphid The wheat aphid imaging hyperspectral experiment was carried out on May 25, 2010 at the Xiaotangshan National Precision Agriculture Demonstration and Research Base in Changping District, Beijing (figure III.3). Initially, 30 wheat plants with different levels of aphid damage were selected visually. To ensure that the sampling leaf water would not lose over time and affect the test results, the samples were immediately put into plastic tapes and sent to the laboratory for imaging spectrometry. The collected leaves were the penultimate leaves of wheat, and leaves with different amounts of aphids were selected from the obtained wheat plants, and every 5 leaves were grouped for hyperspectral imaging. Before leaf imaging, we firstly numbered the leaves with labels beside them, and then counted and recorded the number of aphids on each leaf. After imaging the leaves are imaged, we used a soft brush to remove all aphids from the leaves. According to the leaf area occupied by the honeydew excreted by aphids, the rate of aphid damage area was estimated.

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FIG. III.3 – Experimental field in Xiaotangshan Precision Agriculture Experimental Base.

TAB. III.2 – Investigation results of leaf aphid quantity and damage area ratio. ID Investigated aphid quantity Investigated aphid area rates

No. 1 14 20%

No. 2 8 40%

No. 3 24 45%

No. 4 16 80%

No. 5 26 50%

The damaged area rate were then recorded according to the serial number. The record result is shown in table III.2. ✧ Experiment 4: UAV imaging hyperspectral experiment of wheat yellow rust UAV imaging hyperspectral experiment of wheat yellow rust was carried out at the Langfang Scientific Research Base (39° 30.48′ N, 116° 36.14′ E) of the Chinese Academy of Agricultural Sciences, Hebei Province in Year 2018 (figure III.4). The S185 airborne push-broom hyperspectral imager was used to obtain the hyperspectral image of the wheat. S185 is the first domestic full-frame, real-time imaging airborne hyperspectral imaging system with short exposure time and fast integration time. Seven experiments were conducted from April 20 to May 25, including key growth periods such as jointing, heading, and filling. In each experiment, representative plots were taken for canopy spectroscopy based on the occurrence of disease, and the severity of disease and growth period information were recorded. At the same time, physiological and biochemical measurements including plant height, leaf area index (LAI), chlorophyll content, and NBI, anthocyanins were obtained. In each period, we simultaneously acquired UAV hyperspectral images. The flying height of the UAV was 30 m. The experiment conducted a total of 6 field surveys on disease severity from April 25 to May 30, and the wheat field-scale hyperspectral images were acquired at the same time. The experimental investigation mainly recorded indicators such as wheat growth status, planting density and the disease severity.

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FIG. III.4 – UAV hyperspectral imaging system. The severity of yellow rust was investigated according to the national standard “Technical Specification for Forecasting and Reporting of Wheat Yellow Rust” (GB/T 15795-2011). The follow-up studies were mainly divided into two categories of healthy wheat (DI ≤ 5%) and yellow rust infested (DI > 5%) wheat for monitoring. As yellow rust infested wheat with DI ≤ 5% was visually indistinguishable from healthy wheat, it was classified as healthy wheat in this study. ✧ Experiment 5: Aerial imaging hyperspectral experiment of wheat yellow rust To understand the image and spectral features of wheat yellow rust, a set of synchronous experimental data of wheat yellow rust were obtained at the Xiaotangshan Precision Agriculture Demonstration Base in Beijing in 2002 (figure III.5). The used PHI hyperspectral sensor was an area array push-broom imaging spectrometer, developed by the Shanghai Institute of Technical Physics, Chinese Academy of Sciences. The sensor covered the wavelength range of 400–850 nm and formed a hyperspectral image with a spectral resolution of less than 5 nm and a spatial resolution of about 1 m (corresponding to a flight altitude of 1000 m). A total of 3 flight experiments were carried out on April 18, May 17, and May 31 in the year of 2002, corresponding to the periods of wheat jointing, filling, and milking, respectively. The aerial image was preprocessed for sensor calibration, radiation correction, reflectance conversion and geometric correction, and abnormal waveband investigation. In addition, the corresponding ground canopy spectroscopy, and the investigation of the severity of yellow rust were carried out simultaneously with the three flight times. ✧ Experiment 6: Regional experiment of wheat yellow rust To carry out research on the construction of the spectral knowledge base construction of wheat diseases, a set of satellite-to-ground disease survey data were collected in the southeast of Gansu Province from June 1 to 3, Year 2009, with the help of the staff from Gansu Plant Protection Station (figure III.6). The study area

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FIG. III.5 – Schematic diagram of the machine-ground synchronous test field and ground survey points.

FIG. III.6 – Distribution of verification points in Gansu.

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had the characteristics of high temperature and high humidity, and was a typical high-incidence area of wheat yellow rust in China. In Year 2009, wheat yellow rust was moderately prevalent in this area. Two HJ-CCD multispectral images (Path/Row: 122/516, 122/518) were obtained on June 2, Year 2009. The ground survey was carried out from June 1st to June 4th, and a total of 26 sample plots with relatively scattered spatial distribution were selected. The survey area of each plot was a continuous wheat planting area with a diameter of more than 30 m. Trimble GeoXT differential GPS was used to record the longitude and latitude coordinates of the center of each plot. The survey content of the sample plot was the severity of yellow rust. ✧ Experiment 7: Regional experiment of wheat powdery mildew Wheat powdery mildew usually occurs in the field plots, which is an ideal disease type for satellite remote sensing monitoring. The experimental area was in the Shunyi and Tongzhou districts of Beijing (figure III.7). At the same time, the wheat planting structure in this area was relatively simple and the planting area was large, which was more suitable for the application of medium-resolution remote sensing data for disease monitoring. According to the experience of the plant protection department and the general rule of disease canopy symptoms, the investigation period was selected from April 30 to May 28, 2010, involving the key wheat growth

FIG. III.7 – Diagram of sample distribution in Beijing Research Area.

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periods of jointing, filling, and booting. After a comprehensive query of the images during the key growth periods in the study area, 6 scenes of HJ-CCD and 3 scenes of HJ-IRS were downloaded. After image preprocessing, mosaicking, and clipping, the HJ-CCD image data of 4 phases and the HJ-IRS image data of 3 phases in Beijing research areas were obtained, respectively. The acquisition of ground survey data synchronized with remote sensing images is of great significance to the training and verification of the model. According to the acquisition time of the satellite data, a set of ground survey data matched with multi-temporal HJ-CCD images was obtained in the experimental area. A total of 4 surveys were conducted in the study area. The time of each survey was two days before and after the satellite image acquisition time, except that the first survey was 4 days later than the image acquisition time of the same period. As the symptoms of the early disease changed relatively slowly and the symptoms of the disease appeared relatively mild, the survey data were still used as the ground reference data for the corresponding image. All selected samples were a contiguous wheat planting area with a diameter of more than 30 m. The content of the investigation included wheat infested area and disease severity. At the same time, the varieties and plant types of wheat in the survey area were recorded, and the planting density of wheat was measured. A total of 90 survey samples were selected, 54 of which were used for model training and 36 were used for model verification. The distribution of samples is shown in figure III.8. ✧ Experiment 8: Regional experiment of wheat aphids The research area of the wheat aphid satellite-ground synchronization experiment was selected in the suburbs of Beijing. The key research areas were Tongzhou and Shunyi in Beijing (figure III.8). The wheat planting structure in this area was relatively simple with large area, which was more suitable for the application of medium-resolution remote sensing data for wheat aphid monitoring. The aphid damage level survey mainly corresponded to the obtained medium-resolution remote sensing data, which provided data support for the research on the medium-resolution satellite remote sensing to monitor the damage level of wheat aphid and predict the occurrence probability. Therefore, we chose to carry out the aphid survey in the field before and after the image acquisition time. In addition, since the acquired medium-resolution images were mainly Landsat 5 TM and HJ-CCD, and the spatial resolution of the images was 30 m, the size of the selected survey sample site must be greater than 30 m × 30 m. The survey adopted a 5-point survey method; each survey point had an area of 2 m2, and 5 wheat plants were selected in the area of 30 m × 30 m. The average number of aphids on the first top leave, second top leave, third top leave and the ear of each wheat plant was taken as the aphid quantity survey result. Then the aphid damage level of the survey point was determined according to the classification standard of aphid damage level, and the longitude and latitude of the center point of the sample plot were recorded by using GPS. We conducted a total of 7 surveys, and the survey time was May 4, May 10, May 12, May 20, May 21, June 4, and June 5, Year 2010. A total of 70 samples were obtained, including 50 random survey points and 20 regional

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FIG. III.8 – Distribution diagram of study area and survey points.

designated survey points. The intensive survey points were to study the changes of wheat aphids over time. Image data pre-processing The acquired hyperspectral and multispectral images need to be pre-processed before the analysis of spectral characterization of crop pests and diseases. Data pre-processing mainly included: 1. Pre-processing of hyperspectral imaging data The imaging spectrometer system is composed of Pushbroom Imaging Spectrometer (PIS), electronically controlled translation stage and controller, adjustable halogen element light source, computer, etc. The working diagram of the system is shown in figure III.9. The instrument was jointly developed by the University of Science and Technology of China and Beijing Academy of Agricultural and Forestry Sciences. The spectrometer was rigorously tested and calibrated by the National Technological

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FIG. III.9 – Schematic diagram of hyperspectral imaging system.

Innovation Laboratory of Optical Radiation Calibration and Labelling at the Anhui Institute of Optics and Precision Mechanics, Chinese Academy of Sciences before use. The instrument’s imaging spectrometer has a wavelength range of 400–1000 nm, spectral resolution of 2 nm, and spatial resolution of 0.5 mm–2 mm (this test resulted in images with a spatial resolution of 1 mm). The image size is 1400 (spatial dimension) × 1024 (spectral dimension), the spectral sampling interval is 0.7 nm, and the field of view of the spectrometer is 16°. Before performing hyperspectral imaging of leaves, the height of the instrument needs to be fixed according to the imaging results. In this experiment, the lens was 380 mm away from the motorized translation stage, the light source was 300 mm away from the stage at a 45°, and the appropriate speed of the motorized translation stage was 2.3 mm/s. When setting the parameters of the imaging acquisition system, the optimal exposure time and frame rate were set to 100 ms and 9 ftps, respectively. The leaves of the wheat are laid on a black cloth and a reference plate is placed in the view of the spectrometer. As the motorized translation stage moves at a constant speed, the spectrometer acquires hyperspectral data from both the leaves and the reference plate, and each of these images contains 1024 spectral bands. The collected hyperspectral images are stored in BMP format in the computer. In order to extract and analyze the spectral features, the DN values of spectral images need to be converted to reflectance. In this experiment, the original images were stitched into BIL format by MATLAB software, and the reflectance conversion was done by the empirical linearity module in ENVI software. The images contain noise due to the uneven response of the dark current of the spectrometer at each band. This experiment used the S-G convolutional smoothing module in Origin software for spectral denoising. At the same time, the noisy bands were removed for research purposes. The final band used in this experiment was 450–900 nm.

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To enhance the spectral characteristics of the leaves under different stress states and to eliminate the differences in reflectance due to light, a normalized reflectance method was applied to the reflectance. The principle was to calculate the spectral mean value for each pixel and divided it by the spectral reflectance of each band, as shown in equation III.1. The final normalized reflectance value was between [0, 2.5]. rj ðIII:1Þ Rj ¼ 1 Pk  i¼1 ri k where Rj denotes the normalized reflectance, ri, rj, denotes the ith and jth band of the original reflectance respectively, and k denotes the total number of bands. 2. Pre-processing of multispectral imaging data After acquiring the images needed for the study, the first step was to pre-process the images. Pre-processing mainly includes atmospheric correction, geometric correction and image clipping. Image pre-processing is the basis of at regional-scale remote sensing monitoring, and the results of image processing affect the final monitoring results. All of the multispectral data covered in this section were preprocessed, including radiometric correction, geometric correction, and image clipping. (1) Atmospheric correction For atmospheric correction of Landsat TM, this chapter uses the ENVI software and its embedded FLAASH atmospheric correction module to perform radiometric calibration and atmospheric correction of Landsat TM (minus the thermal infrared band) images, respectively, in conjunction with the Landsat TM metadata header file information. For the acquired ambient star multispectral data (hereafter referred to as HJ-CCD), the atmospheric calibration is special compared to TM due to its lack of short-wave infrared band. The radiometric calibration was performed first, using the following equation: L ¼ DN =a þ L0

ðIII:2Þ

In the formula, L is the radiated brightness value, α is the absolute calibration factor gain, and L0 is the offset. The unit of the converted radiated brightness is W•m−2•sr−1•μm−1. The gain and offset of each band are extracted from the original image header file. Converting image radiated brightness to reflectance requires atmospheric correction of the image. Due to the difficulty in obtaining accurate atmospheric parameters, this chapter uses the improved dark object methods proposed by Liang et al. (2003) to correct the images. By creating a look up table, estimating aerosol optical thickness, correcting for proximity effects, and inversion of surface reflectance, the method can reflect the spatial distribution of aerosols more accurately, thus to effectively reduce the influence of atmospheric environmental differences on the spectrum.

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(2) Geometric correction The geometric correction of Landsat 5 TM images was done by using the aerial image which has been corrected by differential GPS control points as the reference image. The selected reference points were evenly distributed across the image to ensure that the geographic location error was within 0.5 pixel. The geometrically corrected Landsat 5 TM data were then used as the reference image, and the three-time polynomial geometric correction method was used to correct the other HJ-1B CCD images, Landsat 5 TM images and all thermal infrared images to ensure that the geometric correction accuracy was better than 0.5 pixel.

Chapter 6 Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease Hyperspectral imaging technology is based on narrow-band imaging technology, which combines imaging technology with spectral technology to detect two-dimensional geometric spatial information and one-dimensional spectral information of the target, and obtain continuous, narrow-band image data with high spectral resolution. This chapter analyzes the physiological changes of crops under stress from the manifest symptoms of crop pests and diseases. Based on the spectral sensitivity range of crop pests and diseases in part two, the spectral features required for pests and diseases monitoring are acquired, and some texture features are also extracted and used as input together with spectral features to construct the remote sensing monitoring model for crop pests and diseases. The research results in this chapter provide a reference for scholars to use imaging remote sensing technology for pests and diseases monitoring at leaf scale and canopy scale.

6.1

Wheat Yellow Rust Monitoring

The typical symptoms of wheat yellow rust mainly occur on the leaves. The loss of green and yellowing of the leaves are the most obvious characteristics of yellow rust. The imaging hyperspectral data have many bands and carry a large amount of target feature information. Compared with wide-band and multi-band remote sensing data, it is easier to identify pests and diseases with hyperspectral bands. Moreover, using imaging hyperspectral data to analyze the spectral and image characteristics of wheat yellow rust can provide a basis for aerial and aerospace remote sensing monitoring of wheat yellow rust. This section mainly uses Headwall imaging hyperspectral data, UAV hyperspectral data and PHI imaging hyperspectral data to monitor wheat yellow rust.

DOI: 10.1051/978-2-7598-2659-9.c006 © Science Press, EDP Sciences, 2022

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Monitoring at Leaf Scale

1. Feature extraction and selection 1) Extraction of spectral data and selection of optimum wavebands Spectral reflectance of wheat yellow rust was extracted by setting up regions of interests (ROI) in leaf. Each leaf has three ROIs with 10 × 10 pixels. The average reflectance of each ROI represents the spectral data of one sample. We acquired 288 samples with 147 infested samples and 141 healthy samples. The headwall hyperspectral images contained 406 wavebands. To reduce data redundancy and improve model accuracy, it is very necessary to extract the optimal wavebands (OWs) from hyperspectral images. The successive projections algorithm (SPA) is a forward variable selection method that selects subsets of variables with minimum redundancy, which has been proven to have a better ability in waveband selection. Therefore, the SPA was used to select the OWs in this study. Finally, 538 nm, 598 nm, 689 nm, 703 nm, 751 nm, and 895 nm were selected by the SPA for establishing identification model of yellow rust. 2) Extraction of vegetation indices The previous studies have proved that VIs have good performance to identify crop diseases (Zheng et al., 2018; Zhang et al., 2012). Fourteen VIs were used to identify wheat yellow rust, which were selected from relevant literatures. These VIs included SIPI, PRI, TCARI, NPCI, PSRI, PhRI, ARI, MSR, RVSI, YRI, GI, TVI, NRI, and NDVI. Tables 1.1, 2.1, 4.5, 5.1 and 5.10 showed the formula of these VIs. 3) Extraction of textural features The images' texture is characterized by the relationship of the intensities of neighboring pixels and represents the biophysical characteristics of the leaves, including the intensities, roughness, and their arrangements (Fu et al., 2017; Zhang et al., 2012). Crop diseases not only cause changes of the pigment and structure in the leaves, but also changes of the color and morphology in the leaf surface, thereby causing texture changes. In our research, the GLCM was used to extract texture features. Haralick (1973) proposed fourteen textural features (TFs) based on GLCM. Eight frequently used TFs were selected to identify yellow rust, including mean (MEA), variance (VAR), homogeneity (HOM), contrast (CON), dissimilarity (DIS), entropy (ENT), second moment (SEC), and correlation (COR). Table 6.1 lists the descriptions and equations of the TFs. TFs were extracted from the image obtained from the original image through principal component transformation. A total of 24 TFs were acquired from the first three PC images. 4) Selection of optimal vegetation indices and textural features Fourteen VIs and twenty-four TFs were extracted as alternative features for identifying wheat yellow rust. However, excessive features could cause data

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 191 TAB. 6.1 – Grey-level co-occurrence matrix (GLCM) textural features used in this study. Textural Features Mean (MEA)

Variance (VAR) Homogeneity (HOM) Contrast (CON) Dissimilarity (DIS) Entropy (ENT) Second moment (SEC) Correlation (COR)

Description The Mean is the average grey level of all pixels in the matrix. The Variance describes the rate of change of the pixels’ values. The Homogeneity indicates the uniformity of the matrix. The Contrast represents the local variations in the matrix. The Dissimilarity reflects the difference in the grayscale. The Entropy expresses the level of disorder in the matrix. The Second Moment represents the uniformity degree of the grayscale. The Correlation is a measurement of image linearity among the pixels.

Equation MEA =

VAR ¼

DIS ¼

ENT ¼ 

i¼1

j¼1

ði  lÞ2 Pði; jÞ

Pði;jÞ j¼1 1 þ ðijÞ2

i¼1

i¼1

j¼1

PG PG i¼1

j¼1

PG PG i¼1

j¼1

ði  jÞ2 Pði; jÞ

Pði; jÞji  jj

Pði; jÞ log Pði; jÞ

PG PG i¼1

PG PG i¼1

iPði; jÞ

PG PG

PG PG

SEC ¼

COR ¼

i;j¼1

PG PG

HOM ¼

CON ¼

PG

j¼1

j¼1

P 2 ði; jÞ

ðiMEAj ÞðjMEAj ÞPði;jÞ

pffiffiffiffiffiffiffiffiffiffiffiffipffiffiffiffiffiffiffiffiffiffiffiffi VARi VARj

Note: In the formulation, i is the row number of the image; j is the column number of the image; P(i, j) represents the relative frequency of two neighboring pixels.

redundancy (Cao et al., 2018). Therefore, CFS was employed to choose the optimal VIs and TFs for identifying wheat yellow rust. Finally, four TFs (COR1, COR2, ENT2, and SEC3) and four VIs (NRI, PRI, GI, and ARI) were selected. In addition, we used independent t-test to test the ability of the selected features by CFS for identifying yellow rust. The results showed that there were significant differences in the mean values and standard deviations of these four VIs and four TFs between the healthy and yellow rust-infested samples, and all features selected by the CFS were significant at the 0.999 level, as shown in table 6.2. The optimal features, which were the most sensitive to wheat yellow rust, were used in the SVM classifier to identify wheat yellow rust.

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TAB. 6.2 – Summary statistics of VIs and TFs selected by the CFS. Mean Features

VIs

TFs

Healthy NRI PRI GI ARI COR1 COR2 ENT2 SEC3

0.264 0.084 1.858 −2.038 0.241 0.140 0.279 1.503

Standard deviations Yellow rust 0.116 0.260 1.222 0.883 0.353 0.114 0.462 1.950

Healthy 0.074 0.068 0.326 1.489 0.120 0.088 0.158 0.429

Yellow rust 0.065 0.092 0.231 0.949 0.101 0.017 0.159 0.160

Significance of t-test *** *** *** *** *** *** *** ***

Note: *** Indicates that the mean difference is significant at the 0.999 level.

2. Wheat yellow rust monitoring The OWs, VIs, TFs and their combinations were used to establish identification model of wheat yellow rust. And the performances of the models using the different features were compared. Finally, five groups of features were input into the SVM classifier to evaluate the performance for identifying wheat yellow rust. The features included (1) OWs (538 nm, 598 nm, 689 nm, 703 nm, 751 nm, and 895 nm), (2) VIs (NRI, PRI, GI, and ARI), (3) TFs (COR1, COR2, ENT2, and SEC3), (4) combination of OWs and TFs, and (5) combination of VIs and TFs. The results of the identification model established by different features are listed in table 6.3. The OA and kappa based on OWs were 83.3% and 0.667. The identification accuracy of the healthy and yellow rust-infested samples were 79.2% and 88.4%. These results proved that it is feasible to identify wheat yellow rust based only on the OWs. For the VIs, the OA and kappa were 89.5% and 0.789, respectively. Compared with the OA of OWs, the VIs was 6.2% higher than the previous level. For the TFs, the OA and kappa were 86.5% and 0.73, respectively. The OA of the TFs was 3% lower than that of the VIs, but 3.2% higher than that of the OWs. These results indicated that it is feasible to identify yellow rust of wheat leaves using TFs of hyperspectral images. The OA and kappa of the combined features were higher than that of the single feature. For the OWs + TFs, the OA and the kappa were 90.6% and 0.812, respectively. The OA was 7.3% and 4.1% higher than OWs and TFs, respectively. The VIs + TFs obtained the best performance with an OA of 95.8%. Compared to the OA of VIs and TFs, the VIs + TFs increased by 6.3% and 9.3% respectively. In addition, the U of the healthy samples was 100%, and that of the yellow rust-infested samples was 92.5%. The results demonstrated that the model based on the combined features resulted in the highest OA for the identification of wheat yellow rust. The results of the VIs + TFs scheme are shown in figure 6.1. The lesions in the wheat leaves with different damage levels were accurately identified.

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 193 TAB. 6.3 – Comparison of the results of the identification model using the SVM based on different features. Input feature

OWs

VIs

TFs

OWs + TFs

VIs + TFs

6.1.2

Yellow rust Healthy Sum P/% Yellow rust Healthy Sum P/% Yellow rust Healthy Sum P/% Yellow rust Healthy Sum P/% Yellow rust Healthy Sum P/%

Yellow rust

Healthy

Sum

U/%

OA/%

Kappa

38

11

49

77.5

83.3

0.667

5 43 88.4

42 53 79.2

47 96

89.4

48

1

49

98.0

89.5

0.789

9 57 84.2

38 39 97.4

47 96

80.9

42

7

49

85.7

86.5

0.73

6 48 87.5

41 48 85.4

47 96

87.2

44

5

49

89.8

90.6

0.812

4 48 91.7

43 48 89.6

47 96

91.5

49

0

49

100

95.8

0.916

4 53 92.5

43 43 100

47 96

91.5

Monitoring at Canopy Scale

1. Monitoring with UAV airborne hyperspectral images We used UAV hyperspectral low-altitude observation data to construct an appropriate model for monitoring the disease severity of wheat yellow rust. Similar to the leaf scale, the biophysical parameters of the canopy are the key indicators for identifying pests and diseases. We coupled the wavelet feature and the vegetation index feature as a new feature to identify the canopy-scale yellow rust. The specific goals were: (1) to select wavelet features (WFYs) based on continuous wavelet transform and vegetation index features (VIs) to characterize the spectral changes caused by yellow rust at different stages; and (2) to propose wheat yellow rust monitoring model based on WFYs and VIs by using UAV images.

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FIG. 6.1 – Extraction of yellow rust-infested areas on wheat leaves with different damage levels using the model based on the combination of VIs and TFs.

1) Feature extraction and selection In this section, the continuous wavelet transform was used to extract the wavelet features of yellow rust. The acquired ASD non-imaging hyperspectral data and UAV hyperspectral imaging data were used to extract wavelet features which were sensitive to yellow rust, and the wavelet transform energy spectrum is shown in figure 6.2. Correlation analysis between the extracted wavelet features and the severity of yellow rust was performed, and the wavelet features with a correlation coefficient greater than 0.8 were selected. A total of 5 wavelet features were obtained, which were mainly distributed in the blue band (470–485 nm), green band (520–600 nm) and red band (630–760 nm). To supplement the insufficiency of wavelet features in characterizing canopy structure, we introduced the vegetation index to comprehensively characterize the pathological symptoms caused by yellow rust. The vegetation indices included: (1) MSR that had a good response on crop growth, (2) SIPI that was sensitive to changes in crop canopy structure, (3) RVSI that had a good response to water content, (4) PHRI that responded well to photosynthesis absorption, (5) NDVI that responded well to vegetation coverage, and (6) YRI that had a strong correlation with the degree of crop stress. The wavelet features and spectral indices used in this section are shown in table 6.4.

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FIG. 6.2 – Wavelet feature extraction of wheat yellow rust at the canopy scale. TAB. 6.4 – Spectral features and wavelet feature sets used and filtered in this section. WFs WFY01 WFY02 WFY03 WFY05 WFY06

Bands and scale R481 (s = 24) R543 (s = 23) R574 (s = 23) R689 (s = 25) R742 (s = 24)

VIs MSR SIPI PhRI RVSI NDVI YRI

Expression ðR800 =R670  1Þ=sqrtðR800 =R670 þ 1Þ ðR800  R445 Þ=ðR800  R680 Þ ðR550  R531 Þ=ðR550 þ R531 Þ ððR712 þ R752 Þ=2Þ  R732 ðR830  R675 Þ=ðR830 þ R675 Þ ðR730  R419 Þ=ðR730 þ R419 Þ þ 0:5R736

2) Wheat yellow rust monitoring by coupling wavelet features and vegetation indices In UAV hyperspectral canopy disease monitoring, the observation background is different due to the influence of imaging background and soil conditions, which greatly increases the heterogeneity of hyperspectral data and increases the collinearity of the initial feature set. To better weaken the influence of imaging background on canopy hyperspectral observations, this study proposed SVM based on nuclear principal component analysis (KPC-SVM) to identify wheat yellow rust at canopy scale. To evaluate the monitoring capabilities of the model, we established the KPC-SVM model based on WFYs and VIs, and compared it with the traditional SVM model. The algorithm flow of kernel principal component analysis (KPCA) was proposed as follows. First, the original sample space was transformed through a certain nonlinear mapping. Then, PCA was performed on the samples in the newly generated feature space, and the dot product operation in the training sample space was replaced by the kernel function that satisfies the Mercer condition, which effectively avoided the “dimension disaster”. Therefore, the new sample features transformed by the KPCA kernel function not only had the algebraic characteristics of the principal components, but also effectively retained the physical and chemical parameter information represented by the original features. KPCA provided more components than linear space through feature mapping and principal component

196

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extraction, which further improved the feature recognition ability. In terms of computational efficiency, the computational complexity of KPCA did not increase due to the introduction of the feature mapping process, it is only related to the dimension of the original sample space, and had nothing to do with the dimension of the mapped feature space. The kernel principal component features have the following properties. (1) Due to the linear nature of the feature space after the kernel mapping and the orthogonality of the transformation, it is easy to conclude that any sample belonging to the kernel feature space has a unique feature vector. That is, the dimensional transformation of the kernel feature space does not lose the main physical and chemical information of the original feature. (2) The nuclear principal component feature has spatial heterogeneity. When the feature vector is disturbed by noise, the corresponding nuclear principal component feature interference will be less than the spatial interference. (3) The high-dimensional mapping of the kernel function increases the spatial dimension, and the spatial distance between clusters, thereby reducing the complexity of classification and reducing the probability of misclassification. Before establishing the KPC-SVM model, the input features should satisfy three principles, i.e. sensitivity, independence, and significance. Therefore, it is necessary to test the significant relationship between the newly generated nuclear principal component features and the biophysical parameters related to yellow rust. The determination coefficient between the nuclear principal component features and the physical and chemical parameters was calculated by correlation analysis to quantify the sensitivity of each nuclear principal component feature to specific biophysical attributes. The univariate correlation analysis results between the first five nuclear principal component features and sample parameters included NBI, chlorophyll index (CHL), anthocyanin index (ANTH), percentile dry matter (PDM). The results showed a significant linear correlation between KPF01 and PDM (R2 = 0.82, p < 0.05). KPF02 and KPF03 had a significant correlation with CHL, as R2 values at 0.77 and 0.79, respectively. The R2 values of KPF02 and KPF03 to ANTH were 0.68 and 0.74, respectively. KPF04 has a significant correlation with NBI and PDM, as R2 values at 0.71 and 0.72, respectively. KPF05 and NBI had a significant correlation with R2 at 0.76. The kernel principal component features were input into the SVM classification framework to establish a yellow rust identification model, and the effectiveness of the two models in yellow rust monitoring was compared. Table 6.5 shows the classification accuracy evaluation of kernel principal component support vector machine (KPC-SVM) and traditional SVM. The results showed that the overall accuracy of the KPC-SVM based on the kernel principal component was 15.2% higher than the SVM classifier. The reason may be that KPC-SVM’s unique non-linear feature space mapping capability based on the kernel function enabled the sample space to have greater inter-class distance and separability in the mapped feature subspace. The KPC-SVM classifier was applied to UAV hyperspectral data, the classification effect and accuracy evaluation are shown in table 6.6, and the mapping of wheat yellow rust in different periods is shown in figure 6.3. The results showed that the correct classification accuracy of WFYs-SVM for the lesions of yellow rust was 84.2%–95.2% before obvious yellow rust spore colonies appeared, and leaf symptoms

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 197 TAB. 6.5 – Classification confusion matrix of KPC-SVM and traditional SVM. Classifier SVM

KPC-SVM

Yellow rust Healthy P/% Yellow rust Healthy P/%

Yellow rust 106 41 72.1 126 21 85.7

Healthy 28 66 70.2 10 74 88.1

U/% 79.1 61.7

OA/% 71.4

Kappa 0.79

92.6 77.9

86.6

0.86

TAB. 6.6 – Classification of yellow rust based on UHD-185 hyperspectral imaging. Classification error rate Yellow rust Healthy

7 dai 88.7 84.2

14 dai 92.4 90.1

21 dai 97.5 95.3

28 dai 99.2 97.9

31 dai 98.8 100

34 dai 96.7 100

41 dai 98.9 98.2

FIG. 6.3 – Hyperspectral imaging identification map for yellow rust in different infection periods based on KPC-SVM classifier.

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appeared on the 21st day after inoculation. Due to the high spatial resolution obtained by hyperspectral images, the classification accuracy was steadily improved. 2. Dynamic monitoring of wheat yellow rust with PHI hyperspectral image 1) Comparative analysis of spectral features of healthy and infested sample We superimposed healthy and infested points on the three PHI images, and analyzed the dynamic changes of their spectral features at different growth periods. To highlight the difference between wheat and the surrounding environment, a false color synthesis was performed in the near-infrared band of 783.5 nm, the red band of 682.4 nm, and the green band of 551 nm. As can be seen from figure 6.4, the wheat and roadside trees were red, and the surrounding roads and bare land were gray. The spectral curves of two points in each growth period were extracted for comparison. Due to the existence of system noise, the obtained spectral curve has serious jagged, which affected the spectral characteristics of wheat. Adjacent-averaging with a 2-point window was used to smooth the spectrum curves, and finally the spectrum curve of three growth periods were obtained. It can be seen from figure 6.4a that the image tone is bright red. This is because the image was acquired on April 18, which was just 18 days after the vaccination date of the yellow rust. The wheat had just been infested by yellow rust, and the disease features had not yet appeared at the canopy scale. By comparing the infested and healthy samples, it was found that image colors were very similar, but there were differences in spectral reflectance, especially in the near-infrared range of

FIG. 6.4 – Comparison of image and spectrum curves of healthy and infested samples in different growth stages.

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 199 700–800 nm. Due to the wheat had just been attacked by pathogens, the reflectance of infested spots was slightly lower than healthy spots in the near infrared range. It can be seen from figure 6.4b that the wheat in the control area and the field area were still red, but slightly black; most of the wheat in the inoculated plots were dark red, indicating that the disease had severely invaded the wheat. For the spectral reflectance curves, the healthy and infested points were very different. The spectral curve of the healthy sample had a reflection peak in the green band at 570 nm, an absorption valley in the red band at 670 nm, and high reflectivity in the near-infrared range from 700 nm to 800 nm, which was in line with the typical reflectivity characteristics of green vegetation. However, the green peaks of the infested samples disappeared, the red valleys rose or even became flattened, and the reflectance dropped sharply in the near-infrared range from 715 nm to 800 nm. On the milky maturity period (figure 6.4c), the image color was dark red. At this time, the wheat lost its green color and began to enter the mature period. The image colors of healthy and infested samples were basically the same. However, the spectral curves showed differences. The overall spectral curve of the healthy sample also showed the typical shape of vegetation, but the spectral curve of the infested sample was close to that of the soil, indicating that the yellow rust has already seriously damaged wheat plants. Spectral parameters shown in table 6.7 were selected to quantitatively analyze the difference between healthy and infested samples. The results showed that during the jointing period, because the wheat had just been infested with yellow rust, the difference between the six parameters of the healthy and infested points was small. But as the growth period progressed, the differences of these parameters were gradually enlarged. In the three wavebands of Blue, Green and Red, because yellow rust destroyed wheat pigments and reduced the absorption of chlorophyll, the spectrum value of infested sample was greater than the healthy one. However, in the near-infrared region, this trend was opposite, as yellow rust destroyed the structural organization of wheat leaves, and reduced the photosynthesis of the leaves. Compared with the three spectral bands of Blue, Green and Red, the values of NIR, NDVI and PRI were reduced. 2) Analysis of spectral differences with wheat yellow rust Wheat yellow rust causes damages mainly to wheat leaves, but to leaf sheaths, stems, and ears as well. After the wheat becomes infested, it begins to show the loss of green, and then forms yellow sores. The summer spores are small, which are arranged in strips on the leaves and parallel to the veins. The black, ambushing sores under the epidermis are formed in the later stages. Compared with healthy leaves, the infection of yellow rust causes changes in the pigment, tissue structure, and water content of wheat leaves, resulting in changes in the spectrum of visible light, near-infrared and short-wave infrared. In the wheat canopy, the same infestation characteristics will also be shown. 3) Dynamic monitoring of wheat yellow rust To use PHI images to monitor wheat yellow rust on field scale, it is necessary to select spectral bands which are sensitive to yellow rust for constructing a monitoring

Spectrum parameter

Jointing stage (4/18) Image

Infested

Blue (457.2 nm)

5.55

Green (570 nm)

Filling stage (5/17)

Milk stage (5/31)

Healthy

Infested

4.97

2.98

9.03

9.88

Red (682.4 nm)

9.47

NIR (750.1 nm)

NDVI

PRI

Image

Image

Healthy

Infested

3.64

3.32

6.53

6.89

7.83

10.57

15.22

10.84

2.80

8.45

9.86

20.61

33.37

32.61

54.20

24.87

46.54

37.80

0.561

0.528

0.909

0.492

0.726

0.294

−0.088

−0.053

0.011

−0.141

−0.014

−0.155

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Healthy

200

TAB. 6.7 – Comparison of healthy and infested spot images and spectral curves on multitemporal PHI images.

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 201 model. According to previous studies using PHI images to monitor yellow rust, the spectral features of yellow rust on PHI images were obtained: (1) in the red band (560–670 nm), the reflectance of infested wheat canopy was higher than that of the healthy counterpart; and (2) in the near-infrared band (700–1100 nm), this trend was the opposite. We selected the average values of the red and near-infrared bands in the range of 620–718 nm and 770–805 nm of the PHI image as two independent variables, and the DI as the dependent variable. We selected 45 ground survey points from the jointing stage to the filling stage to construct a linear regression equation (equation (6.1)). The coefficient of determination (R2) of the equation was 0.923, and the standard error was 0.108. The significance level of 0.05 was used to perform an F test on the significance of this equation, and the F value was 121.5, which showed that the equation was very effective. Furthermore, 20 ground survey points are used to verify the equation, and the correlation test result is shown in figure 6.5. It is found that the R2 obtained by the measured DI and the predicted DI was 0.877, indicating that the equation can be used to monitor wheat yellow rust through PHI images. As the acquired PHI airborne hyperspectral image would be affected by various factors during the imaging process, system noise and abnormal values would appear in some specific bands, which affects the availability of data. The average value between 621–720.1 nm and 768.7–805.7 nm of the PHI image was selected as the independent variable, and these independent variables were input into equation (6.1). The spatial distribution map of yellow rust in the three growth stages, as shown in figure 6.6, was drawn by using equation (6.1). DI ¼ 18:652  Red  1:761  NIR þ 7:364

FIG. 6.5 – To verify the linear regression equation by using field survey data.

ð6:1Þ

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FIG. 6.6 – Three-view wheat yellow rust spatial distribution based on PHI airborne hyperspectral images. To classify the yellow rust infection situation, the calculated DI on the PHI images was classified as healthy (0%–5%), mild (5%–25%), moderate (25%–50%), severe (50%–80%), and extremely severe (80%–100%). From the spatial distribution map of yellow rust in three growth stages, we could find the dynamic characteristics of the disease impact on the wheat as follows. At the jointing stage, only mild infection occurred in the inoculated plot and adjacent plots; at the filling stage, the pathogenic bacteria spread, and moderate and severe infections appeared in the inoculated plots, while the main fields were still mild; during the milk maturity period, the inoculated plots have been fully infested with moderate and severe infections, while moderate infections have increased in the main fields. By analyzing the direction of infection of yellow rust, it was found that the incidence in the south was more serious than that in the north, especially on the 5/31 PHI image. The actual situation was that the concentration at the time of inoculation increased from south to north, which was very consistent with the actual situation. However, there were some obvious misclassifications in the image. For example, on the 4/18 and 5/17 images, the edge of the plot was mistakenly classified as a severe area. The reason lied in there was bare soil in the isolation line and some trees were planted around the plot. To extract the disease infestation more accurately, it is recommended to extract the wheat area in the follow-up research, and remove the non-vegetation and trees through masking. In this study, due to the test field was small and the crop type was relatively single, no masking treatment was performed.

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 203

6.2 6.2.1

Wheat Powdery Mildew Monitoring Spectral Response

The visual appearance of the leaves after powdery mildew infection is white to light yellow. Therefore, disease spots on leaves are an important reason for infested leaves spectrum changing. This section mainly uses imaging hyperspectral to study wheat leaves infested by powdery mildew. Figure 6.7 showed the hyperspectral images obtained from the experiment and the spectral curves of the infested and healthy positions in the leaves. The green part of the image corresponded to the healthy area of the leaf, the pink part corresponded to the location of the infested spot, and the black area was the background. Since the imaging hyperspectral system used was push-broom imaging, the light source was an indoor halogen lamp. The light source and spectrum sensor were fixed during the push sweep, and the leaf was moved at a certain speed through the conveyor belt to scan and image. Therefore, it is difficult to achieve a completely uniform light intensity distribution in each part of the image. Due to the characteristics of push-broom imaging, the light intensity parallel to the conveying direction is always the same. When selecting the healthy and infested spots in the leaf, the area of the same leaf parallel to the scanning direction was selected for analysis. As shown in figure 6.7, a total of 3 sets of locations that meet the requirements were selected in the hyperspectral image, and the hyperspectral reflectance of a 4 × 7 pixel area (the image pixel size was about 1 mm × 1 mm) is extracted at each location. The reflectivity curve was obtained, as shown in the upper right of figure 6.7. We have noticed that although the reflectance values of the three groups of spectra were significantly different, looking at the positions of each pair of healthy and infested spots, the changes in the spectra of the infested spots were always the same (figure 6.7 lower right). Therefore, it basically eliminated the influence of different light irradiation intensities on the spectral changes of the infested spots on the studied leaves. From the upper and lower pictures on the right side of figure 6.7, it can be found that the spectral reflectance of powdery mildew spots was significantly different in the visible and near-infrared regions compared to the healthy part. On the one hand, the photosynthetic mechanism of wheat leaves affected by powdery mildew pathogens was inhibited, and the absorption of light energy by photosynthetic pigments was greatly reduced, leading to a corresponding increase in reflectivity. On the other hand, the decrease in reflectance of the infested leaf spectrum in the near-infrared region was related to the destruction of the cell structure at the infested part. Table 6.8 showed the correlation analysis results between the DIs and the corresponding pigment contents of the infested leaves in this study. The correlation coefficient between the DIs and pigment contents were about 0.65–0.75, indicating that there was a clear relationship, that is, as the degree of infection deepened, more pigments were destroyed. However, the correlation coefficient did not reached a high level, indicating that there was a certain degree of uncertainty in the relationship, and the leaf chlorophyll level was one of the important effects. There are certain

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FIG. 6.7 – Schematic diagram of imaging hyperspectral analysis of healthy and infested parts of wheat leaves. The upper right graph is the original spectral reflectance curve of the three groups of healthy and lesion locations, and the lower right graph is the spectral ratio curve of the lesions to healthy. The leaf growth period is the wheat filling stage.

TAB. 6.8 – Correlation analysis results of leaf DI and pigment content of wheat powdery mildew. Correlation DI Chla Chlb Chla+b Car Car/Chla+b DI Na −0.677*** −0.721*** −0.691*** −0.692*** 0.319 Chla −0.677*** Na 0.982*** 0.999*** 0.974*** −0.469** Chlb −0.721*** 0.982*** Na 0.990*** 0.971*** −0.458* Chla+b −0.691*** 0.999*** 0.990*** Na 0.976** −0.468** Car −0.692*** 0.974*** 0.971*** 0.976*** Na −0.275 Car/Chla+b 0.319 −0.469** −0.458* −0.468** −0.275 Na *Indicates that the difference is significant at the 0.950 confidence level; **indicates that the difference is significant at the 0.990 confidence level; ***indicates that the difference is significant at the 0.999 confidence level; Na is the diagonal data; Chla and Chlb indicate chlorophyll a and chlorophyll b, respectively; Chla+b is the sum of Chla and Chlb; Car is the carotenoid.

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 205 differences in the chlorophyll levels between the leaves of a healthy plant. For example, a leaf with a high chlorophyll content which is severely susceptible and a leaf with a low chlorophyll content which is slightly susceptible may eventually have similar chlorophyll content. Therefore, when the spectra of infested leaves are affected in this way, the sensitivity of the characteristic chlorophyll absorption band to the disease will be reduced. Of course, this explanation still needs more analysis to verify.

6.2.2

Monitoring at Leaf Scale

To study the texture characteristics of powdery mildew leaves, the PIS high-resolution image was analyzed in ENVI based on probability statistics. The probability statistics used the number of occurrences of each gray level in the processing window for texture calculation. A total of five different texture filters based on probability statistics were obtained, i.e. Data Range, Mean, Variance, Entropy and Skewness. In figure 6.8b, the entropy and skewness carried too little information, so the other three filter statistics were analyzed. It can be seen from figure 6.8c that in the false color image synthesized by the Data Range, Mean, and Variance, the background was displayed as black, the healthy leaves were displayed as dark green, and the powdery mildew spots were displayed as white. The colors of these objects were consistent with the actual observations, which can distinguish powdery mildew spots. Based on the regions of interest (ROI) of healthy and infested wheat leaves selected in figure 6.8a, the three texture filtering of Data Range, Mean, and Variance were calculated (table 6.9). It can be found that the statistical values of the three texture filtering of infested leaves were larger than those of healthy leaves, which can be explained that the coverage of powdery mildew increased the unevenness of the leaf surface, so the corresponding texture information was more abundant. From the

FIG. 6.8 – Texture filtering analysis of PIS image of powdery mildew leaf.

206

Data range

Healthy Infested Difference value *

Mean

Min

Max

Mean

0.174 0.349

1.130 8.254

0.584 2.956

Standard deviation 0.164 1.661

0.175

7.124

2.372

1.497

Variance Min

Max

Mean

3.864 14.654

Standard deviation 0.397 3.993

0.004 0.010

0.124 8.106

0.035 1.210

Standard deviation 0.018 1.333

10.790

3.596

0.006

7.982

1.175

1.315

Min

Max

Mean

2.664 7.065

4.807 28.107

4.401

23.300

*Represents the difference between the corresponding statistical values of infested leaves and healthy leaves.

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

TAB. 6.9 – Statistics of texture filtering for healthy and infested leaves.

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 207 difference between the two statistics, it can be found that the maximum value difference of the mean was 23.3 and the minimum value of the variance was 0.006. Figure 6.9 is a comparison of the PIS image of wheat leaf infested with powdery mildew before and after smoothing. It can be shown that the smoothed spectral curve of the same pixel has been greatly improved, especially in the range of 445– 680 nm and 760–1000 nm. The typical spectral characteristics of vegetation such as green peaks, red valleys, and near-infrared platforms of the leaves have been significantly enhanced. In the smoothed image (figure 6.9), two pixels were selected from the healthy and infested area. Comparing the spectrum curves of healthy and infested area in figure 6.10a, it can be found that in the spectral range of 450– 950 nm, the spectral curve of infested area was significantly higher than the healthy counterpart, and the vegetation characteristics of the spectral curve of infested area (green peaks and red valleys in the visible light band) became blurred or even disappeared. Comparing the reflectance of wheat leaves of the four severity levels in figure 6.10b, it was found that as the disease severity increased, the reflectance also increased. The coverage of powdery mildew greatly enhanced the reflectivity of the leaves, and at the same time, destroyed the internal tissue structure of the leaves,

FIG. 6.9 – Comparison of before and after smoothing of PIS image of wheat leaf infested by powdery mildew.

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FIG. 6.10 – Highlight curves of healthy and powdery disease spots (a) and four disease severities (b).

which caused the typical vegetation reflectance characteristics of the leaves to weaken or even disappear. On the high resolution PIS image, 110 healthy leaf pixels and 110 powdery mildew pixels were randomly selected. To find the band with the largest spectral difference in the 450–950 nm band, the average spectra of two types of pixels were first obtained. According to the construction principle of NDVI (Rouse et al., 1973), the difference between the red light and the near-infrared range was the most sensitive area for changes in biomass and chlorophyll content. In this study, the average of 10 red light bands (675.1–681.1 nm) and 10 near-infrared bands (706.2–712.1 nm) was calculated to obtain two integrated bands. 220 pixels were placed in the feature space constructed by these two bands (the red band was the X axis and the near infrared band was the Y axis), and the distribution trends of healthy and powdery infested spots were analyzed. The distribution of infested spots was more discrete than that of healthy leaves (figure 6.11a). A straight-line equation (equation (6.2)) separates the two types of pixels. Y ¼ 3:48 X  7:57

ð6:2Þ

In the equation, X is the reflectance of the red band (675.1–681.1 nm), and Y is the average reflectance of the near-infrared band (706.2–712.1 nm). To verify the validity of the equation, 60 healthy and 60 infested pixels were selected, and the average reflectance of the red and near-infrared bands were brought into equation (6.2). The results showed that 8 infested pixels were misclassified to healthy pixels; a healthy pixel was misclassified as a infested pixel, and the overall classification accuracy reached 92.5%. Comparing the original image and the extracted result map, it can be found that most of the infested spots were identified, but due to the uneven illumination during imaging, some healthy spots were mistakenly classified as infested spots.

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 209

FIG. 6.11 – Construct a linear equation to extract powdery mildew spots on leaves.

6.3 6.3.1

Wheat Aphid Monitoring Extraction of Aphid Information by Sensitive Wavebands

1. Selection of sensitive wavebands Sensitive wavebands is one of the basic features in the research of remote sensing monitoring of pests and diseases. In the study, we tried to find the spectral bands sensitive to aphids through the leaf imaging spectrum, and discussed the extraction method of leaf aphids based on the sensitive bands. First, 50 typical pixels of aphid attachment points and 50 aphid-free leaf pixels were selected in the original image, respectively. All 100-pixel values were extracted. The reflectivity of pixels with aphid attachment points and pixels without aphid attachment points is averaged to represent the reflectance of aphid attachment pixels and healthy leaf pixels. Based on the changes of the above two types of pixels,

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FIG. 6.12 – Reflectance curves of aphid-attached and non-attached aphid pixels.

the 10 bands with the largest difference in the visible light region (500–710 nm) and the near infrared region (710–820 nm) were initially selected as the sensitive bands of the aphid. Finally, through screening, 669–675 nm and 751–757 nm were the most different wavelength regions of the two types of pixels, and the aphid-sensitive bands of the imaging spectra. Figure 6.12 is the corresponding reflectivity curve. In the visible light band (500–710 nm), the reflectance of the pixel with aphids was significantly higher than that of healthy leaf pixels. The change trend of reflectance in the infrared band (710–820 nm) was opposite to that of the visible light, while the change trend of reflectance in the 820–900 nm was the same as that of the visible light. According to previous research, the spectral reflectance of leaves in the near-infrared band was mainly controlled by the structural characteristics of the leaves. In the 820–900 nm, the reflectance of pixels attached aphid was higher than that of healthy leaves, which was an obvious spectral feature for identifying aphids. Therefore, whether aphids were attached to the leaves could be identified through this waveband area. 2. The construction of identification model The average reflectance in the selected band range of 669–675 nm was taken as R1, and the average reflectance in the range of 751–757 nm was taken as R2. The two-dimensional spectral feature space was used to identify aphid attachment points and no aphid attachment points on the image. By using the same method as above, 200 pixels (R1:100, R2:100) were extracted to construct the recognition model in the imaging image. Figure 6.13 is a two-dimensional feature space distribution map of aphid attached leaf pixels and aphid-free attached leaf pixels based on R1 and R2.

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 211

FIG. 6.13 – Distribution of aphid attached leaf pixels and aphid-free attached leaf pixels in a two-dimensional feature space. Through the distribution of the two types of pixels in the two-dimensional feature space, it was found that the two types of pixels could be distinguished and identified by fitting a straight line. Through the straight-line fitting method, the final recognition model was R2 = 6.016*R1−10.79. Discrimination method: if the pixel was above the line of the recognition model in the two-dimensional feature space, it was determined as a healthy leaf pixel, if it was below the line, it was determined as a leaf pixel attached to aphids. Based on the above recognition model, the R2 was calculated according to the value of R1 of the pixel. If the actual R2 of the pixel was greater than the R2 calculated by the recognition model, the pixel was judged as a healthy leaf pixel, otherwise it was an aphid attached pixel. 3. Identification results The recognition model was applied to the image, and the identified result was obtained by band calculation function. By using the supervised classification method, the image was classified, identified and divided into background, leaves with aphids and healthy leaves. The recognition result is shown in figure 6.14.

6.3.2

Extraction of Aphid Information by Spectral Index

Spectral index method is the most basic method to extract crop growth and stress information using hyperspectral imaging data and multispectral remote sensing data. Therefore, in this research, the vegetation index was used to extract aphid information and find the best vegetation index for extracting aphid information. According to previous research, 8 spectral indices that were commonly used for identifying pests and diseases were selected to extract aphid information, which included AI, photochemical reflectance index (PRI), nitrogen reflection index (NRI), red edge vegetation stress index (RVSI), damage sensitivity spectral

212

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FIG. 6.14 – Identification classification diagram. index (DSSI2), NBNDVI, PSRI, and ARI. Figure 6.15 showed 4 images of spectral indices. Comparing the spectral indices images with the original image, it is found that the DSSI2 index can better identify leaf aphids on the image. Therefore, the decision tree classification method was used to identify aphids based on DSSI2 images. The classification results are shown in figure 6.16. An initial analysis of the classification results showed that the DSSI2 index had good performance to identify aphids on the leaves; at the same time the aphids were correctly identified, but the edge of the leaves was also misjudged as an aphid attachment area.

6.3.3

Extraction of Aphid Information by Principal Component Analysis

PCA is the best orthogonal linear transformation based on statistical characteristics with the smallest root mean square error. The purpose is to reduce the number of indicators to a few comprehensive indicators. Hyperspectral remote sensing can completely record the spectral curves of observation objects and obtain continuous spectral information. The number of bands can reach hundreds or even thousands. However, while the amount of information increases, there is a lot of redundancy in hyperspectral data due to the high correlation between adjacent bands. PCA generates a series of uncorrelated new variables by constructing an appropriate combination of original variables. And a few new variables are selected, which contain as much information as possible on the original variables. PCA has been widely used in remote sensing data processing. In recent years, many scholars have applied the

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 213

FIG. 6.15 – Spectral indices images. PCA to identify pests based on hyperspectral images. Therefore, in this section, PCA was used to identify leaf aphids. The PCA was implemented in ENVI. In this process, the principal components were constructed by statistically analyzing the images, calculating eigenvalues on the basis of the band covariance matrix. According to the relationship between principal component and eigenvalue, a small number of principal components were selected as the output result. Figure 6.17 shows the first four principal components after the principal component transformation. For this image, the first three principal components included most of the information of the original image (V = 99.95%). In the fourth principal component, obvious noise appeared in the image. It can be seen from the principal component images that the first principal component mainly represented the leaf information, but basically had no response to the aphid information, while the second and third principal components enhanced the aphid information. The third principal component had the strongest ability to characterize aphid information. Three vertical bars parallel to the scanning direction can be found on the leaves of the first three principal component images. These vertical bars may be caused by the uneven sweeping speed during the scanning

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FIG. 6.16 – Classification recognition results based on DSSI2. process. These vertical bars would interfere with the extraction of aphids, especially in the PC3 image. It can be found that the vertical bar was enhanced as well as the aphid features. Therefore, extracting aphid information by using PC3 would be disturbed by the vertical bar. But PC2 can distinguish the vertical bar information and aphid, so PC2 and PC3 were comprehensively used to classify and extract the aphids. The extraction results are shown in figure 6.18. It is found from the classification result that PC2 and PC3 had a stronger ability to enhance aphid information, and can better extract and identify aphids from wheat leaves.

6.3.4

Construction of Aphid Index and Extraction of Aphid Damage

Considering the healthy leaf area, the aphid-infested leaf area (the honeydew distribution area), and the aphid-attached area have different spectral responses, we tried to identify the three types of leaf areas based on their different spectral responses. It was assumed that the damage degree of the aphid was, in descending order, aphid-attached area, aphid-infested area, and healthy area. We constructed a spectral index to identify these three types. First, we found that the largest difference in visible and near-infrared band was 10 nm. Based on the difference between the two spectral curves, it was found that 665–675 nm in the visible light band and 733–743 nm in the near infrared band met the conditions (figure 6.19). Then, the average reflectance of these two band regions

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 215

FIG. 6.17 – Principal component transform results of imaging hyperspectral image.

was normalized and defined as leaf aphid damage index (LADI). The specific calculation method is as follows. Among them, Mref1 and Mref2 were the average values of the spectral reflectance of 665–675 nm and 733–743 nm, respectively. LADI ¼ ðMref 1  Mref 2 Þ=ðMref 1 þ Mref 2 Þ

ð6:3Þ

The LADI was used to calculate each pixel in the image. The object-oriented image segmentation method was used to reclassify the images into three categories as aphid-attached areas, aphid-infested areas, and healthy areas (figure 6.20). To verify the classification results, the damage area ratio (DAR) of each leaf in the image was estimated by pixel statistics calculation. The calculation formula is as follows. In which, N1, N2, and N3 are the number of pixels in the aphid-attached area, aphid-infested area, and healthy leaf area, respectively. DAR ¼

N1 þ N2  100% N1 þ N2 þ N3

ð6:4Þ

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FIG. 6.18 – Classification chart based on PC2 and PC3.

FIG. 6.19 – Difference curve of two types of pixel spectra.

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 217

FIG. 6.20 – Image monitoring results of leaf aphids and hazard areas. We assessed the identification results by comparing the estimated damage area rate based on the imaging spectrum with the actual investigation leaf damage area rate. The evaluation results are shown in table 6.10. The results showed that the estimated leaf damage area ratio using imaging spectra was basically consistent with the actual survey results, and the estimated maximum relative error was 10%, on the first and fifth leaves; the estimated result of the fourth leaf had the smallest relative error at 2.5%. Therefore, the leaf aphid damage index based on the imaging spectrum can better estimate the damage area rate. At the same time, the results also confirmed the hypothesis that the spectral response degree of the aphid-attached region was greater than that of the aphid-infested region, and in some cases, the amount of leaf aphids alone cannot truly characterize the degree of damage to the leaves. For instance, in the fifth leaves, the number of aphids was 26, which was the largest number of aphids among the five leaves. However, the monitoring results showed that the actual damaged area of the fifth leaf was 50%, and the damaged degree was not the most serious TAB. 6.10 – Comparison of the estimated damage area ratio of the imaging spectrum with the actual surveyed leaf area damage ratio. ID Investigate infested area rates Estimated infested area rates Relative error

No. 1 20% 18% 10%

No. 2 40% 38% 5%

No. 3 45% 42% 6.67%

No. 4 80% 82% 2.5%

No. 5 50% 55% 10%

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FIG. 6.21 – Reflectance curves of three types of pixels. among the five leaves. The actual number of aphids on the fourth leaf was not the largest, but the actual area damage rate was as high as 80%. Therefore, at the leaf scale, the damage area rate to characterize the damage degree of the leaf by aphids was better than the number of aphids. To further understand the characteristics of the spectral reflectance of the three types of pixels, especially the spectral changes of the aphid-attached area and the aphid-infested area, the average spectral reflectance of the three types of pixels was used to represent the three types of reflectance. As shown in figure 6.21, it can be found that the reflectance curve of aphid-infested area was between the reflectance curves of the healthy area and the aphid-attached area. Imaging remote sensing technology has great potential for crop pests and diseases monitoring. This chapter utilizes the image information and spectral information of imaging hyperspectral data to monitor the spatial and temporal state of crop pests and diseases. Monitoring models were constructed based on textural features and spectral features. For crop diseases, disease spots identification and development monitoring were conducted. For crop pests, information on the extent of occurrence and pest migration was obtained. The research results in this chapter confirm the feasibility of imaging remote sensing technology in crop pests and diseases monitoring, and also provide a theoretical basis for crop pests and disease monitoring based on UAV and satellite images.

References Araújo M. C. U., Saldanha T. C. B., Galvão R. K. H., et al. (2001) The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemom. Intell. Lab. Syst. 57, 65. Broge N. H., Leblanc E. (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76, 156.

Imaging Hyperspectral Remote Sensing Monitoring of Crop Pest and Disease 219 Cao J., Leng W., Liu K., et al. (2018) Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens. 10, 89. Chen J. M. (1996) Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sens. 22, 229. Cheng X., Chen Y. R., Tao Y., et al. (2004). A novel integrated PCA and FLD method on hyperspectral image feature extraction for cucumber chilling damage inspection. Trans. ASAE. 47, 1313. Chuanlei Z., Shanwen Z., Jucheng Y., et al. (2017) Apple leaf disease identification using genetic algorithm and correlation based feature selection method. Int. J. Agric. Biol. Eng. 10, 74. Datt B. (1998) Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves. Remote Sens. Environ. 66, 111. Devadas R., Lamb D. W., Simpfendorfer S., et al. (2009) Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agric. 10, 459. ElMasry G., Wang N., ElSayed A. (2008) Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J. Food Eng. 81, 98. Filella I., Serrano L., Serra J., et al. (1995) Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci. 35, 1400. Fu Y., Zhao C., Wang J., et al. (2017) An improved combination of spectral and spatial features for vegetation classification in hyperspectral images. Remote Sens. 9, 261. Galvao R. K. H., Araujo M. C. U., Fragoso W. D., et al. (2008) A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm. Chemom. Intell. Lab. Syst. 92, 83. Gitelson A. A., Kaufman Y. J., Stark R. (2002) Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80, 76. Gitelson A. A., Merzlyak M. N., Chivkunova O. B. (2001) Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem. Photobiol. 74, 38. Haboudane D., Miller J. R., Tremblay N., et al. (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 81, 416. Hall M. A., Holmes G. (2003) Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Knowl. Data Eng. 15, 1437. Haralick R. M., Shanmugam K., Dinstein I. H. (1973) Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610. Huang W., Guan Q., Luo J., et al. (2014) New optimized spectral indices for identifying and monitoring winter wheat diseases. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 2516. Gamon J. A., Penuelas J., Field C. B. (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 41, 35. Jia B., Wang W., Yoon S. C., et al. (2018) Using a combination of spectral and textural data to measure water-holding capacity in fresh chicken breast fillets. Appl. Sci. 8, 343. Karegowda A. G., Manjunath A. S., Jayaram M. A. (2010) Comparative study of attribute selection using gain ratio and correlation based feature selection. Int. J. Inf. Tech. Knowl. Manage. 2, 271. Knauer U., Matros A., Petrovic T., et al. (2017) Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images. Plant Methods. 13, 47. Li Q., Wang M., Gu W. (2002) Computer vision based system for apple surface defect detection. Comput. Electron. Agric. 36, 215. Liang S., Fang H., Morisette J. T., et al. (2003) Atmospheric correction of Landsat ETM+ land surface imagery. II. Validation and applications. IEEE Trans. Geosci. Rem. Sens. 40, 2736. Liu Y., Chen Y. R., Wang C. Y. (2006) Development of hyperspectral imaging technique for the detection of chilling injury in cucumbers; spectral and image analysis. Appl. Eng. Agric. 22, 101. Lorenzen B., Jensen A. (1989) Changes in leaf spectral properties induced in barley by cereal powdery mildew. Remote Sens. Environ. 27, 201. Merzlyak M. N., Gitelson A. A., Chivkunova O. B., et al. (1999) No-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 106, 135.

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Penuelas J., Baret F., Filella I. (1995) Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica. 31, 221. Peñuelas J., Gamon J. A., Fredeen A. L. (1994) Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves. Remote Sens. Environ. 48, 135. Richards J. A. (1999) Remote sensing digital image analysis: an introduction. Springer-Verlag, Berlin, Germany. Rouse J. W., Haas R. H., Schell J. A. (1973) Monitoring vegetation systems in the great plains with ETRS. In: Third ETRS Symposium, NASA SP353, Washington, DC, 1: pp. 309–317. Sankaran S., Mishra A., Ehsani R. (2010) A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72, 1. Stohlgren T. J., Binkley D., Chong G. W., et al. (1999) Exotic plant species invade hot spots of native plant diversity. Ecol. Monographs. 69, 25. West J. S., Bravo C., Oberti R. (2003) The potential of optical canopy measurement for targeted control of field crop diseases. Ann. Rev. Phytopathology. 41, 593. Zarco-Tejada P. J., Berjón A., López-Lozano R., et al. (2005) Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 99, 271. Zhang J. C., Pu R. L., Wang J. H., et al. (2012) Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements. Comput. Electron. Agric. 85, 13. Zhang C., Xie Z. (2012) Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery. Remote Sens. Environ. 124, 310. Zheng Q., Huang W., Cui X., et al. (2018) New spectral index for detecting wheat yellow rust using Sentinel-2 multispectral imagery. Sens. 18, 868.

Chapter 7 Multi-Spectral Remote Sensing Monitoring The above chapters mainly discuss the spectral response mechanisms of crops under different stresses on a small scale such as leaf and canopy. By extracting spectral features which can characterize specific crop pests and diseases, crop pests and diseases monitoring and discrimination methods at different scales are established. However, limited by the high cost of acquisition, crop pests and diseases monitoring methods based on hyperspectral remote sensing technology were confronted with difficulties in wide promotion and application; and the ultimate goal of crop pests and diseases remote sensing monitoring is to achieve practical applications at the regional scale. In recent years, with the development of satellite, aviation and UAV technologies, various airborne and spaceborne remote sensing data sources have been increasing, providing remote sensing information products for users with multiple temporal, spatial and hyperspectral resolution. It also provides a valuable opportunity for crop pests and diseases monitoring and forecasting. How to use these remote sensing data sources to carry out crop pests and diseases monitoring and forecasting research at regional scale has become an important topic. This chapter mainly discusses remote sensing monitoring of crop pests and diseases at regional scale by using aviation and aerospace remote sensing data and mathematical analysis methods. The main content includes remote sensing monitoring of wheat yellow rust, wheat powdery mildew, wheat Fusarium head blight, and migratory locust.

7.1 7.1.1

Wheat Yellow Rust Monitoring Monitoring at Regional Scale

Fast and accurate monitoring of wheat yellow rust at regional scale plays a vital role in reducing yield loss. With the development of remote sensing technology, satellites with high spatial resolution and high time revisiting cycles were launched successfully. Remote sensing data have great advantages over traditional data in disease monitoring, including simpler operation, more real-time, higher resolution, and more DOI: 10.1051/978-2-7598-2659-9.c007 © Science Press, EDP Sciences, 2022

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targeted technical guidance for scientific prevention and control of the disease. Selecting multi-spectral satellite data containing multiple different band information can provide more abundant and effective information for crop disease monitoring at regional scale, which can improve the accuracy of the model effectively. Compared to conventional broad-band satellite remote sensing images, the Sentinel-2 with three red-edge bands is sensitive to crop disease stress. Based on the pre-processed remote sensing images, the spectral reflectance of the bands and the vegetation indices of the wide bands were extracted. In this paper, the reflectivity characteristics of 4 bands (red, green, blue, near-infrared), 3 red-edge bands (B5, B6, B7), 14 wide band vegetation indices, and 5 red-edge vegetation indices were extracted from Sentinel-2 as the primary characteristic factors of yellow rust monitoring model, including NDVI, RGR, VARI, SIPI, SR, DVI, TVI, NDGI, EVI, GNDVI, OSAVI, SAVI, RDVI, MSR, NDVI, NREDI1, NREDI2, NREDI3 and PSRI. Tables 1.1, 2.1, 5.1, 5.5, and 7.1 list the specific names and calculation formulas of each vegetation index. The classification accuracy of wheat yellow rust severity can be improved effectively by selecting characteristic variables that can best reflect the occurrence of disease. An appropriate feature selection method can effectively remove the irrelevant and redundant variables and improve the performance of the model. K-means algorithm is a commonly used unsupervised clustering algorithm, which can improve the clustering accuracy among features through clustering analysis. However, this algorithm has a high requirement for the selection of the initial center. The Relief algorithm (Chen et al., 2018) is widely used in feature selection by calculating weights according to the correlation between features and the dependent variable, that is, between vegetation indices and the disease severity. Therefore, to reduce the impact of the initial center on the results when using the K-means algorithm for feature screening, we adopted the method of combining the K-means algorithm with the Relief algorithm to select the optimal feature. Firstly, the Relief algorithm was used to calculate the sensitivety weight of each feature to the severity of wheat yellow rust. Due to that the algorithm randomly selects samples during operation, and the difference of random numbers would lead to the fluctuation of the resulting weight, we took the average after running the main program 20 times as the final TAB. 7.1 – Calculation formula of the selected vegetation indices. Type

Broad band vegetation indices

Red-edge vegetation indices

Vegetation index Ration of red and green, RGR Simple ratio index, SR Normalized greenness vegetation index, NDGI Re-normalized difference vegetation index, RDVI Normalized red-edge1 index, NREDI1 Normalized red-edge2 index, NREDI2 Normalized red-edge3 index, NREDI3

Formula RR/RG RNIR/RR (RNIR − RG)/(RNIR + RG) pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðRNIR
RR Þ= RNIR =RR (RRe2 − RRe1)/(RRe2 + RRe1) (RRe3 − RRe1)/(RRe3 + RRe1) (RRe3 − RRe2)/(RRe3 + RRe2)

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weight of each feature. After that, the feature with the highest weight value is taken as the initial center of the K-means algorithm, and clustering was carried out in descending order of the feature weight. If the contribution of the feature to the clustering accuracy was positive, the feature was retained; otherwise, the feature was removed. Finally, the feature combination with the highest clustering accuracy was taken as the final model input variable. Figure 7.1 shows the averaged Relief weight of each feature. SIPI was the feature with the highest weight, that is, the feature most relevant to the disease severity. Therefore, SIPI was taken as the starting center of K-means clustering. To reduce the amount of calculation, we conducted K-means clustering analysis on the top 10 features with the highest Relief weight, that is, the 10 features most relevant to the disease, in order to select the feature combinations that can obtain the highest clustering accuracy. Table 7.2 shows the clustering accuracy of each feature. Finally, three broad band vegetation indices EVI, SIPI, SR, and two red-edge vegetation indices NREDI2 and NREDI3 were selected for the construction of the wheat yellow rust severity monitoring model. BPNN (Wang et al., 2015) is a supervised neural network with forward signal transmission and reverse error feedback, which has the advantage of self-learning ability. By building a multi-layer network, BPNN enables automatically learning of the relationship hidden in the data. BPNN has the ability to deal with complex nonlinear relationship between the severity of wheat yellow rust and its characteristic factors. Therefore, two remote sensing monitoring models of wheat yellow rust was established with BPNN, respectively, taking the feature set of broad-band vegetation indices and the combination of broad-band and the red-edge vegetation indices as input variables. The omission error, misclassification error, overall accuracy, and kappa coefficient of the monitoring results obtained by each monitoring method are shown in table 7.3.

FIG. 7.1 – Different features weight average values based on ReliefF algorithm.

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TAB. 7.2 – Clustering precision by combined features based on K-means algorithm. SIPI

EVI

PSRI1

NREDI3

NREDI2

SR

NREDI1

RGR

VARIgreen

NDGI

63.3%

70%

63.3%

73.3%

76.6%

76.6%

73.3%

73.3%

63.3%

56.6%

TAB. 7.3 – Overall validation result of BPNN models.

Feature factor

Broad band vegetation indices

Broad band + red-edge band vegetation indices

Accuracy indices

Actual samples

Healthy

Slight

Severe

Sum

Healthy Slight Severe Sum Healthy Slight Severe Sum

9 1 1 11 10 1 0 11

1 3 2 6 1 3 2 6

1 2 10 13 0 1 12 13

11 6 13 30 11 5 14 30

Omission error/% 18.2 50.0 23.1

Commission accuracy/% 18.2 50.0 23.1

9.1 40 14.2

9.1 50 7.7

Overall accuracy/%

Kappa coefficient

73.3

0.58

83.3

0.73

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Feature factor Precision by combined features

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By analyzing the results of the two models, it was found that the overall accuracy of the model with red-edge vegetation indices was 10% higher than that of the model with only broad-band vegetation index features, reaching 83.3%, and the kappa coefficient was 0.73. The omission error and overall accuracy indicated that, the error was mainly from the incorrect classification of slightly infested samples to healthy or severely infested ones. But overall, for both healthy and infested samples, the model showed better performance when combining broad-band and red-edge vegetation indices. From the perspective of pathology, the leaf structure of infested wheat was destroyed, resulting in a large response at the red-edge band. It can be concluded that compared with the model constructed with only traditional broad-band indices, the method combining broad-band and red-edge ones can provide more abundant information for disease monitoring. Therefore, the addition of red-edge band features can reflect the wheat growth and disease severity more comprehensively, and effectively improve the accuracy of the monitoring model. Using remote sensing image data of May 12, 2018 in the research area and taking a single pixel as the basic processing unit, three broad band vegetation indices EVI, SIPI, SR and two red-edge indices NREDI2 and NREDI3 were selected by combining K-means algorithm and Relief algorithm. Broadband vegetation indices (EVI, SIPI, SR) and the combination of broadband and red-edge vegetation indices

FIG. 7.2 – Monitoring spatial map of wheat yellow rust severity by BPNN.

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TAB. 7.4 – Statistics on the incidence of disease in each model. Wheat disease level accounts for the percentage/%

Type Field survey point Broad band + red-edge band vegetation indices

Healthy 36.7 50.8

Slight 20 20.5

Severe 43.3 28.6

(EVI, SIPI, SR, NREDI2, NREDI3) were put into the BPNN method, and the monitoring results of the severity of wheat yellow rust are shown in figure 7.2. As can be seen from the results, the overall spatial distribution of yellow rust of the two models was consistent, that is, the severly infested was continuously distributed in the southeast region, and the healthy and infested area was uniform. In comparision, figure 7.2 showed that the eastern region was more severely affected and the northern region was less affected. Table 7.4 listed the percentages of healthy, slightly infested, and severely infested wheat in the field survey. The occurrence proportion of yellow rust in figure 7.2 was 49.1%. Figure 7.2 showed a similar disease distribution in the field survey. According to the spatial distribution of diseases in the model results in figure 7.2 and the disease infection statistics in table 7.4, figure 7.2 as a whole was consistent with the actual situation.

7.1.2

Monitoring at National Scale

Wheat yellow rust forms yellow streaks or oval spots on wheat leaves, which makes the leaves turn yellow and reduces chlorophyll and water content. Wheat yellow rust criterion refers to the national standard (GB/T15795‐2011). Table 7.5 lists the detailed severity classes. Ground disease investigation data and remote sensing imagery were both collected. In addition, level 1 and level 2 constitute the yellow rust slight infestation, level 3 constitutes a moderate infestation, and level 4 and level 5 constitute severe infestation (table 7.5).

TAB. 7.5 – Wheat yellow rust severity level classification criterion. Severity levels

Index DI The rate of disease field/%

1

2

3

4

5

0.001 < Y ≤ 5

5 < Y ≤ 10

10 < Y ≤ 20

20 < Y ≤ 30

Y > 30

1 35

Note: Y means disease index. R means the disease field rate. Reference: http://doc.mbalib.com/view/2e0ae53c7f397af70deb37edb07c5a12.html.

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First, according to field investigation and existing study knowledge, prior wheat yellow rust spatial position data were used. Based on red edge vegetation stress indices RVSI (equation (7.1)) and PSRI (equation (7.2)), a wheat yellow rust index WYRI (equation (7.3)) was then developed for the yellow rust monitoring (Merzlyak et al., 1999). This developed index considered wheat growth status and pigment change. Finally, combining disease environmental indicators which included meteorological features such as wind and rainfall, and remote sensing indicators such as LST, as well as corresponding historical data, we established DI (equation (7.4)) for the monitoring of wheat yellow rust. RVSI ¼ ððR712 þ R752 Þ=2Þ
R732

ð7:1Þ

PSRI ¼ ðRR
RB Þ=RNIR

ð7:2Þ

WYRI ¼ f ðDRVSI; DPSRIÞ

ð7:3Þ

  DI ¼ g WYRI; LST
LSTavg ; R
Ravg ; W

ð7:4Þ

where, R712 means the band reflectance of 712 nm; R752 means the band reflectance of 752 nm; and R732 means the band reflectance of 732 nm; RR means red band reflectance; RB means blue band reflectance; RNIR means near-infrared band reflectance. LST means land surface temperature; LSTavg means the average historical land surface temperature. R means rainfall; Ravg means the average historical rainfall. W means wind direction. f and g are regression analysis processes based on the ground investigation data set. The DI value range is from 0% to 100%, and 0% < DI ≤ 30% represents slight yellow rust infestation, 30% < DI ≤ 60% represents moderate infestation, and DI > 60% represents severe infestation. Wheat yellow rust infestation area achieved 0.7 million ha in China in 2019. In early April, Jianghan, Jianghuai, southern Huanghuai, Northwest China, Southwest China were the places where the yellow rust first appeared. From mid-late April to mid-May, yellow rust reached its reproductive peak, and occurred widely in Jianghuai, Huanghuai, and Northwest China. Figure 7.3 and table 7.6 respectively list the disease spatial status and occurrence area in different stages.

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FIG. 7.3 – Spatial distribution of wheat yellow rust in 2019 (a) Early April, (b) Late April, (c) Early May, and (d) Mid May. TAB. 7.6 – Occurrence area of wheat yellow rust in China in 2019. Area/thousand ha

Regions Northeast China North China East China South China Central China Northwest China Southwest China Sum

Early April 0 17.9 56.7 0 44.1 22.7 12.7 154.1

Late April 0 37.3 111.3 0 87.3 45.3 23.4 304.6

Early May 0 76 198.7 0 157.3 82.7 34.7 549.4

Mid May 0 82 242.7 0 188 96.7 50 659.4

Total area 85.3 3579.3 8556 16.7 6710 3376 1828.7 24152

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7.2

229

Wheat Powdery Mildew Monitoring

Crop disease results in obvious canopy structure, moisture, and pigment changes, causing the change of reflectance of near-infrared band and visible region. For the red and blue shift phenomenon, the red and near-infrared bands have a significant correlation with the disease, so combing specific bands reflectance could strengthen the differences between health and infested samples. Therefore, twelve commonly used broad band vegetation indices (table 7.7) which are sensitive to wheat powdery mildew were extracted by using the corresponding satellite imagery, including EVI, MSR, NDVI, OSAVI, RDVI, SAVI, SR, TVI, GDNVI, RTVI, DSWI, and SIWSI. Tables 1.1, 2.1, 5.1 and 7.7 list the basic information of the selected indices. In addition, LST was obtained by single-channel algorithm inversion as the primary features of the powdery mildew monitoring model.

7.2.1

Monitoring with HJ Data

The accuracy of the model can be effectively improved by selecting the characteristic variables that can best reflect the occurrence and development of the disease. Two parts were included to select optimal features for modeling. First, by combining Relief algorithm and K-means algorithm, the optimal indices were selected. Second, in order to further highlight the difference between healthy wheat and infested wheat, a Gabor wavelet transform method was applied to the optimal indices to obtain a set of detailed characteristics of the indices. Meanwhile, the second feature selection was carried out within dependent t-test method. Table 7.8 lists the Relief feature weight, K-means single feature clustering accuracy and feature combination clustering accuracy of each feature. Finally, NDVI, SR, and LST were selected for the model construction. Gabor wavelet can carry out local analysis of time and frequency at the same time, which makes the analysis of stationary signals easier. The Gaussian kernel function was used as the parent wavelet to construct the wavelet kernel function. The vegetation index features were convolved with the wavelet kernel function, and the convolution amplitude was used as the modeling feature information – a total of 40 wavelet kernel functions in 5 scales and 8 directions, so that the amount of data after the wavelet transformation was expanded by 40 times. By independent sample t-test, the corresponding wavelet kernel function was obtained. m, n, and corresponding scale factors and rotation angles were obtained as shown in table 7.9. TAB. 7.7 – Vegetation indices used in this article. Vegetation index Red-edge Triangular Vegetation Index, RTVI

Formula [55 (RNIR − RG) − 90 (RR − RG)]/[90 (RNIR + RG)]

Note: RNIR means near-infrared reflectance; RR means red band reflectance; RG means green band reflectance; and RB means blue band reflectance.

230

Feature weights by Relief clustering precision by K-mean Precision by combined features

NDVI

SR

SAVI

ρR

ρNRI

EVI

RDVI

LST

ρG

OSAVI

0.081

0.072

0.066

0.065

0.062

0.048

0.042

0.036

0.0038

0.7059

0.7059

0.7059

0.6078

0.5098

0.6667

0.7059

0.6078

0.7059

0.7059

0.6078

0.4902

0.2941

0.2353

0.2353

0.7451

Note: ρG means green band reflectance.

0.0035

MSR −0.006

ρB −1.280

0.2941

0.2941

0.2941

0.2353

0.2353

0.2353

0.2353

0.2157

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TAB. 7.8 – Feature weights by Relief, clustering precision by K-means, precision by combined features.

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Three models were established with SVM method. The first model used all the twelve vegetation indices to establish control experiments. The second model used three selecting indices i.e., LST, NDVI, and SR. The third model used three optimal features (table 7.9) after Gabor wavelet transform. Table 7.10 lists the user accuracy, overall accuracy and Kappa coefficient of the SVM model (M1), SVM with Relief and K-means (M2) and GaborSVM combined with Relief algorithm and K-means clustering (M3). It can be seen from the results that the three groups of experiments obtained good experimental results. From the overall accuracy, the SVM model’s overall accuracy was lower than that of GaborSVM model, indicating that Gabor small wave feature had a higher rate of disease recognition than the original vegetation index feature. The Kappa coefficient of M3 also reached 0.583, which was higher than M1 at 0.286 and M2 at 0.444. In addition, in the two SVM models, the accuracy of the SVM model through feature screening was higher than that of the SVM model without feature screening, which could be speculated that this was due to the removal of redundant features and negative correlation features. From the perspective of user accuracy, the user accuracy of disease in the three models were 50%, 83.3%, and 91.7%, respectively, indicating that the recognition accuracy of the three models to disease is constantly improving, and the user accuracy of GaborSVM was up to 91.7%, indicating that this model can identify disease samples more accurately. The above results showed that the small wave feature could improve the accuracy of the monitoring model to distinguish health from disease, and the feature screening was helpful to improve the model accuracy. TAB. 7.9 – Parameters of optimal wavelet functions. Vegetation index LST NDVI SR

Scale (m) 4 1

Direction (n) 2 0

4

1

Scale factor (a
m )

Rotation angle (h)

1 4 p1ffiffi 2 1 4

p 4

0 p 8

TAB. 7.10 – Overall verification results. Accuracy indices Model

SVM (M1)

Healthy Disease

Healthy

Disease

Sum

User accuracy

Overall accuracy

Kappa coefficient

3 6

0 6

3 12

100% 50%

60%

0.286

80%

0.444

86.7%

0.583

Sum

9

6

15

SVM with Relief and K-means (M2)

Healthy

2

1

3

66.7%

Disease

2

10

12

83.3%

Sum

4

11

15

Gabor-SVM with Relief and K-means (M3)

Healthy

2

1

3

66.7%

Disease

1

11

12

91.7%

Sum

3

12

15

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FIG. 7.4 – Monitoring spatial map of wheat powdery mildew.

The spatial distributions of wheat powdery mildew based on SVM and GaborSVM models are shown in figure 7.4. It can be seen from the figure below that the overall spatial distribution of powdery mildew in the three monitoring models was similar, and the incidence in the east was more serious than that in the west. In figure 7.4a, the occurrence of powdery mildew was relatively sporadic compared with the other two figures. Figure 7.4b is roughly the same as figure 7.4c, showing the whole area distribution, except for some small differences. Wheat powdery mildew is caused by Candida brucellosis, which is characterized by rapid propagation and wide spread. Therefore, the probability of powdery mildew scattered in wheat filling period is low. Thus, it can be indirectly concluded that the reliability of the GaborSVM combined feature screening model was better than the others.

7.2.2

Monitoring with GF Data

The selection of suitable modeling method and feature selection algorithm plays an important role in improving the performance of remote sensing monitoring of crop diseases. Relief algorithm has a high operating efficiency. By calculating the weight of features, a higher weight can be assigned to features with strong classification ability. However, the Relief algorithm does not consider the redundancy among the features. Although the candidate feature set obtained only through the Relief algorithm had a high correlation with wheat powdery mildew, the potential redundancy among the features would adversely affect the accuracy of the model.

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The mRMR algorithm (Zhang et al., 2007) can obtain the feature set with the least redundancy among the features and the greatest correlation and the target. However, the mRMR algorithm has a high computational complexity and a large amount of calculation, and its correlation accuracy is lower than the relief algorithm. In addition, the mRMR algorithm cannot obtain a clear weight, and the extracted feature set cannot reflect the difference in the classification effect of different features, so it may lead to the mistaken deletion of features with good discrimination against powdery mildew. Therefore, by combining Relief and mRMR algorithms, the optimal indices for the disease monitoring were selected. The weight distribution among the features was calculated through the Relief algorithm (Sun and Jian, 2006), as shown in figure 7.5. To control the number of characteristic variables, a weighted threshold was set to 2500, and eight characteristics which met the condition (2500 or higher) were as candidate feature set of mRMR algorithm, and then were further screened by mRMR algorithm to get the optimal features variable characteristics of SR, NIR, NDVI as group 1 set; at the same time, we selected algorithm to get the weight of the three largest Relief features SR, GNDVI, TVI as group 2 set; and then we selected three characteristics of mRMR algorithm to get the optimal TVI, RTVI and RDVI feature set in group 3. By comparing the characteristic variables selected by different methods, SR, NIR, NDVI and GNDVI mainly represented the growth potential and vegetation coverage changes after disease stress. RDVI mainly represented the biomass information under different levels of vegetation coverage. TVI and RTVI represented the content changes of biological components caused by stress. From the pathological point of view, the canopy structure of crops was damaged due to the influence of the development of spore colonies on the surface of leaves after powdery mildew infection, resulting in a large response at the red-edge and near-infrared bands. Therefore, compared with the latter two groups of features, the features screened by the Relief-mRMR algorithm laid more emphasis on reflecting such changes in growth and canopy structure information. A remote sensing monitoring model of wheat powdery mildew was established based on the SVM optimized by GA algorithm. The basic idea of SVM is to find an optimal hyperplane that maximizes both sides of the hyperplane while ensuring classification accuracy. Radial basis kernel function is selected as the kernel

FIG. 7.5 – Calculation results of feature weight by Relief algorithm (a) Different features weight values based on Relief algorithm (b) Distribution of feature weight valuesbased on Relief algorithm.

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function of SVM. Two model parameters that affect the accuracy of the monitoring model are penalty factor C and radial basis kernel function parameter. The traditional method of parameter selection is to determine by trial and error. Currently, GirD Search (GS) algorithm is commonly used to obtain the optimal parameters, but this method has low efficiency and high workload. Genetic algorithm (GA) has the advantages of solving global optimal problems and strong robustness, so the algorithm has good expansibility. GA algorithm was used to optimize the factors and kernel parameters, and GA optimized SVM was used to establish the wheat powdery mildew monitoring model. Nine wheat powdery mildew monitoring models were established by using the optimal feature variables selected by Relief algorithm, mRMR algorithm and Relief-mRMR algorithm, combined with the unoptimized SVM, GSSVM optimized by GS algorithm and SVM optimized by GA algorithm (GASVM) (table 7.11). As can be seen from table 7.11, in the SVM, GSSVM and GASVM method models, the accuracy and Kappa coefficient of the monitoring model established by the Relief-mRMR algorithm were higher than those established by Relief algorithm and mRMR algorithm to filter the features separately. By comparing the models established by SVM, GSSVM and GASVM, it can be seen that the accuracy of the monitoring model established by GASVM was higher than that established by SVM and GSSVM. Among them, the accuracy of the Relief-mRMR-GASVM model was the highest, with the overall accuracy 21.4% and 7.2% higher than that of the Relief-mRMR-GSSVM and the Relief-mRMR-SVM models, respectively. Moreover, the overall accuracy, user accuracy and drawing accuracy of the Relief-mRMR-GASVM model was 85.7%, and the kappa coefficient was 0.714, which was the highest among all the models. The above results showed that the Relief-mRMR algorithm can more effectively screen out the characteristics reflecting the growth and disease of wheat, and the model of its screening features was superior to that of the Relief and mRMR algorithms. The monitoring model established by the GASVM method was superior to the model established by unoptimized SVM and GSSVM methods in accuracy. The powdery mildew monitoring model established by the Relief-mRMR algorithm combined with the GASVM method can effectively improve the model monitoring accuracy. The accuracy of the three SVM monitoring models was the lowest among the nine models, with the highest accuracy at 64.3%. Therefore, they could not be applied to powdery mildew monitoring. The occurrence and release of wheat powdery mildew on May 26, 2014 were obtained by using three feature selection algorithms combined with GSSVM and GASVM model methods respectively, as shown in figure 7.6. The monitoring results showed that powdery mildew occurred slightly in mRMR-GSSVM and mRMR-GASVM models, with a serious deviation from the field investigation. For the four monitoring models based on the features selected with Relief and Relief-mRMR algorithms, Relief-GSSVM (figure 7.6a), Relief-mRMR-SSVM (figure 7.6c), Relief-GASVM (figure 7.6d) and Relief-mRMR-ASVM (figure 7.6f), the monitoring results were roughly the same in spatial distribution. That is, the disease incidence was higher in southern region than that in northern region of the study area, which was consistent with the field survey. Consistent with the actual investigation, the percentages of the incidence

Accuracy indices

Model

Relief

SVM

mRMR

Relief-mRMR

Relief

GSSVM

mRMR

Relief-mRMR

Relief

GASVM

mRMR

Relief-mRMR

Healthy Disease Sum Healthy Disease Sum Healthy Disease Sum Healthy Disease Sum Healthy Disease Sum Healthy Disease Sum Healthy Disease Sum Healthy Disease Sum Healthy Disease Sum

Healthy 1 0 1 0 0 0 5 3 8 5 2 7 4 2 6 6 2 8 5 2 7 5 2 7 6 1 7

Disease 6 7 13 7 7 14 2 4 6 2 5 7 3 5 8 1 5 6 2 5 7 2 5 7 1 6 7

Sum 7 7 14 7 7 14 7 7 14 7 7 14 7 7 14 7 7 14 7 7 14 7 7 14 7 7 14

User’s accuracy/% 14.3 100

Producer’s accuracy/% 100 53.8

Overall accuracy/%

Kappa

57.1

0.143

0 100

0 50

50

0

71.4 57.1

62.5 66.7

64.3

0.286

71.4 71.4

71.4 71.4

71.4

0.429

57.1 71.4

66.7 62.5

64.3

0.506

85.7 71.4

75 83.3

78.5

0.571

71.4 71.4

71.4 71.4

71.4

0.429

71.4 71.4

71.4 71.4

71.4

0.429

85.7 85.7

85.7 85.7

85.7

0.714

Multi-Spectral Remote Sensing Monitoring

TAB. 7.11 – Accuracy analysis of different classification for models.

235

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

FIG. 7.6 – Monitoring spatial map of wheat powdery mild mildew. (a) Relief-GSSVM, (b) mRMR-GSSVM, (c) Relief-mRMR-GSSVM, (d) Relief-GASVM, (e) mRMR-GASVM, (f) Relief-mRMR-GASVM, (g) Position of local monitoring, (h) Relief-mRMR-GSSVM, and (i) Relief-mRMR-GASVM.

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237

area with these four models in the overall area were 44.5%, 62.1%, 62.4% and 60.4%, respectively. Compared with the other three figures, figure 7.6a had a smaller incidence area, which was deviated from the serious occurrence of wheat powdery mildew in the field investigation. By comparing the monitoring results in figure 7.6a, c, d and f, it can be found that the distribution of powdery mildew incidence area in figure 7.6a and 7.6d was relatively scattered. However, wheat powdery mildew was caused by powdery brucellosis, which generally did not occur scattered due to its fast propagation and wide spread area. Therefore, the monitoring results in figure 7.6a and 7.6d were contrary to the characteristics of wheat powdery mildew, while the monitoring results in figure 7.6c and 7.6f were more consistent with the occurrence characteristics of wheat powdery mildew and had higher credibility. Figure 7.6c and 7.6f were generally consistent, but there were differences in details. By observing the two local monitoring results in figure 7.6h and 7.6i, it can be found that the areas classified as healthy in figure 7.6i were classified as infested in figure 7.6h. The plots between healthy and infested wheat were evenly distributed in figure 7.6i, while most of the plots in figure 7.6h were the entire infested region and only a few plots showed uniform distribution. By comparing the local monitoring results of Relief-mRMR-GSSVM model and Relief-mRMR-GASVM model, it could be found that the overall trend monitored by two models was consistent with the actual situation. However, the distinguish ability of Relief-mRMR-GSSVM model was better than Relief-mRMR-GSSVM model. The Relief-MRMR-GASVM model was still applicable for local monitoring.

7.2.3

Monitoring with Multi-Temporal Landsat Data

Disease remote sensing basis is formed by the difference of the spectrum caused by powdery mildew. In general, in blue region, green region, and red region, the spectrum of the infested wheat was increased compared with that of the healthy wheat. But in near infrared region, compared with the healthy wheat, the spectrum of the infested wheat was decreased. This abnormal spectral response was caused by differences in plant biophysical and biochemical indicators caused by disease, such as changes in pigment content, water content, canopy structure, and leaf color due to pustules or lesions (Huang et al., 2007; Sankaran et al., 2010). Figure 7.7 shows the index response change of healthy wheat, slight infestation wheat, and severe infestation wheat. The average and standard deviation of each index were used to compare the indices for all six growing periods. For slight infestation wheat, the indices of all six periods showed the highest value. Powdery mildew spreads with the wind and infests hosts after harvest and crop sowing in autumn. The disease cycle is slow, but under suitable temperature, the disease may continue in winter. With the increase of temperature and humidity in spring, the disease infection is accelerated when the wheat grows rapidly. In general, the pathogen starts to recover in early February, develops quickly in March, and occurs in April and May (Zhang, 1991). Taking into account the characteristics of powdery mildew infection, occurrence and development, six Landsat-8 images were obtained from November 2013 to May 2014 (table 7.12).

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FIG. 7.7 – Index response to powdery mildew in six growing periods. TAB. 7.12 – Information provided by the images for disease monitoring. Growth period Wintering period Re-greening period Jointing period Filling period

Stage number Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6

Image acquisition date 16 November 2013 2 December 2013 8 March 2014 24 March 2014 9 April 2014 11 May 2014

Although the four VIs, i.e., DSWI, OSAVI, SIWSI, and TVI, of the six different periods were selected as the main input variables to construct the KNN monitoring model based on multi-temporal VIs, it is uncertain whether the performance of this multi-temporal combination is the best. Since the feasibility of the combination of the six selected periods is uncertain, it is necessary to evaluate the different temporal

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239

TAB. 7.13 – Estimated results of monitoring models based on different multi- temporal index combinations. Multi-temporal index combination R2 RMSE

Stages 1–6 0.50 0.58

Stages 2–6 0.69 0.44

Stages 3–6 0.61 0.51

Stages 2, 4, 5, 6 0.79 0.36

Stages 2, 5, 6 0.53 0.55

Stages 2, 4, 6 0.72 0.42

index combinations, so a back stepwise elimination method (Derksen and Keselman, 1992) was adopted. Table 7.13 lists the assessment results of the different temporal index combinations. The results showed that the monitoring model based on the indices of all six periods was the worst, with R2 of 0.50 and RMSE of 0.58. The monitoring model based on the index combination of stage 2, 4, 5, and 6 had the highest R2 and RMSE. Based on the multi-temporal indices combination and single-date indices, the monitoring models through BPNN, CART, and KNN were established (Laakso and Garrison, 2000; Ding et al., 2008; Saini and Khosla, 2013; Kim et al., 2012). Figure 7.8 shows the disease temporal distribution obtained by using the multi-temporal indices combination. The monitoring results were consistent with the ground field survey data. The infested area in region 1 was significantly higher than that in region 2. The KNN model obtained the largest severe infection area among the three models, while the BPNN model obtained the largest slight infection area. The testing results of the three models according to the multi-temporal vegetation indices are shown in table 7.14. The results revealed that all three methods could get an acceptable precision, and the KNN method had the highest overall accuracy and kappa coefficient of 84.6% and 0.516 among them. In addition, the three methods were used to establish monitoring models based on the conventional vegetation of the single date in stage 6, and figure 7.9 shows the damage distributions of the three models. The disease distributions of the three models were obviously different. Among the three monitoring models based on the single date vegetation indices, only the KNN monitoring model showed a receivable overall accuracy of 76.8% (table 7.15). Compared to the multi-temporal vegetation indices with conventional single stage vegetation indices, the monitoring precision of the three methods increased by 38.5%, 30.8%, and 7.7%, respectively. All these results revealed that the multi-temporal vegetation indices had greater potential in crop disease monitoring than the conventional single date vegetation indices, and the KNN method performed better than both CART and BPNN methods in crop disease monitoring.

7.3 7.3.1

Wheat Fusarium Head Blight Monitoring Monitoring at Regional Scale

In crop condition identification, red-edge spectral information is important, which can increase the disease monitoring precision at regional scale. Thus, the potential of red edge bands of Sentinel-2 satellite imagery in the monitoring of Fusarium head

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

FIG. 7.8 – Disease spatial distribution based on the most suitable multi-temporal vegetation indices (the left region was represented region 1, and the right region was represented region 2). blight is worth to be explored. The Sentinel-2 spectral band based spectral features which were sensitive to wheat Fusarium head blight are shown in figure 3.22. The result illustrated that NIR-R and RE3-R performed better in wheat Fusarium head blight identification. Based on this result, a red edge head blight index (REHBI) was constructed for the monitoring of wheat Fusarium head blight at regional scale. Many vegetation indices have been successfully used for the monitoring of plant diseases. Fourteen commonly used vegetation were also chosen to apply to wheat Fusarium head blight monitoring. The evaluation results revealed that the new index REHBI was the best of all indices, RDVI was the best of all traditional indices, and PSRI1 was the best of all red edge indices. Overall,

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TAB. 7.14 – Testing results of the three monitoring models based on the most suitable multi-temporal vegetation indices. Testing

Field investigation

CART

BPNN

KNN

16

Slight infection 2

5

3

8

21 76.2 19

5 60.0 3

26 22

86.4

2

2

4

50

21 90.5 19

5 40.0 2

26 21

90.5

1

3

4

75

1

0

1

21 90.5

5 60.0

26

Healthy

Methods Healthy Slight infection Sum PA/% Healthy Slight infection Sum PA/% Healthy Slight infection Severe infection Sum PA/%

Sum

UA/%

OA/%

Kappa

18

88.9

73.1

0.295

37.5

80.8

0.330

84.6

0.516

traditional spectral indices outperformed red edge spectral indices in wheat Fusarium head blight severity estimation. The capability of the newly constructed index REHBI in practical wheat Fusarium head blight monitoring at regional scale was tested. The disease development is mainly affected by temperature and humidity. The disease habitat characteristics were represented by mean land surface temperature and mean precipitation from April to May, which were MOD11A1 data and Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The SVM method was used to the monitoring models establishment based on the two conventional indices RDVI, and OSAVI, and the new index which respectively combined with the two habitat features land surface temperature and precipitation. Meanwhile, the models were tested by a leave-one-out cross validation method. Table 7.16 shows the estimations of the three monitoring models with the three indices. The overall accuracy and kappa coefficient of REHBI were the highest among these models at 78.6%, and 0.51, respectively. Figure 7.10 illustrates the disease spatial distribution produced by the monitoring model based on REHBI.

7.3.2

Monitoring at National Scale

Wheat Fusarium head blight destroyed the cellular integrity of the impacted tissues leading to cell death and degradation of chlorophyll. Wheat Fusarium head blight

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FIG. 7.9 – Disease spatial distribution based on the conventional single date vegetation indices.

criterion refers to the national standard (GB/T15796‐2011). Table 7.17 lists the detailed severity classes. Ground disease investigation data and remote sensing imagery were both collected. In addition, the level 1 and level 2 constituted the Fusarium head blight slight infestation, level 3 constituted moderate infestation, and level 4 and level 5 constituted severe infestation (table 7.17). First, according to field investigation and existing study knowledge, prior wheat Fusarium head blight spatial position data were used. Based on NDVI (equation (7.5)) and DVI (equation (7.6)), a wheat Fusarium head blight index (WFHBI, equation (7.7)) was then developed for the Fusarium head blight monitoring. This developed index considered wheat ear spectrum and wheat canopy

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TAB. 7.15 – Testing results of the three monitoring models based on the conventional single date vegetation indices. Testing

Surface disease investigation

CART

BPNN

KNN

7

Slight infection 1

8

2

10

6

2

8

21 33.3 8

5 40.0 0

26 8

100

12

5

17

29.4

1

0

1

21 38.1 17

5 100 2

26 19

89.5

4

3

7

42.9

21 81.0

5 60.0

26

Healthy

Methods Healthy Slight infection Severe infection Sum PA/% Healthy Slight infection Severe infection Sum PA/% Healthy Slight infection Sum PA/%

Sum

UA/%

8

87.5 20

OA/%

Kappa

34.6

0.035

50.0

0.201

76.8

0.355

spectrum. Finally, we combined with disease environmental indicators which included meteorological features such as rainfall, crop information such as wheat growth stages, and remote sensing features such as LST, as well as corresponding historical data to establish DI (equation (7.8)) for the monitoring of wheat Fusarium head blight. NDVI ¼ ðRNIR
RR Þ=ðRNIR þ RR Þ

ð7:5Þ

DVI ¼ RNIR
RR

ð7:6Þ

WFHBI ¼ f ðDNDVI; DDVIÞ

ð7:7Þ

  DI ¼ g WFHBI; G; LST
LSTavg ; R
Ravg

ð7:8Þ

where G means wheat growth stage, f and g are regression analysis processes based on the ground investigation data set. The DI value which ranges from 0%–100%, and 0% < DI ≤ 30% represents slight yellow rust infestation, 30% < DI ≤ 60% represents moderate infestation, and DI > 60% represents severe infestation.

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TAB. 7.16 – Estimation results of the three monitoring models based on the three indices. Field truth

FLDA

LR

MD

Healthy Fusarium head blight Sum PA/% TP/% Type I error TN/% Type II error Healthy Fusarium head blight Sum PA/% TP/% Type I error TN/% Type II error Healthy Fusarium head blight Sum PA/% TP/% Type I error TN/% Type II error

UA/%

OA/%

Kappa

19

84

78.6

0.51

6

9

67

19 84 84 16 67 33 17

9 67 67 33 84 16 6

28

23

74

71.4

0.26

2

3

5

60

19 89 89 11 33 67 17

9 33 33 67 89 11 6

28

23

74

71.4

0.26

2

3

5

60

19 89 89 11 33 67

9 33 33 67 89 11

28

Healthy

Fusarium head blight

Sum

16

3

3

Wheat Fusarium head blight infestation area achieved 0.3 million ha in China in 2019. In late April, Jianghuai, and middle and lower Yangtze River were the places where the Fusarium head blight first appeared. From early May to mid-May, Fusarium head blight reached its reproductive peak, and occurred widely in Jianghuai, southern Huanghuai, and middle and lower Yangtze River. Figure 7.11 and table 7.18, list the disease spatial status and occurrence area in different stages.

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FIG. 7.10 – Disease spatial distribution produced by the monitoring model based on REHBI. TAB. 7.17 – Wheat Fusarium head blight level classification criterion. Severity levels

Index The ratio of infested panicle/% The incidence area ratio/%

1

2

3

4

5

0.1 < Y ≤ 10

10 < Y ≤ 20

20 < Y ≤ 30

30 < Y ≤ 40

Y > 40

R > 30

R > 30

R > 30

R > 30

R > 30

Note: Y is the rate of infested panicle, which refers to the ratio of the number of wheat ears to the total number of ears investigated, and R is the incidence area ratio. Reference: https://www.taodocs.com/p-86284688.html.

FIG. 7.11 – Spatial distribution of wheat Fusarium head blight in 2019 (a) early May, and (b) mid May.

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting TAB. 7.18 – Statistics of wheat Fusarium head blight in China in 2019.

Region

Area/thousand hectare

Northeast China North China East China South China Central China Northwest China Southwest China Total

7.4 7.4.1

Early May 0 22 160.7 0 108.7 25.3 0 316.7

Mid May 0 32.7 168.7 0 118 27.3 0 346.7

Total area 85.3 3579.3 8556 16.7 6710 3376 1828.7 24152

Oriental Migratory Locust Monitoring Monitoring at Regional Scale

The cloudless and partial cloudless Landsat and MODIS imageries from 2000 to 2015 were acquired to extract locust area at Dagang District, Tianjin City, China. Locust area is defined as the habitat suitable for locust breeding, where locust damage occurs at least once in ten years. Taking into account the special ecological status of Dagang District, and the different sensitivity of different land cover types to locusts (Rong et al., 2006; Yang et al., 2007), table 7.19 summarizes the representative land use types and sensitivity to the locust infection. The locust host type quantitative evaluation needed to refer to land use data set. Visual recognition was only pure pixel with a defined land use class. To further determine classes, the national historical land cover surveys of 30 m spatial resolution were applied to mark the initial reference pixel selection. Due to the limitation of suitable historical data, only the investigation data in 2000, 2005, and 2010 were used. Thus, the land use classes in the three years were evaluated. Table 7.20 lists the class reference pixel numbers of the three suitable years. A data fusion method, STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model), was used to fuse Landsat and MODIS images, for which the method has an excellent performance in semi-automatically producing continuous time series Landsat-MODIS fusion data (Gao et al., 2008, 2015, 2017; Semmens et al., 2016). After fusion, the Landsat & MODIS reflectance was evaluated through the Landsat investigations which did not appear in fusion process, and only clear pixels were employed. For quantization of locust habitat landscape structure, pixel-level land use data are important. Therefore, a RF approach based on seasonal features was constructed. The seasonal features were composed of NDVI time series which were calculated by using fused Landsat-MODIS imagery. To reduce the effect of bad data and noise, a Savitzky–Golay filter based smoothing was first used for NDVI

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TAB. 7.19 – Summary of the representative land use types and sensitivity to locust infection. Land use class Reed

Reed and weed

Crop land

Water

Other

Descriptions The reed region which with a vegetation coverage of 40%–100% can provide sufficient food for locust. The reed in the region which with a vegetation coverage of 40%–50% is the best host for locust spawning and nymph growth. The reed region which with a vegetation coverage of 50%–80% is the best host locust migration. However, the canopy density is not appropriate for locust breeding when the vegetation coverage over 80%. Grasses, such as weeds, are mostly reeds on wet or semi-dry soil, with a vegetation coverage rate of 20%–70%. Soil and vegetation status gives a suitable environment for locust reproduction and development. The regions with a vegetation coverage of 20%–50% are the best hosts for locust spawning and nymph growth, while the regions with a vegetation coverage of 50%–70% are the best hosts for locust migration. Crops with vegetation of 10%–60% are mainly include barley, corn, cotton, peanut, sorghum, some weeds and reeds. Due to manual management such as conventional farming and irrigation, field conditions are inapposite for locusts to reproduce or lay eggs, but thriving crops provide an option for locust migration. The water body includes low vegetation coverage regions, such as rivers, lakes, and wet land. Water resources are often related to soil moisture and temperature, which in turn affects the growth of surrounding vegetation. Others include exposed soil with surface salt deposits, urban and rural settlements, and artificial embankments unsuitable for grasshopper breeding.

TAB. 7.20 – Class reference pixel numbers of the three suitable years. Year 2000 2005 2010

Reference pixel number Reed 1880 1875 2181

Reed and weed 2462 2844 2977

Crop land 1904 1887 1651

Water 2015 1989 1247

time series. Locust habitat can be divided into two types, one is the permanent locust region, which mainly includes reed and weed, and the other is the occasional locust region which includes reed, weed, and some gramineous crop. The calculated NDVI time series, TVI time series, and LST were used as remote substitutions of land use, vegetation coverage, and land temperature. A RF method based on landscape membership was used to determine the locust habitat landscape at patch

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scale. Through the STARFM fusion method (Gao et al., 2008), the Landsat-MODIS reflectance in twelve years was produced. The fusion results revealed that the green band, red band, and near-infrared band in 2004, 2007, and 2008 were with obvious deviations. Therefore, in subsequent analysis, the Landsat-MODIS reflectance in these three years was not included. Figure 7.12 lists the annual land use of the remaining 12 years obtained by the RF method with seasonal NDVI. Table 7.21 shows the evaluation results of the land use of the three evaluation years. The results indicated that the overall accuracy of each year reached 80% among the three evaluation years. Then, through the RF model based on LST, NDVI, and TVI time series, the locust habitat spatial distribution during the investigation period was produced (figure 7.13). These results supplied the basis for tracing locust habitat change trends in the 16 years. Table 7.22 gives the confidence values of permanent locust region and occasional locust region. The results indicated that the significance of permanent locust region and occasional locust region in all 12 years were higher than 0.80 and 0.76, respectively.

FIG. 7.12 – Annual land use in the 12 years.

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249

TAB. 7.21 – Evaluation results of the land use of the three evaluation years. Referenced pixels Year 1730 94 56 0

Reed and weed 345 1871 197 49

92

Reed Reed Reed and weed Crop land 2000 Water Producer’s accuracy/% Reed Reed and weed Crop land 2005 Water Producer’s accuracy/% Reed Reed and weed Crop land 2010 Water Producer’s accuracy/%

Cropland Water 58 152 1656 38

20 0 0 1995

76

87

99

1837 38 0 0

228 2503 85 28

0 57 1812 19

0 0 0 1989

98

88

96

100

2007 87 65 22

268 2530 119 60

66 83 1502 0

0 0 0 1247

92

85

91

100

User’s accuracy/% 80 88 87 96

89 96 96 98

86 94 89 94

Overall accuracy/%

84

91

86

A method based on profile angle was improved to identify the pixel landscape difference. The closer pattern of the two years was assumed to appear in the unchanged time series pixels; therefore, the profile angle of these pixels and the basis pixels in the marked land use types were consistent. According to the assumption, the annual landscape difference was evaluated through the profile angle matching method. This method can reveal locust habitat landscape dynamics. A pairwise univariate regression analysis was used to investigate the relationship between locust habitat difference and landscape dynamics. The contribution of each land use difference to locust habitat change can be assessed through the comparison of the investigation truth of locust occurrence region in summer and autumn. Figure 7.14 shows the locust habitat land cover in the twelve years. The results revealed that the average area ratio of permanent locust region and occasional locust regions, respectively, reached 21.35% and 20.91%, respectively, of the gross area. The largest area of permanent locust region appeared in 2002, and the largest area of occasional locust region appeared in 2010. Figure 7.15 shows the location of annual locust habitat difference in the twelve years. On the time scale, the largest permanent locust region changes appeared between 2002 and 2003, while the occasional locust region changed slightly in the whole twelve years. On the spatial scale, the identified differences accounted for a large proportion of the landscape's subtle change.

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FIG. 7.13 – Spatial distribution of annual locust area in the 12 years.

TAB. 7.22 – Locust area confidences of the 12 years. Locust habitat types Permanent locust region Occasional locust region

2000

2001

2002

2003

2005 2006

2009

2010

2011

2013 2014

2015

0.83

0.89

0.9

0.86

0.87

0.88

0.89

0.82

0.88

0.89

0.84

0.8

0.78

0.82

0.84

0.79

0.82

0.84

0.81

0.77

0.79

0.8

0.82

0.76

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FIG. 7.14 – Locust habitat land cover in the twelve years.

FIG. 7.15 – Location of annual locust area differences of (a) permanent locust region and (b) occasional locust region in the twelve years.

7.4.2

Monitoring at National Scale

Oriental migratory locust is one of the most threatening pests in China’s agricultural production (Li et al., 2010; Yang and Ren, 2018). Land use can greatly influence locust region formations and dynamics. Identification of the land use

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change in the locust region is important for pest control. This section provides a habitat suitability evaluation method based on patch scale for the monitoring of locust region at national scale. The locust area dynamics during 1995–2017 were then investigated by using a multi-temporal binary coding method. Finally, the collection of land use and locust deracinate and formation were explored. The data involved for locust area monitoring included satellite data, land use products, locust occurrence records, and auxiliary data. Among them, satellite data included Landsat imagery from 2003 to 2007, and 2015 to 2018 were used to calculate vegetation coverage for extracting locust area in 2005 and 2017. The land use products in 1995 and 2005 were given by Chinese Academy of Sciences. For 2017, a global land use product was obtained. All land use products in these three years were with a spatial resolution of 30 m. The historical locust data from 1990 to 2018 were obtained from a Chinese government agency (National Agro-Tech Extension and Service Center), which included locust area types, occurrence position and area, severity, and management. The soil dataset of 1:1000000 offered by Institute of Soil Science, Chinese Academy of Sciences in 2008 was adopted. The basic geographic information data updated in 2015 was also adopted. The methods for area extraction included three steps. For step 1, a habitat appropriateness evaluation method based on patch scale was developed to monitor locust area based on the data of vegetation coverage, agrotype, land use, and locust region. For step 2, a multi-temporal binary coding method was adopted to analyze locust area dynamics, and the influence of land use on locust region change was analyzed based on these locust types. For step 3, the land use transform matrix was computed to study the collection of land use and locust area change, and the land use class ratio differences of different ecological types were adopted to analyze the influence of land use on locust area depopulation and formation. Based on the above data and method, the national locust region of 1995, 2005, and 2017 in China was obtained. And according to the monitored and statistical locust occurrence area of the three stages, the evaluation results of each year and all three years are shown in figure 7.16. The results illustrated that the two locust occurrence area data were largely related, suggesting that the developed model had the potential to meet the study requirement. According to the locust habitat, the locust region was reclassified into four types (figure 7.17). In the last 20 years, the whole locust region in China was comparatively stable with slight changes. With the passage of time, the locust occurrence area showed a decreasing trend, while the number of locust occurrence counties was not stable (table 7.23). Cropland was the most suitable living environment among the four ecological types (water, wetland, grassland, and cropland) for locusts in the three stages, while artificial ground and other land were not appropriate for the breeding of locust (figure 7.18a). The results of the land use type percentage for different locust areas in figure 7.18b indicated that the riverine locust region and waterlogged locust area

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FIG. 7.16 – Evaluation of monitored locust occurrence area in (a) 1995, (b) 2005, (c) 2017, and (d) all three stages. were more greatly impacted by crop change. The coastal locust region and riverine locust area were more greatly impacted by wet land change, in which, water change had higher influence on lakeside locust area and grass land change had higher influence on riverine locust area. Figure 7.19 shows the locust area dynamic change of the three stages which was obtained through multi-temporal binary coding method. Then, the locust region evolution map was produced (figure 7.19b) and table 7.24 lists the evolution determination process. The results suggested that the variability of the locust time and space were high. Although new locust area appeared, China seemed to have made progress in locust ecological control from 1995 to 2017. The widespread locust area depopulation was accompanied by the newly formed locust area. Although locust occurrence area had decreased, locust still occurred in a considerable region. These results revealed that the locusts breeding conditions were not stable. A stable locust area with the smallest proportion was the key area for locust management.

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FIG. 7.17 – Locust distributions in (a) 1995, (b) 2005, and (c) 2017.

Period 1995

2005

2017

Area/ten thousand hectares Proportion/% Area/ten thousand hectares Proportion/% Area/ten thousand hectares Proportion/%

Coastal locust area

Riverine locust area

Lakeside locust area

Waterlogged locust area

Sum

32.32

40.57

28.22

23.64

124.75

25.91

32.52

22.62

18.95

–

24.62

44.52

28.36

20.00

117.50

20.95

37.89

24.14

17.02

–

19.28

35.65

17.41

14.34

86.68

22.24

41.13

20.09

16.54

–

Distribution range 172 counties in 10 provinces 204 counties in 12 provinces 186 counties in 12 provinces

Multi-Spectral Remote Sensing Monitoring

TAB. 7.23 – Locust occurrence conditions in three different stages.

255

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FIG. 7.18 – Contribution (a) area and (b) percentage of different land use types for different locust regions in three stages. These stable locust areas were mainly located on the land near to river, lake, and beach (figure 7.19b). Those areas which were not appropriate for locust management were due to the fluctuations of water level. Based on multi-source and time-series satellite remote sensing images, this chapter constructs remote sensing monitoring models and methods for wheat yellow rust, powdery mildew, Fusarium head blight and migratory locust by simultaneously considering host growth condition, pests and diseases habitat and their spatial distribution and severities. The research results in this chapter demonstrate the feasibility of multi-source and time-series satellite remote sensing images in pests and diseases monitoring, providing methodological reference for the monitoring of pests and diseases at regional scale.

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FIG. 7.19 – Locust region (a) dynamic change distribution and (b) evolution distribution in the last 20 years.

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TAB. 7.24 – Statistics on dynamic change and evolution type of locust area in 1995, 2005 and 2017. Vdyna

Binary code

1 2 3

2017 0 0 0

2005 0 1 1

1995 1 0 1

4

1

0

0

5

1

0

1

6

1

1

0

7

1

1

1

Evolution type

Area/104 ha

Extinct Repetitive Extinct Newly formed Repetitive Newly formed Stable

57.54 47.38 33.88 35.53

Histogram

14.90 17.80 18.44

References Chen P. H., Huang H., Mai M., et al. (2018) Multi-label feature selection algorithm based on ReliefF and mutual information. J. Guangdong Univ. Technol. 35, 20. Derksen S., Keselman H. J. (1992) Backward, forward and stepwise automated subset selection algorithms: frequency of obtaining authentic and noise variables. Br. J. Math. Stat. Psychol. 45, 265. Ding H., Goce T., Peter S., et al. (2008) Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. Vldb Endow. 1, 1542. Gao F., Jeffrey T. M., Robert E. W., et al. (2008) An algorithm to produce temporally and spatially continuous MODIS-LAI time series. IEEE Geosci. Remote Sens. Lett. 5, 60. Gao F., Martha C. A., Zhang X. Y., et al. (2017) Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sens. Environ. 188, 9. Gao F., Thomas H., Zhu X. L., et al. (2015) Fusing Landsat and MODIS data for vegetation monitoring. IEEE Geosci. Remote Sens. Mag. 3, 47. Huang W. J., Lamb D., Niu Z., et al. (2007) Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precis. Agric. 8, 187. Kim J., Byung S. K., Silvio S. (2012) Comparing image classification methods: K-nearest-neighbor and support-vector-machines. In WSEAS international conference on computer engineering and applications, and proceedings of the 2012 American conference on applied mathematics. Laakso A., Garrison C. (2000) Content and cluster analysis: assessing representational similarity in neural systems. Philos. Psychol. 13, 47. Li G., Wang N. A., Li Z. L. (2010) Study on social influence, environmental significance and ecological explanation of the dynamics of locust plagues in China during the historical period. Prog. Geogr. 29, 1375. Merzlyak M. N., Anatoly A. G., Chivkunova O. B., et al. (1999) Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 106, 135. Rong J., Li D., Xie B., et al. (2006) Spatial distribution of oriental migratory locust (Orthoptera: Acrididae) egg pod populations: implications for site-specific pest management. Environ. Entomol. 35, 1244. Saini I. D. S., Khosla A. (2013) QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases. J. Adv. Res. 4, 331. Sankaran S., Ashish M., Reza E., et al. (2010) A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72, 1.

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Semmens K. A., Martha C. A., William P. K., et al. (2016) Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sens. Environ. 185, 155. Sun Y, Jian L. (2006) Iterative RELIEF for feature weighting. In International Conference on Machine Learning, pp. 913–920. Wang L., Zeng Y., Chen T. (2015) Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst. App. 42, 855. Yang P., Ryosuke S., Wu W. B., et al. (2007) Evaluation of MODIS land cover and LAI products in cropland of North China Plain using in situ measurements and Landsat TM images. IEEE Trans. Geosci. Remote Sens. 45, 3087. Yang P. Y., Ren B. Y. (2018) Promoting the application of green crop pest management technologies—review on the key issues in the national technical schemes of major crop pest management for 2011 to 2017. Plant Prot. 44, 6. Zhang Y., Ding C., Tao L. (2007) A two-stage gene selection algorithm by combining ReliefF and mRMR. In Bioinformatics and bioengineering, pp. 164–171. Zhang Z., Li Z. (1991) The laws of occurence and development of powdery mildew of wheat in Tianshui area. J. Northwest Sci-Tech Univ. Agric. For. 19, 81.

Part Four

Remote Sensing Forecasting for Crop Pest and Disease

Accurate forecasting for crop pests and diseases can enhance the predictability and planning of pests and diseases control. Timely forecasting for crop pests and diseases can make the prevention and control work well prepared and enhance prevention initiative. Crop pests and diseases forecasting not only effectively reduces pests and diseases damage and yield loss, prevention and control costs and pesticide usage, and environmental impact pollution, but also improves prevention and control effect, economic, ecological and social benefits of prevention and control work, making the prevention more economical, secure and effective. The systematic data accumulation of pests and diseases forecasting can help to understand the mechanism of pests and diseases occurrence and development, support the relationship analysis between various factors and the occurrence and damage of pests and diseases in the cropland ecosystem with theory and method of system engineering, and furthermore, promote the design of reasonable and comprehensive prevention and control plan. This work is not only related to the agricultural production of the current year and season, but also has strategic significance for improving the overall benefits of long-term comprehensive management. Traditionally, agricultural-meteorological models are developed for crop pests and diseases forecasting, based on statistical relationship establishment of disaster occurrence and development with agricultural field investigations and meteorological station data. They are mainly used to be applied in certain specific areas. In recent years, an important development of crop pests and diseases forecasting is to incorporate meteorological and plant protection information into GIS framework, and then, BN, SVM and other data mining algorithms are used to conduct comprehensive analysis from the aspects of meteorological suitability and the spatial and geographic relationship of pests and diseases sources, thereby, establishing the pests and diseases epidemic trend forecasting model at the overall region level. Moreover, the previous forecasting of crop pests and diseases mainly rely on meteorological data with coarse spatial and temporal resolution, and seldom consider the host-habitat, such as crop growth status, moisture, land surface temperature etc.,

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for which the differences of pests and diseases risks could not be distinguished in different habitats within the same region. In recent years, optical and thermal infrared remote sensing provides continuous spatial and temporal monitoring of pests and diseases habitats with a fine resolution. Crop pests and diseases habitat and host could be described at fine level to improve the accuracy of pests and diseases forecasting. This part focuses on the related content of crop pests and diseases forecasting research based on multi-source data such as remote sensing data, meteorology data, field data, etc., to analyze the research progresses and development trends of crop pests and diseases forecasting.

Chapter 8 Crop Pest and Disease Forecasting with Multi-Source Data With the rapid development of remote sensing technology, a variety of remote sensing data and models have been widely constructed and applied in crop pests and diseases monitoring. Like crop pests and diseases monitoring, forecasting is also an important mask for crop pests and diseases control and prevention. To realize crop pests and diseases forecasting, it is necessary to analyze the occurrence and damage dynamic monitoring, and use appropriate methods and technologies to scientifically analyze and infer the future state of crop pests and diseases. To analyze the disaster occurrence and development, pest/disease source, host, and habitat should be considered. The pests and diseases can be ideally predicted when the mechanism of pest dispersal and disease infection is understood, and these three factors are acquired. This chapter takes wheat yellow rust, wheat powdery mildew and migratory locust as the objects. The host growth and habitat factors which are closely related to the crop pests and diseases occurrence and development are extracted by integrating with remote sensing, meteorology, and field inspection data. The forecasting of wheat yellow rust, wheat powdery mildew and migratory locust are realized by combining the source information of pest and diseases. The crop pests and diseases forecasting could provide information for scientific prevention and field control, to promote precision agriculture and green agriculture.

8.1 8.1.1

Wheat Yellow Rust Data Acquisition

1. Field investigation From the perspective of the regional epidemic disease, a certain area is usually divided into bacteria source and non-bacteria source based on whether the bacteria source in the area exists or reaches a certain amount. Among them, the occurrence of DOI: 10.1051/978-2-7598-2659-9.c008 © Science Press, EDP Sciences, 2022

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FIG. 8.1 – Incidence of the disease in early April. (a) incidence in 2010; (b) incidence in 2011; (c) incidence in 2012. yellow rust in non-bacterial areas is usually provided by the spores, and the occurrence time, extent and area of yellow rust in non-bacterial areas have a greater impact on the incidence of non-bacterial areas. The Longnan area of Gansu Province is the main source of yellow rust, and its incidence has a greater impact on the incidence of non-source areas in the study area. Therefore, the incidence of Tianshui and its neighboring counties in early April is used as the bacterial population factor of the forecasting model. The incidence of Gansu in the study area from 2010 to 2012 in early April is shown in figure 8.1. 2. Host-habitat NDVI was used as the host growth information. The data source was MODIS-NDVI product MOD13Q1, and the time range was from 2010 to 2012. Forty-six images were obtained every year, in which the time series NDVI data from late April to early May (i.e., 81–128 days) were used to extract the wheat growth information. Due to the influence of cloud cover and aerosol, the obtained original MODIS-NDVI data had burr noise on the time series. To eliminate the influence of noise, TIMESAT software developed by Lars Eklundh et al. was used to filter MODIS data on time series, and Savitzky–Golay (S–G) was selected as the filtering method. NDVI varies with vegetation coverage and types but the NDVI differences among different vegetation at the same period may be not obvious, which makes it

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difficult to use a single-phase NDVI for vegetation types classification. Considering the differences in growing seasons and growing periods for different vegetation could help to improve the classification. Also, due to the differences in climate and planting systems, there are certain differences in vegetation types on different plots, which leads to significant differences in the variation trajectory of NDVI value in different plots within a year. Therefore, vegetation could be classified with time series NDVI. Figure 8.2 shows the change trajectory of NDVI values of different vegetation types within a year. According to ground survey, the main crops planted in the study area were winter wheat and corn. It can be seen from figure 8.2 that NDVI of winter wheat planting plots had one peak, while winter wheat-summer/spring corn planting plots had two peaks, which can be well distinguished with other vegetations. Then, winter wheat planting areas were extracted, as shown in figure 8.3. The extracted wheat planting areas were used to mask the NDVI images, and then the average NDVI value of the wheat planting areas was extracted at county scale. The NDVI from late April (the 81st day) to early May (the 128th day) of each year between 2010 and 2012 were selected for the following processing. (1) The NDVI of the cropland in the study area was averaged by counties, and the NDVI of the above period was accumulated to reflect the activity status of the crop growing season, which was used as the forecasting model variable and represented by NDVI-Total; (2) With time as the abscissa, NDVI value as the ordinate, linear regression fitting was performed on the time series of NDVI of each county in the study area, and the slope of the fitted linear equation was used as the remote sensing factor of the forecasting model. The change in the trend of wheat NDVI with different disease severities in time series is shown in figure 8.4. It can be seen that the NDVI value and the change in the trend of the occurrence and spread of dots were basically the same, and there were obvious differences in the size and increasing trend of the NDVI value and the trend of sporadic occurrence and non-occurrence. The trend difference was not

FIG. 8.2 – NDVI growth curves of different vegetation.

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FIG. 8.3 – Extracted winter wheat planting area.

FIG. 8.4 – Time series NDVI of wheat under different damage levels. obvious. This observation and reflection of crops physiological activities at specific growth stages based on remote sensing information is helpful to evaluate crop susceptibility. Generally, the water and fertilizer status of wheat can affect the

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269

occurrence of yellow rust. For example, the greater the amount of nitrogen fertilizer applied, the more serious the occurrence of yellow rust. The main meteorological factors affecting the occurrence of yellow rust include air temperature, relative humidity, sunshine hours and rainfall. Yellow rust fungus is fond of coolness and could not endure high temperature. It needs a suitable temperature and a humid environment for its growth and reproduction. It needs suitable light and liquid water during the infection process. The duration of various meteorological conditions will also have a greater impact on the growth and reproduction speed of yellow rust. Considering the influence of various meteorological factors on yellow rust, the highest temperature, lowest temperature, average temperature, rainfall, average relative humidity, sunshine hours and other factors that have indicated significance for yellow rust occurrence were selected as candidate variables for forecasting. According to the research results of Cooke, Zeng, etc., the calculated meteorological factors are shown in table 8.1, and the significance of each meteorological factor and the occurrence of yellow rust was tested by an analysis of variance. The results are shown in table 8.1. Among them, only the p-value of the days corresponding to the mean minimum temperature in March and the minimum temperature less than 0 °C in April was greater than 0.5, while the other meteorological factors were significantly related to the occurrence of yellow rust. Meteorological factors with the p-value < 0.05 were chosen as the factors for disease forecasting. Since there may be a greater correlation between similar meteorological factors, correlation analysis would be carried out among meteorological factors that have passed the significance test. According to the results of Pearson correlation TAB. 8.1 – Significance analysis of meteorological factors and incidence degree of yellow rust. Meteorological factors Average March temperature Mean relative humidity in March Days in March when the average relative humidity exceeded 50% Mean maximum temperature in March Mean minimum temperature in March The days in March when the lowest temperature was below 0 °C Average rainfall for March More than 0.25 mm of rain fell in March Average hours of sunshine in March In March, the sunshine hours exceeded 8 h

p-value 0.024 0 0.002 0.009 0.105

0.04 0.039 0.001 0.004 0.001

Meteorological factors Average temperature in April Average relative humidity in April Days in April when the average relative humidity exceeded 50% Average maximum temperature in April Average minimum temperature in April The days in April when the lowest temperature was below 0 °C Average rainfall for April More than 0.25 mm of rain fell in April Average hours of sunshine in April In April, the sunshine hours exceeded 8 h

p-value 0 0 0.003 0 0 0.076 0.014 0.003 0.002 0.018

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TAB. 8.2 – Selected factors for yellow rust forecasting.

Meteorological data

Remote sensing data Bacteria in the source data

Factor Mean relative humidity in March The days in March when the lowest temperature was below 0 °C More than 0.25 mm of rain fell in March Average hours of sunshine in March Average temperature in April The average relative humidity in April exceeded 50% on several days Average rainfall for April In April, the sunshine hours exceeded 8 h Sum of NDVI values from late March to early May Linear fitting slope of NDVI value from late March to early May The incidence area of districts and counties in Gansu province was studied in early April

analysis, the two factors with a correlation coefficient higher than 0.8 had the smaller p-value, and the meteorological factors used for forecasting were finally selected as shown in table 8.2. According to the study of Cooke et al., temperature, relative humidity, rainfall and sunshine are the key meteorological factors affecting the occurrence of yellow rust, and the four meteorological factors have different influencing mechanisms on the occurrence of yellow rust. Therefore, at least one of the meteorological factors should be retained. According to this guiding principle, the finally selected meteorological factors are shown in table 8.2. Since there were outliers in the meteorological data obtained by the ground meteorological stations, which can only represent the data of a certain region and cannot cover all the counties in the study area, the meteorological data should be averaged on a monthly basis. The processed meteorological factors were interpolated at a spatial resolution of 250 m to coordinate with MODIS-NDVI image data. The average value of each factor was extracted from the interpolated data by counties. Finally, the monthly average data of county-scale meteorological factors within March and April were obtained.

8.1.2

Modelling and Validation

The occurrence of wheat yellow rust is a complex problem which is comprehensively affected by three factors: pest/disease source, host and habitat conditions. The more comprehensive the information in theory, the more accurate the forecasting of disease development. Studies by Zeng et al. (2006) have shown that the resistance of wheat to yellow rust is affected by the nutritional status and crops growth, while the growth and nutritional status of the crop change over time. A large number of studies have shown that the NDVI obtained by remote sensing has a significant

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correlation with the nutritional status and crops growth, and can reflect the nutritional status and growth information of crops. In addition, remote sensing methods have the characteristics of real-time, high-efficiency, and spatial continuity in acquiring large area data. Spatialized meteorological data are an effective data source for wheat yellow rust forecasting at regional level. The characterization of the distribution and growth of crops by remote sensing information also brings new ideas to wheat yellow rust forecasting. The combination of spatialized meteorological data and remote sensing information under the geographic information system GIS platform brings opportunities for the improvement of disease forecasting capabilities. Based on meteorological data, remote sensing data, and plant protection data, an SVM method for small sample problems was used to construct an SVM forecasting model to study disease forecasting combined with remote sensing and meteorological information. The flowchart is shown in figure 8.5.

FIG. 8.5 – Flow chart of wheat yellow rust forecasting model with SVM.

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Taking Guanzhong plain of Shaanxi province and some counties where yellow rust frequently occurs in Gansu province as an example, SVM was used to predict the occurrence of yellow rust in the region from 2010 to 2012. When establishing a disease forecasting model based on SVM, the selected factors which influence the occurrence of disease were taken as input variables of the model, and the occurrence of disease was taken as the output of the model. To facilitate the establishment of the forecasting model of yellow rust, the occurrence was divided into four conditions, i.e. healthy (represented by 0), slight (represented by 1), moderate (represented by 2), and severe (represented by 3). The study area belongs to the typical yellow rust epidemic spreading area, i.e. the Northwest-North China epidemic area. It includes 34 counties, with 23 counties in Gansu Province and 11 in Shaanxi Province. Among them, Tianshui in Gansu is mountainous and varies greatly in elevation. Low-elevation areas are warm and humid in winter, and high-elevation areas are cool in summer. Yellow rust bacteria can complete the annual cycle in this area, making it one of the main sources of wheat yellow rust. In Guanzhong plain of Shaanxi province, the summer temperature is higher, and the yellow rust is difficult to get over the summer in comparison the winter temperature is lower, so the yellow rust overwintering rate is low, and its occurrence is mainly affected by the Tianshui area of Gansu province. The data of wheat yellow rust were provided by plant protection station of Gansu province and plant protection station of Shaanxi province. The data of yellow rust in each county of Gansu province in the study area were weekly survey data, and the time range was from the wheat reforestation period to the harvest period. The data included the occurrence area and degree, providing the early source information. Meteorological data were obtained from the China meteorological data sharing service website, including daily standard meteorological data of meteorological stations in the research areas of Gansu and Shaanxi provinces and surrounding areas from March 2010 to June 2012. The forecasting results are shown in figure 8.6. The forecasting results on the spatial distribution of strongly aligned with the actual incidence (tables 8.3 and 8.4). The overall accuracy was improved from 50% to 66.67%, the Kappa coefficient increased from 0.29 to 0.48. It can be seen that the forecasting model which considered the meteorological factors, the information of bacteria source and the information of remote sensing performed better than the forecasting model based on meteorological data alone. The occurrence of yellow rust is the result of bacteria source, host and habitat. Among them, meteorological conditions mainly affect the activity of spores, and host disease resistance mainly affects the extraction of nutrients by spores. The nutritional status of wheat can also affect the occurrence of yellow rust. For example, Wang et al. (2008) showed that normalized vegetation index could well reflect the growth information of crops, such as nitrogen content and water content.

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FIG. 8.6 – Yellow rust forecasting results. (a) actual occurrence; (b) forecasting result containing NDVI information; (c) forecasting result without NDVI information, 1–3 represents the three years from 2010 to 2012. TAB. 8.3 – Yellow rust forecasting result with remote sensing data and meteorological data. Absence Slight Moderate Severe Sum Absence Slight Moderate Severe Sum Accuracy

6 3 0 1 10 0.60

2 4 0 0 6 0.67

2 0 0 2 4 0.00

1 3 0 18 22 0.82

11 10 0 21 42

User accuracy 0.55 0.40 0.00 0.86

Overall accuracy

Kappa

0.67

0.48

TAB. 8.4 – Yellow rust forecasting result with meteorological data. Absence Slight Moderate Severe Sum Absence Slight Moderate Severe Sum Accuracy

5 4 0 1 10 0.50

2 2 1 1 6 0.33

1 0 2 1 4 0.50

1 7 2 12 22 0.55

9 13 5 15 42

User accuracy 0.56 0.15 0.40 0.80

Overall accuracy

Kappa

0.50

0.29

274

8.2 8.2.1

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

Wheat Powdery Mildew Data Acquisition

1. Field investigation The experimental area of wheat powdery mildew forecasting was selected from Shunyi and Tongzhou areas in Beijing. These areas are the main planting areas of wheat in Beijing, and the typical sub-humid continental monsoon climate in warm temperate zone makes them prone to powdery mildew. According to the monitoring experience of plant protection department and the general rule of canopy disease symptoms, the investigation was conducted during winter wheat flowering stage in 2010, 2011 and 2012. All samples were collected from wheat planting area with a diameter of more than 30 m. The contents of the survey were wheat area of occurrence and severity of the disease in this area, and the varieties plant types, planting density of wheat in this area were also recorded. In addition, considering the disease occurrence and development of high uncertainty, some sampling points were evenly distributed in the main wheat fields in Shunyi and Tongzhou districts. And, according to previous experiences of experts from consulting department of plant protection, more points were set in the areas with high disease risk. 90 sampling points were collected in 2010, including 54 points for model training, 36 points for model validation, while in 2011 and 2012, 29 and 32 sampling points were used for model validation respectively. Actually, wheat powdery mildew occurred at a moderate level in 2010, at a slight level in 2011, and almost none in 2012. Since the spatial resolution of HJ-CCD image used was 30 m, each survey sample point was a circular region with a diameter of 30 m. In the investigation, the wheat growth rate and the level of disease were taken as the standard, and the suitable area in the field was selected as the sample field. Differential GPS was used to record the latitude and longitude of the center point of the sample site. In each sample plot, a 5-point survey method with uniform distribution was adopted to conduct the survey, with an area of 1 m2 per point. The survey content was referred to the canopy survey method in the second part of the experiment. After collecting 5 points of data, the DI value of sample was calculated by averaging all values of 5 points. A total of 10 HJ-CCD images and 6 HJ-IRS images of winter wheat at the tillering stage and jointing stage were acquired. The acquisition time and row number of each scene are shown in table 8.5. Since the single-scene HJ-CCD images of 2010 and 2012 cannot cover the entire study area, the two-scene images were mosaicked. Considering that meteorological factors have a very important influence in the whole growth period of winter wheat, the four meteorological factors were obtained, including daily average temperature, humidity, sunshine and precipitation from wheat tillering stage to jointing stage (April 1–May 10).

Data type

Year

HJ-CCD Remote sensing data

2010 2011 2012

HJ-IRS

2010 2011 2012

Temperature, Humidity, sunshine, precipitation

2010 2011 2012

Disease occurrence Level at the survey Site

2010 2011 2012

Meteorological data

Field survey data

Growth period Tillering stage 1 May (P456, R64; P456, R68) 27 April (P455,R68) 3 May (P457,R64;P457, R68) 1 May (P2, R63) 29 April (P453, R69) 3 May (P2, R63) Stage 1 Stage 2 1 April–10 April

11 April–20 April

Number of healthy samples 59 26 32

Jointing stage 13 May (P1, R64; sP1, R68) 13 May (P457, R68) 14 May (P3, R68; P455, R68) 13 May (P4, R63) 15 May (P455, R70) 14 May (P1, R63) Stage 3 Stage 4 21 April– 30 April

1 May– 10 May

Number of disease points 31 3 0

Crop Pest and Disease Forecasting with Multi-Source Data

TAB. 8.5 – Multi-source data list.

275

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2. Host-habitat The crop vitality and planting density also reflect the crop growth status. According to investigations, the crop vitality has a certain relationship with the susceptibility of crops to diseases, and the decline of vegetation vitality leads to weakened resistance to diseases. Planting density also affects micro-habitat conditions, such as canopy humidity, solar radiation, soil moisture content, etc. Therefore, the green, red, and near-infrared band reflectance candidates related to pigment absorption and biomass were the input variables of the forecasting model. In addition, TVI and SAVI were also used as candidate variables for the model. TVI can reflect the growth and stress state of crops, and SAVI can reduce the changes caused by soil brightness. Table 8.6 shows the definition of all indices, in which RSWIR is the short-wave infrared band reflectance. Crop water status is also closely related to the occurrence and development of diseases. Water stress is a physiological disaster, and its impact on plants is comprehensive and multifaceted. These effects will inevitably lead to changes in plant growth status, and this change often plays an important role in the interaction between the host and the pathogen. To obtain this information from remote sensing data, DSWI and SIWSI were used as candidate variables of the model. The definitions of the two indices are shown in table 8.6. Both indices include the near-infrared band and the short-wave infrared band, which had great potential for monitoring crop water stress at the canopy scale. The LST not only relates to air temperature, but also reflects soil respiration and plant transpiration on a small scale. In this part, the HJ-IRS data were used to invert the surface temperature using a single-channel algorithm with higher universality. Meteorological conditions mainly include temperature, humidity, light, wind, rainfall, etc., which are directly related to the reproduction, invasion and expansion of pathogens. At the same time, meteorological conditions also affect crop growth. Among these factors, temperature and humidity, especially humidity, have a greater impact on disease prevalence. After literature investigation and field verification, four meteorological factors, including air temperature, precipitation, sunshine and humidity, were selected as candidate factors for the forecasting model. The selected characteristic factors of various types are shown in table 8.7. The extremely

TAB. 8.6 – Selected spectral characteristics. Spectral features RG RR RNIR TVI SAVI DSWI SIWSI

Formula Green band reflectance Red band reflectance Near-infrared band reflectance 0:5½120ðRNIR
RG Þ
200ðRR
RG Þ ð1 þ LÞ  ðRNIR
RR Þ=ðRNIR þ RR þ LÞ; L ¼ 5 ðRNIR þ RG Þ=ðRSWIR þ RR Þ ðRNIR
RSWIR Þ=ðRNIR þ RSWIR Þ

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TAB. 8.7 – Data types and corresponding characteristics. Affecting factor Plant activity and plant density Water status of plants

Habitat factors

Data source HJ-CCD

Characteristics RG, RR, RNIR, TVI, SAVI

Access time Tillering stage (T) Jointing stage (J)

HJ-CCD, HJ-IRS HJ-IRS

DSWI, SIWSI

Tillering stage (T) Jointing stage (J) Tillering stage (T) Jointing stage (J) 1 April–10 April (S1) 11 April–20 April (S2) 21 April–30 April (S3) 1 May–1 May (S4)

Meteorological data

LST Precipitation, temperature, sunshine, humidity

limited meteorological stations cannot meet the accuracy of the temporal and spatial distribution of meteorological factors, although their measurement accuracy is high, the representativeness of the sample points is poor. To obtain relatively accurate meteorological forecasting factors for the survey sample points, the study used the spatial interpolation method in GIS spatial analysis to expand the information obtained on a small local scale to a larger area, thereby obtaining the meteorological forecasting of each sample point factor. To further explore the sensitivity of the above-mentioned characteristic variables to disease in two phases, the multi-temporal RG, RR, RNIR, TVI, SAVI, DSWI, SIWSI, LST and four meteorological factors, i.e. precipitation, temperature, sunshine, humidity, performed t test on the difference between healthy and disease samples. The more significant the difference, the more sensitive the feature is to disease. When P-value < 0.05, feature factors with confidence levels lower than 0.95 were eliminated. Relevance analysis was performed on the remaining characteristic variables to ensure that the selected characteristic variables were independent of each other. If the coefficient of determination R2 of any two variables reached 0.8 or more, the one which was less sensitive to disease would be discarded to reduce the redundancy of features. The results of feature screening are shown in table 8.8. Finally, RG (RED_J) at the jointing stage, LST at the jointing stage (LST_J), precipitation at stage 2 (Precipt_S2), temperature at stage 4 (Temp_S4), and sunshine at stage 4 were selected. Six characteristic variables and humidity (Hum_S4) in stage 4 were used for subsequent modeling analysis.

8.2.2

Modelling and Validation

With the rapid development of powdery mildew in the field, remote sensing images with high time resolution are needed to predict diseases. Satellites for environmental

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TAB. 8.8 – T-test of multi-temporal characteristic variables in healthy samples and disease samples. Data type

Remote sensing factors

Meteorological factors

Growth period

Variable RG RR RNIR TVI SAVI DSWI SIWSI LST Precipitation Temperature Sunlight Humidity

Tillering stage * *

Jointing stage *** * **

* ** Stage 1 ** *** ** *

** Stage 2 *** ** ** **

Stage 3 * *** **

Stage 4 *** *** ** **

and plague monitoring and forecasting have high revisit cycle and high spatial resolution, which are suitable for diseases forecasting. The probability of disease occurrence is related to habitat conditions, crop growth and meteorological factors. Among them, crop growth is reflected by some filtered spectral characteristics, and the data source is HJ-CCD images. Habitat conditions includes two categories, namely LST and crop moisture content. LST is derived from HJ-IRS image and crop moisture content from HJ-CCD image. In the model training, the remote sensing data and meteorological data of 2010 in tillering and jointing stages were used as independent variables, and the training data in the ground survey data during the flowering stage of 2010 were used as the dependent variables, and the logistic regression model was used for training. The logistic regression model output was the probability of disease occurrence, and the model results used ground survey data of multiple years, i.e. 2010, 2011 and 2012, as verification data. Figure 8.7 is the flowchart of wheat powdery mildew remote sensing forecasting. Logistic regression is currently a commonly used method to estimate the possibility of a certain thing. In real life, many phenomena can be divided into two possibilities, or summarized into two states. The two states can be represented by 0 and 1, respectively. If multiple factors are used to make a causal relationship between phenomenon represented by 0 or 1, it is possible to use logistic regression. Therefore, logistic regression is mainly used to forecast the relationship between a discrete dependent variable and a set of explanatory variables. It can be used for probability forecasting and classification. The main purpose of logistic regression analysis can be summarized into three aspects. First, to find risk factors; second, to forecast occurrence or non-occurrence probability of a certain situation in the case of different independent variables; and third, to distinguish classification, which is

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FIG. 8.7 – Flowchart of wheat powdery mildew remote sensing forecasting. actually somewhat similar to calculate similarity, and it is also based on the logistic model to determine the probability of a certain situation. Therefore, logistic regression is extremely versatile in practical applications, and it has almost become the most important analysis method for the exploration of risk factors in epidemiology and the forecasting of disease occurrence probability. The dependent variable of logistic regression can be binary or multi-category, but binary classification is

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more common and easier to explain, so the most common method used in practical applications is Binary logistic regression. Considering that this method is closer in principle to the actual problem of forecasting the probability of disease occurrence through data from multiple different sources (meteorological data, crop growth and habitat information) in this study, the logistic regression model was chosen to establish a forecasting model of disease occurrence probability. When training the model, the dependent variables were modeled by using 60% of the ground survey sample points. 0 means no disease occurrence, and 1 means disease occurrence. The independent variable consisted of spectral characteristics RR of 1 J time phase, LST data of 1 J time phase and 4 phases meteorological data. There are two ways to verify and evaluate the model. One is to test model fitness on the training data set, and the other is to use the established logistic model to investigate the occurrence of the ground survey samples in different years in the study area. Probability was used for forecasting, and the forecasting accuracy was evaluated using sample points. In the actual disease forecasting, we tried to figure out the information of the occurrence (1) or non-occurrence (0) of the disease, so we chose the Binary logistic regression model for analysis, namely the logit function, and its expression is as follows.
 p log it ðpÞ ¼ ln ð8:1Þ 1
p p¼

expðb0 þ b1 x1 þ b2 x2 þ    þ bi xi Þ 1 þ expðb0 þ b1 x1 þ b2 x2 þ    þ bi xi Þ

ð8:2Þ

Among them, p is the probability of disease occurrence, x1, x2,…, xi are independent variables, β0 is a constant, β1, β2…βi are the coefficients corresponding to each independent variable. From the sample data of the field survey in 2010, 60% samples were randomly selected for model training. Once the model training is completed, the parameters of the model are determined, which can be used to forecast the occurrence probability of powdery mildew. Figure 8.8 shows the forecasting probability distribution of powdery mildew in Tongzhou District and Shunyi District of Beijing in 2010, 2011 and 2012. It can be seen from the thematic map that the incidence of powdery mildew in the southern area of the study area in 2010 and 2011 was higher than that in the northern area. In 2012, the incidence of powdery mildew in the entire study area was lower, which was highly consistent with the results of the field survey. It showed that the forecasting result was more accurate. To explore whether remote sensing factors can improve the forecasting accuracy, we compared the hybrid model established by combining meteorological factors and remote sensing data with the model based on meteorological factors alone. It can be seen from figure 8.9 that the disease occurrence probability distribution obtained by the hybrid model had more accurate spatial information. To further verify the universality and reliability of the model, the remaining 40% sample data in 2010 and the field survey data in 2011 and 2012 were used to further verify the model. The Hosmer–Lemeshow test was used to evaluate the

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FIG. 8.8 – Wheat powdery mildew forecasting results with hybrid model, in 2010 (a), 2011 (b), 2012 (c).

FIG. 8.9 – Occurrence probability of powdery mildew in some regions in 2010, hybrid model (a), meteorological model (b). model fitness. In addition, the forecasting probability of disease occurrence and the actual observed results of disease occurrence (occurrence or non-occurrence) were tested with paired samples to obtain the statistical parameters of Somers’ D, Goodman–Kruskal Gamma and Kendall’s Tau-a, as shown in table 8.9.

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Statistics parameter Hosmer–Lemeshow p-value Somers’ D Goodman–Kruskal Gamma Kendall’s Tau-a

Model type Meteorological model 0.67 0.68 0.70 0.30

Hybrid model 0.97 0.78 0.78 0.34

Hosmer–lemeshow tested p-value, Somers’ D, Goodman–Kruskal Gamma and Kendall’s Tau-a values of the hybrid model were all higher than the corresponding values of the meteorological model, indicating that the forecasting accuracy of the mixed model in training data was higher than that of the meteorological model. Since the logistic regression model obtains a probability output, in practical applications, it is hoped to visually observe which plots are infested and which plots are healthy by setting a segmentation threshold. To find an optimal threshold, the probability threshold range was set between 5% and 95%, with 5% increments, and the verification data in 2010 were used to obtain the overall accuracy, omission error, and misclassification error of each probability threshold. Finally, an optimal threshold for minimizing risk was obtained. Figure 8.10 shows the overall accuracy, omission error and misclassification error of the two models under different segmentation thresholds. With the increase of the segmentation threshold, the missed score error of the two models would increase, and the misclassified error would decrease. For the hybrid model, the overall accuracy was between 61% and 78%. When the segmentation threshold reached 20%, the overall accuracy reached the maximum at 78%. For the meteorological model, the overall accuracy was between 58% and 69%, and the highest overall accuracy was 69% when the segmentation threshold reached 55%. Therefore, the optimal segmentation thresholds for the hybrid model and the meteorological model were 20% and 55%, respectively. Under the optimal segmentation threshold, the remaining 40% data in 2010 were used to verify the model. The verification results are shown in table 8.10. From the results, it can be seen that the misclassification error of the hybrid model and the meteorological model were equivalent, at 17% and 14%, respectively, while the omission error of the hybrid model was 6%, which was lower than the 17% of the meteorological model. In addition, the data of 2011 and 2012 were used to further verify the model. The results are shown in figure 8.11. It can be seen that the misclassification error of the hybrid model was significantly lower than that of the meteorological model. In addition, the logistic regression model can derive the importance of six factors in the model, as shown in figure 8.11. In the Hybrid model, three meteorological factors, including temperature, humidity and sunshine, and a remote sensing factor LST have relatively high weight coefficients, indicating that these four variables contribute significantly to the forecasting model. In the meteorological model, the weight coefficients of temperature, humidity and sunshine are the main factors, which indicate that temperature, humidity and sunshine are the dominant factors in the occurrence and development of powdery mildew.

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FIG. 8.10 – Model accuracy under different segmentation thresholds, hybrid model (a), meteorological model (b).

TAB. 8.10 – Forecasting accuracy of the model in 2010. Reference Year

Model

Precision index

Sample Health Disease Sum

Hybrid model 2010 Meteorological model

Health Disease Sum Health Disease Sum

16 6 22 17 5 22

2 12 14 6 8 14

18 18 36 23 13 36

Overall accuracy

Wrong points error

Leakage points error

78%

17%

6%

69%

14%

17%

FIG. 8.11 – Variable weight coefficient of Hybrid model (a) variable weight coefficient of meteorological model (b).

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8.3 8.3.1

Crop Pest and Disease Remote Sensing Monitoring and Forecasting

Oriental Migratory Locust Data Acquisition

1. Field investigation In 2016, at Dagang District, Tianjin City (38°32′–38°57′ N, 117°13′–117°37′ E), the habitat conditions of the oriental migratory locusts and the data survey over the years were conducted. The data obtained include Tianjin Dagang District locust area division data, data on vegetation types in main locust areas, and ground survey data on the occurrence of locust over the years 2000–2015. And Landsat and MODIS images for many years were downloaded for time series analysis. The study area and images are shown in figure 8.12. 2. Host-habitat The research results of Section 7.4.1 showed that the change of dense reed beach was the main variable of permanent habitat change. 30.2% of the dense pure reeds were transformed into sparse mixed reeds and weeds, and then into the core locust area. And the sparse mixed reed and weed area was the main land cover change in the occasional habitat. 36.8% of the occasional locust area changes were affected by the sparse reed beach change. Totally, 17.8% of locust area changes were affected by changes in cropland, and 7.4% of locust area changes were affected by changes in water body. To improve the calculation efficiency, this study directly used the national land use data sets of 1995, 2000, 2005, and 2010 produced by the Chinese Academy of Sciences as the land surface classification data. The Landsat TM/ETM/OLI remote sensing images were used as the main data. After image fusion, geometric correction, image enhancement and splicing, etc., through the method of human–computer interaction visual interpretation, the country’s land use types were divided into 6 first-class categories, 25 second-class and part of the third-class land use data products. Based on the secondary land use classification of land use, the submerged mask in the unsuitable area of locust was first constructed, and the land use classes which were not suitable for the reproduction and migration of locust were used as the submerged mask of the land cover class. Vegetation coverage (Fv) affects the choice of spawning sites of the migratory locust and the distribution of locusts. Comprehensive consideration of the spatial, temporal and spectral resolution characteristics and data availability of the data are required by each forecasting index, and Landsat data (with relatively high spatial resolution) is then chosen to invert the vegetation coverage index. This study used Landsat data to calculate NDVI to achieve the inversion of vegetation coverage. The average vegetation coverage of no less than 3 phases during the key growth period of locusts from May to September was taken as the final inversion result (figure 8.13).

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FIG. 8.12 – Study area map and MODIS and Landsat data collection (2000–2015), black dots represent collected MODIS data, red dots represent collected Landsat-5 data, and blue dots represent collected Landsat-8 data.

The inversion equation of vegetation coverage is as follows. Among them, NDVI is the actual observed normalized vegetation index, NDVIvegetation is the maximum NDVI of the vegetation area in the study area, and NDVIsoil is the NDVI value of the soil in the study area. Fv ¼

NDVI
NDVIsoil NDVIvegetation þ NDVIsoil

ð8:3Þ

In this study, the single-window algorithm was used to calculate LST and applied in subsequent studies. The algorithm uses Landsat short-wave infrared bands (10th and 11th bands) to construct a radiation transfer equation (Sobrino et al., 2004). However, the LST retrieved by the single-window algorithm cannot represent the average temperature of the whole day. Therefore, we combined the surface temperature data observed by meteorological stations, and used the day temperature model (Degree-day) for the entire regional temperature calculated by

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FIG. 8.13 – Remote sensing extraction of vegetation coverage in the 1990s, 2000s, 2005s, and 2010s.

FIG. 8.14 – Surface average temperature from April to October in the study areas of 1990s, 2000s, 2005s, and 2010s.

reverse evolution. Figure 8.14 shows the average surface temperature of the study area from April to October in the 1990s, 2000s, 2005s, and 2010s. Figure 8.15 shows the results of the spatial distribution of LST in the study area.

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FIG. 8.15 – Surface temperature spatial interpolation in the 1990s, 2000s, 2005s, and 2010s.

8.3.2

Modelling and Validation

Considering that the control of oriental migratory locust relies more on the control of the range and changes of their habitats, this study proposed a method for forecasting changes in the suitable area of locust based on Markov chains. The monitoring result of the locust area was achieved by using time series data, taking into account the spatio-temporal change characteristics of the locust area in each period; and a Markov chain-based locust area forecasting method was constructed to realize the forecasting of the locust area evolution. Based on the analysis of the biological basis of existing locust outbreaks and locust habitat suitability, this study used multi-source fusion method to obtain vegetation coverage and land cover classes. According to the occurrence law of locust biological growth period, a multi-factor assessment model of locust habitat landscape structure suitability was established to quantify the suitability of the locust and the landscape ecological structure at regional scale, and divide the types of locust areas. From the perspective of theoretical analysis and numerical practice, this study first built a method that can effectively improve the extraction accuracy and efficiency of locust area at regional scale. Step 1: We obtained ground survey data such as vegetation coverage, traditional locust area division and population density of locust through satellite-ground synchronous field scientific observation experiments, as well as multi-source and multi-temporal remote sensing observation data during the growth cycle of locust. Then, we quantitatively analyzed the spectral response characteristics between land

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surface class and vegetation coverage and remote sensing observation data to construct at regional-scale inversion model of habitat factors such as land use class, LST and vegetation coverage, and then obtained the large-scale distribution of habitat factors. Step 2: First, we collected the research area overview, historical locust area survey data, and locust area physiological characteristics to build a parameterized prior knowledge base. Then, we set the input parameters and initial conditions of the locust area extraction model based on the prior knowledge, which included (1) 9 input files: water distribution file, wetland distribution file, grassland distribution file, cropland distribution file, locust survey file, vegetation coverage file, LST file, management file and soil profile file; (2) 12 fixed value input parameters: vegetation type and coverage parameters, locust age parameters, locust free migration distance parameters, soil and humidity parameters, temperature and date parameters; (3) 2 adjustable input parameters: window size and landscape structure coefficient; finally, we calculated and extracted vegetation index data under various landscape structures and ecological conditions. Step 3: We calculated the habitat suitability index of locust area by using the canopy coverage, LST and vegetation index data quantitatively retrieved by multi-source and multi-temporal remote sensing, and then verified with the ground survey data to compare locust over the years. The severity of the occurrence survey was compared with the corresponding landscape ecological index to determine the optimal habitat suitability index of locust area in the target study area. Step 4: We used the optimal habitat suitability index of locust as the threshold for locust area extraction and division, which was the final result of the rank and distribution of the locust area. The study area included 36 counties in Henan and Shandong provinces distributed mainly in the middle and lower reaches of the Yellow River (31°53ʹ N–39°17ʹ N, 117°4ʹ E–128°38ʹ E), which is one of the main locust habitats in China. Among them, Shandong Province is located on the east coast of China, in the lower reaches of the Yellow River, with the Yellow Sea and the Bohai Sea to the east, Jiangsu and Anhui to the south, Henan to the southwest, and Hebei to the northwest. The landform is basically dominated by low hills and alluvial plains, with numerous rivers and hills scattered vertically and horizontally. Shandong Province has a warm temperate monsoon climate with drought and rain in spring. It provides a suitable environment for locusts due to the influence of atmospheric circulation and monsoon climate instability, frequent droughts and floods, with a large area of lakeside along the Yellow River, a wasteland in northern Shandong, and extensive cultivation in the region. Its breeding area ranks first in China. Among them, the vegetation in the shallow water areas along the Yellow River is dominated by aquatic or semi-soaked plants, such as Phragmites communis, Polygonum orientale Linn; the vegetation in the floodplains of lake beaches is mainly grassland plant communities, such as Phragmites communis, Cynodon dactylon (L.) Pers., Pinellia ternata, Echinochloa crusgalli (L.) Beauv. The vegetation of the lake beach terraces is mainly field crops and mesophytic or semi-flooded grasses. The Yellow River beach locust area includes the old course of the Yellow River and the Yellow River beach. The locust area is hot and rainy in summer, cold and dry in winter, and windy and sandy

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in spring. The vegetation on the beach of the Yellow River is mainly crops, followed by weeds. The Er tan area is mainly planted with wheat, corn, soybeans, sorghum, etc. The wasteland is mainly composed of Imperata cylindrica (L.) Beauv., Phragmites communis, Echinochloa crusgalli (L.) Beauv and other weeds. The waterlogged locust areas are mainly distributed in southwestern Shandong. The main vegetation is crops, accompanied by a small number of weeds, such as small reeds, thatch, and barnyard grass. Henan Province is located in the middle-east part of China and the middle and lower reaches of the Yellow River. Its terrain is high in the west and low in the east. Henan is located in a warm temperate zone and a northern subtropical region. It has a monsoon climate, with rainy and snowy winter, dry and windy spring, hot and rainy summer, and prime autumn. The huge annual and monthly variability of precipitation caused by the instability of the monsoon circulation, combined with the vast Huanghuai Plain and the variable flow characteristics of related river systems, is very easy to cause floods and droughts. Besides, the Yellow River flooding and diversions in history have formed large-scale sandy land, saline-alkali land, and low-lying water-prone land, which provide favorable breeding environment for locust. The vegetation types on the beaches in the lower reaches of the Yellow River are very rich. The plants in the locust area mainly include wheat, barley, corn, sorghum, rice, sugarcane, soybean, cotton, rape, tobacco, etc. Poaceae, cyperaceae, asteraceae, polygonaceae, lamiaceae, brassicaceae, solanaceae are the main species of wild weeds in the locust area. The formula for calculating the habitat suitability index of locust area is as follows, where W is the weight parameter of the landscape structure of the locust area, L is the weight parameter of the ground zoning survey, and LST is the surface temperature data retrieved by remote sensing. Mi;t0 ðxw=2 ; yw=2 Þ w X w X Wi;j  Lj;k  ¼ j¼1 k¼1

NDVIðxj ; yk Þ
NDVIsoil ðxj ; yk Þ  lnðLSTðxj ; yk ÞÞ NDVIvegetation ðxj ; yk Þ þ NDVIsoil ðxj ; yk Þ ð8:4Þ

Among them, Mi,t0 is the degree of membership of the center pixel to category i, w is the size of the moving window, (xw/2,yw/2) is the center pixel of the moving window. The weight Wi,j determines the degree of image of neighboring pixels to the center pixel, using Euclidean distance to define the spatial distance coefficient. The optimal habitat suitability index of locusts was used for extraction and division of the locust areas, and the final result of the rank and distribution of the locust areas was obtained (figure 8.16). The dynamic monitoring of locust remote sensing monitoring is the basis for its forecasting. In this study, the change detection method proposed by (Tewkesbury et al., 2015) was used to detect changes in the extraction results in four year period. Through different periods (1990s–2000s, 2000s–2005s, 2005s–2010s), the multi-temporal change analysis of locust area was made (table 8.11). During the last 20 years, the overall changes in the study area indicated that the loss of locust area was about 11.9%. These changes are mainly due to the development of the

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FIG. 8.16 – Extraction results of locust areas in the 1990s, 2000s, 2005s, and 2010s. TAB. 8.11 – Interannual variation of locust area. Loss 1990s  2000s 2000s  2005s 2005s  2010s

Area/ha 5743 3126 12826

Increase Percent/% 12.3 7.8 22.1

Area/ha 8948 9428 4827

Percent/% 15.1 18.2 8.4

middle and lower reaches of the Yellow River. Due to changes in river hydrology, sedimentation and wetland, the evolution of wetland reed is the main reason for the reduction of locust areas. The changes in the range of the locust area calculated for each period in the study area show that a significant increase in the locust area was observed from 1990s to 2005s, which was mainly related to the increase in environmental temperature and the decrease in wetland reed vegetation coverage. The substantial decrease in locust areas in 2010s was mainly related to the change in land use.

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The Markov chain is a random model proposed by the Russian mathematician Markov in the early 20th century to describe a series of possible events, where the probability of each event depends only on the state obtained in the previous event (Feitosa et al., 2011). In probability theory and related fields, the Markov process is a random process that satisfies the Markov property. If the future can be predicted based on the status quo, then the process will satisfy the Markov attribute. Therefore, the Markov chain is used to analyze these changing processes (Mishra and Rai, 2016). The Markov chain model in this study was used to forecast the changes in locust area in the study area (Feitosa et al., 2009). This study chose the crop target forecast time from 2010 to 2015, and the forecast results are shown in figure 8.17. The forecast results showed that the coastal locust areas had undergone major changes from 2010 to 2015. The core locust areas had changed by more than 6.2%, and the general locust areas have changed by 4.8%. To verify the significance of the forecasting results, this study used the survey results of Locust Area Agricultural Plant Protective Station in 2018 as the verification data set. The correlation and significance analysis of core locust area and general locust area in each county in the study area and the predicted area showed that the forecasted significance of the forecasting results for core locust area and general locust area was higher than 0.55

FIG. 8.17 – Remote sensing forecasting results of 2015s.

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FIG. 8.18 – Correlative graph of measured core locust area and general locust area and predicted core locust area and general locust area by Markov chain in every county in the research area in 2015.

(figure 8.18). It is believed that the forecasting method of the suitable habitat of locust in this study on at the regional scale can provide scientific guidance for the management of the locust area. The wheat yellow rust, wheat powdery mildew and migratory locust forecasting methods and processes are explained in this chapter. The forecasting models were established by combining with the epidemic mechanism of crop pests and disease occurrence and remote sensing technology, and using remote sensing, meteorological, field survey and other multi-source data. Above research not only reflects the application of remote sensing technology in crop pests and diseases forecasting, but also provides effective information support for the decision-making of plant protection and prevention of crop pests and diseases. Then, how to realize the application of crop pests and diseases methods and models to actual production with the rapid development of Internet technology, GIS technology and remote sensing technology is also the focus at present. The following chapter of this book will focus on the principle, architecture, and application of the remote sensing monitoring and forecasting system of crop pests and diseases based on the big data platform.

References Cooke B. M., David G. J., Bernard K. (2006) The Epidemiology of Plant Diseases. Springer, Dordrecht. Feitosa R. Q., Costa G. A. O. P., Mota G. L. A., et al. (2009) Cascade multitemporal classification based on fuzzy Markov chains. Isprs J. Hotogrammetry & Remote Sens. 64, 159. Feitosa R. Q., Mota G. L. A., Feijó B. (2011) Modeling alternatives for fuzzy Markov chain-based classification of multitemporal remote sensing data. Pattern Recognit. Lett. 32, 927. Sobrino J. A., Jiménez-MuOz J. C., Paolini L. (2004) Land surface temperature retrieval from landsat TM 5. Remote Sens. Environ. 90, 434.

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Tewkesbury A. P., Comber A. J., Tate N. J., et al. (2015) A critical synthesis of remotely sensed optical image change detection techniques. Remote Sen. Environ. 160, 1. Wang J. H., Zhao C. J., Huang W. J. (2008) Quantitative Remote Sensing in Agriculture. Science Press, Beijing. Zeng S. M., Luo Y. (2006) Long-distance spread and interregional epidemics of wheat stripe rust in China. Plant Disease. 90, 980.

Part Five

System and Application for Crop Pest and Disease Monitoring and Forecasting

Chapter 9 Crop Pest and Disease Monitoring and Forecasting System To make the methods and models obtained from the research truly serve agricultural production, automated processing of multi-source data, efficient computation of pests and diseases monitoring and forecasting models, and automated production of thematic maps and reports are needed. With the rapid development of Internet technology, GIS technology and remote sensing technology, more and more researchers have tried to build a stable and timely remote sensing monitoring and forecasting system to serve the actual production. This chapter describes how to use the above techniques to achieve rapid collection of pests and diseases data, and introduces how to integrate multiple functions such as data processing and product production in the system to achieve automated production of pests and diseases monitoring and forecasting products. This chapter first describes the architecture and usage of the “Field Data Acquisition System”, then introduces the “Crop Pest and Disease Remote Sensing Monitoring and Forecasting System” and “National Locust Remote Sensing Monitoring and Early Forecasting System”. This chapter brings the results of theoretical research into practical applications.

9.1

Field Data Acquisition System

In recent years, more and more researchers have done monitoring and forecasting of biophysical and chemical parameters of crops using remote sensing technology (Weiss et al., 2020). Meanwhile, utilizing technologies such as computer network technology, database technology and Geographic Information System (GIS) technology to manage field data and improve the availability of field data are important for crop pests and diseases monitoring and forecasting. Especially, it is of great practical significance to improve the efficiency of field investigation using modern intelligent technologies. Due to the variety of vegetation subjects, it is necessary to conduct field verification frequently. If there is any deviation from the previous records, it is necessary to correct the deviation in time, and make the record. So, the traditional method is time-consuming and complex with few supporting materials DOI: 10.1051/978-2-7598-2659-9.c009 © Science Press, EDP Sciences, 2022

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(Huang et al., 2005). Besides, it needs lots of manpower and material resources to carry out field investigation and data post-processing and analysis (Zhu et al., 2015). With the development of new technology, data collection, recording and synchronous sending to client for visualization could not only enhance the data acquiring efficiency, but also reduce the personnel work. In view of the rapid development of Android and IOS operating system (Wukkadada et al., 2015), as well as the portability and operability of mobile devices, this chapter introduces the field data acquisition system developed in Android and IOS operating system. The mobile App can be integrated into mobile devices such as mobile phones and tablets that support GPS. It can meet the needs of field investigators and improve the operation efficiency. Also, it has a strong practical significance. The field data acquisition system developed in Android and IOS operating system adopts hierarchical design, which mainly includes 4 layers, i.e., system layer, business layer, data layer and application layer. The structure is shown in figure 9.1. The system can quickly record the local vegetation growth and the occurrence of pests and diseases. Users can also view historical sample records at any time to manage field survey data. The most important thing for field data collection is to standardize and uniformly organize and record vegetation data (Jennings et al., 2009). Therefore, the design of data collection and management modules is very important. This design regards this aspect as the core business module of the system. For data collection, we first create a new task name, and then edit the related attributes of the task, such as latitude and longitude, collection time, vegetation type, planting density, and taking photos for field sampling. The interfaces of this system mainly include login interface, data record interface, sample attribute interface, data collection interface and data management interface, data export interface. All interfaces make up the

FIG. 9.1 – Field data acquisition system architecture diagram developed in Android and IOS operating system.

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workflow of the entire system. When using the App, the user needs to log in with the password. After logging in, the mobile App will automatically obtain the user’s current location and users could view all historical data information of the account. Users can upload data in “Basic Information”, “Plot Information”, “Growth Status”, “Pests, Diseases, and Weeds”, “Physical and Chemical Parameters”, “Remarks” and “Photos” to record relevant information of the surveyed plots. The overall process is shown in figure 9.2. To verify the stability and reliability of the data acquisition system, a system test application was carried out. After system testing, this Android/IOS terminal-based field data collection system now is safe, reliable, easy to use, widely used, accurate in positioning, and can basically meet the general needs of plant protection workers. Realizing the digitization of field data collection not only improves the efficiency of field work (Berger and Platzer, 2015), but also reduces duplication of labor, which adds convenience to the field collection work in the process of plant protection. This chapter takes a vegetation data survey in a certain place in Beijing as a case to introduce the collection process and steps of this system in detail.

FIG. 9.2 – Overall flow chart.

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The user clicks the application icon to open the system, and then the main interface will display, with the background as a map showing the current location. Then users log in with the username and password, and enter the task name to start the collection operation. Figure 9.3 shows the application icon, main interface, and login interface of App. When obtaining sample information, the program will automatically obtain its latitude, longitude and current address. The user needs to input vegetation and plot information according to the actual situation, including plot name, plot area, vegetation type, etc. If you choose agricultural land, the App will ask you to continue to input information about vegetation growth status, pests and weeds occurrence. If it is not agricultural land, some options will be invalid. Some options are optional, and users can complete them as soon as possible. Finally, remember to add a sentence of remarks, as well as the real picture shooting; then after clicking the “Save” button, the sampling points will be saved locally. If there are multiple sampling points, you can continue to add them without creating a new task. Figure 9.4 shows the data collection interface and data upload interface of App. Corresponding to the field data acquisition App, the field data acquisition system also includes a data management system. The data management system provides a powerful field experiment data management function, which is convenient for administrators to add, modify, delete, and query experimental data (Jain et al., 2019). The goal of this system is to realize the systematization, standardization and automation of experimental data management, and achieve the purpose of improving the efficiency of experimental data management. Entering the 21st century, computer technology is rapidly developing towards networking and integration. The traditional stand-alone version of the application software is gradually

FIG. 9.3 – Application icon, main interface and login interface of App.

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FIG. 9.4 – Data collection interface and data upload interface of App. withdrawing from the market, replaced by a new generation of software that supports the network and multiple data information. At present, there seems to be two kinds of software, one is client–server (C/S) structure application system, and the other is browser–server (B/S) structure application system (van Oorschot, 2020). The latter is characterized by the direct use of powerful browser software as the interface on the client. Its advantage is the high efficiency of software development. The client is not restricted by the operating platform or geographical area, and the network transmission volume is small (Zhou et al., 2019). It is suitable for local area network and more suitable for the Internet. This system is developed by using the B/S structure. The system is mainly composed of functional modules such as experimental data maintenance, role management, personal information management, etc., to realize user management of field experimental data. Figure 9.5 shows the login interface of data management system. The main contents are as follows. (1) Data maintenance module  Administrators can add, delete, view, and modify App option settings (figure 9.6).  Administrators can add, delete, view, and modify field experiment data.  Users can view and filter the field experiment data.  Users can view their basic information, add and modify personal details (figure 9.7). (2) Permission and role management module The administrator can add and modify role permissions (figure 9.8). The data management system provides convenient management functions and an online information retrieval platform. Users can check field survey data through this system, and administrators can manage all data and user information. Based on the Apache Tomcat container operating platform, the system uses Dreamweaver to

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FIG. 9.5 – Login interface of data management system.

FIG. 9.6 – App option settings.

FIG. 9.7 – Basic information viewing of the user.

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FIG. 9.8 – Adding administrator. design the overall framework. The system uses JSP programming language, MYSQL database and Apache Tomcat as the development platform, and uses software engineering ideas and methods to quickly and effectively develop the system. The Field Data Acquisition System provides a fast and convenient field information acquisition tool for plant protection and scientific research workers. This system is of great significance to promote the development of crop pests and diseases remote sensing.

9.2

Crop Pest and Disease Remote Sensing Monitoring and Forecasting System

Using monitoring methods and models only cannot meet the needs of ecological monitoring. Applying methods and models to daily life to ensure ecological safety is the ultimate goal of crop remote sensing monitoring and forecasting research. At present, the convenient storage, calculation and rapid transmission of large amounts of remote sensing data and vector data are important factors to achieve the promotion of crop monitoring and forecasting methods and models (Zhang et al., 2019). In recent years, with the rapid development of Internet technology, GIS technology and remote sensing technology, more and more researchers are trying to combine the three to build a stable and timely remote sensing monitoring system platform to serve actual production (Avtar et al., 2019). The development of Internet technology has made the rapid transmission of large amounts of data possible. GIS technology has powerful spatial data storage and management capabilities, and can realize online computing. These two technologies provide the ability to popularize the results of crop monitoring based on remote sensing technology, and also enable

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timely and accurate monitoring for crop at large-scale (Gu et al., 2009). Figure 9.9 showed the relationship between geographic information system technology, remote sensing technology and Internet technology. The current popular GIS system architecture includes C/S mode, stand-alone mode, B/S mode, multi-layer C/S mode, etc. Among them, C/S mode and B/S mode are widely used. The basic operating relationship of the C/S technology application system is embodied in a “request/response” mode. When a user needs to access the server, the client sends a “request”, while the server accepts the “request” and “responses”, and then executes the corresponding service. The execution result is sent back to the client, where it is further processed and submitted to the user. With the development of network technology, one of the advantages of C/S technology is that it has strong interactivity and a more secure access mode. The speed is generally faster than B/S, which is more conducive to processing large amounts of data. However, static web pages cannot provide sufficient interactive functions, and dynamic information publishing is relatively difficult. The database needs to be connected to the Web server for users to query or update; publishing dynamic information can be as simple as changing only a few records or fields in the database.

FIG. 9.9 – Relationship between geographic information system technology, remote sensing technology and Internet technology.

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In this way, B/S starts a large number of applications. First, it simplified the client. For the B/S mode, it has no need to install different client applications on different clients like C/S mode, and the users only need to install common browser software, which not only can save the hard disk space and memory of the client, but also make the installation process easier and the network structure more flexible. The B/S system has more Web servers. Users use Web browsers to access Web pages, and interact with the database through the forms displayed on the Web pages. The information obtained from the database can be displayed on the Web in the form of text, images, tables, or multimedia objects. As shown on the page, with the development of Internet technology, especially wireless network technology and network computing technology, the B/S architecture has gradually become a public-oriented GIS system with the advantages of simple client use, low price, and convenience for online information release. Based on the advantages and disadvantages of the two models, this system uses the Internet-based B/S structure, and the system is built on a wide area network (Dong et al., 2020). The user interface is a thin client through the WWW browser. The overall system framework is divided into display layer, business logic layer, data access layer and data service layer, as shown in figure 9.10. The data service layer stores a large amount of data, including remote sensing data, meteorological data, spatial data, and historical experience data. The data access layer provides access to the database. The business logic layer performs remote sensing image index calculations, spatial interpolation of meteorological data, and analysis and calculation of models. Based on crop growth and habitat information, combined with the agricultural meteorological database and the corresponding pests and diseases monitoring and forecasting models, the occurrence range, distribution, occurrence level and trend of crop pests and diseases could be determined. Then, based on historical empirical data, the corresponding tuning method is given. The display layer used to complete interaction with users is responsible for responding to user instructions and providing users with crop pests and diseases monitoring and forecasting information. The crop pest and disease remote sensing monitoring and forecasting system uses ArcGIS Server to calculate, render and publish data. ArcGIS Server is a

FIG. 9.10 – Framework of Crop Pest and Disease Remote Sensing Monitoring and Forecasting System.

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FIG. 9.11 – ArcGIS server architecture diagram. development and application tool provided by ArcGIS for network GIS services, and it includes most of ArcGIS data operation and management functions. After ArcGIS Server is deployed on the server side, multiple client-level browsers connected to the server can call related GIS services of ArcGIS Server and obtain data processing results through cloud computing and cloud services. Figure 9.11 showed the ArcGIS Server architecture diagram. After the data are processed and uploaded to ArcGIS Server, the system will automatically use Apache Tomcat to open the server port for users in the local network to access. Both Apache and Tomcat are projects developed by the Apache open source organization to handle HTTP services, and they are often used together (Fall et al., 2019). The Apache server is implemented by C language and is specially used to provide HTTP services. It is simple, fast, and stable, and can process all static page and picture information. Tomcat is a JSP server developed by Java that conforms to the Servlet specification of JavaEE, and it can process dynamic page information. When they are used together, if the user needs to process static pages, they will be processed by Apache to process and transmit data and information in a timely manner. If the user needs to process dynamic page requests, Apache will forward the parsing work to Tomcat for processing, and Tomcat will return the results through Apache after processing. Figure 9.12 showed the Architecture diagram of Apache and Tomcat. Port mapping technology is used in the system to map server ports to public IP for Internet users to access (Vanelslander et al., 2019). The port mapping technology is realized by Peanut Shell software, which is a dynamic domain name resolution software that can assist users in establishing an Internet host with fixed domain names and maximum autonomy. The main interface of the crop pest remote sensing monitoring and forecasting system is shown in figure 9.13, and the system includes three modules in terms of functions, namely, user retrieval module, scientific report module and order submission module.

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FIG. 9.12 – Architecture diagram of Apache and Tomcat.

FIG. 9.13 – Main interface of Crop Pest and Disease Remote Sensing Monitoring and Forecasting System.

The user retrieval module can help users query the results of remote sensing monitoring and forecasting of crop pests and diseases (figure 9.14). The user selects the specific year, month and region as required. According to the time and location selected by the user, the server will retrieve the data in the database. The Scientific Reports module helps users to select pests and diseases thematic maps for online browsing and downloading as needed. The pests and diseases thematic maps are automatically generated and saved in the database after the monitoring and forecasting results being generated, so the server only plays the role of

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FIG. 9.14 – Monitoring result of wheat yellow rust. online transmission. Figure 9.15 showed a scientific report on remote sensing monitoring of wheat pests and diseases. If the type, time or area of crop pests and diseases provided by the system does not meet the user’s needs, the user can submit information feedback through the order submission module (figure 9.16). The user needs to leave contact information and detail the type, time and area of crop pests and diseases that he would like to monitor and forecast. After the requirement is submitted, the administrator will review the user’s requirement in the background. The administrator will call relevant data and models to monitor and forecast the pests and diseases, and finally the corresponding monitoring and forecasting results will be fed back to the user. The database uses SQL Server database, which is an excellent database management system launched by Microsoft. The SQL Server 2005 database engine provides more secure and reliable storage functions for relational data and structured data, allowing users to build and manage for business. The high-availability and high-performance data application program can not only effectively perform large-scale online transaction processing, but also complete many challenging tasks such as data warehouse and e-commerce applications. Its functional framework is shown in figure 9.17 and the main functions implemented are listed as follows. 1. It is capable of centralized management of large-scale spatial data. The data in the system are mainly divided into raster data and vector data, of which raster data include remote sensing image data and meteorological data, and vector data include meteorological station data and field experiment data. 2. The system can provide multi-user concurrent access capability. The structure of the crop pest and disease remote sensing monitoring and forecasting system adopts B/S mode, and external users can access the system through computer network or other communication facilities to perform distributed operations such as time selection, parameter inversion, pests and diseases monitoring, forecasting

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FIG. 9.15 – Scientific report on remote sensing monitoring of wheat pests and diseases.

and thematic map download, which requires the database of the system to be equipped with the ability to handle multi-user concurrent access, putting forward higher requirements for database security at the same time. The system data include spatial data and tabular data, document data, model data, etc. Spatial data are integrated by using spatial engine technology. The technology adopts an object-oriented design method, organizes spatial data in units of data sources, and defines consistent spatial access interfaces and specifications. The data source can be physically stored as a file or a database. The essence is to store the data in the data source in a series of two-dimensional tables in the specified database. Data sources include vector datasets and raster datasets. The main data include wheat pests and diseases related data, basic geographic data, meteorological

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FIG. 9.16 – Interface of order submission module.

FIG. 9.17 – Database of crop pest and disease remote sensing monitoring and forecasting system.

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data, remote sensing data, user information, etc. To facilitate the organization and management of the massive information in the database, the database is generally divided into a spatial database and an attribute database. Spatial database mainly includes geographic information database with obvious spatial location information used for GIS spatial analysis and visualization analysis applications. The attribute database is mainly used to store and manage descriptive data related to monitoring and predicting the occurrence of wheat pests and diseases. 1. Basic geographic data. Basic geographic data include national provincial and county-level administrative division maps, artificially obtained distribution maps of main crop planting species with the obtained vector data stored in *.shp format, and national DEM data which correspond to the remote sensing image during disease and insect pest incidence period, etc. 2. Pest-related data. It mainly includes field survey data, habitat data, field test data, control information, etc., such as detailed records of pest/disease type, hazard characteristics, development process, pest/disease status index, soil, hydrology, biological conditions and measurement data of field surveys or field trials, and guidance for the prevention and control of various pests and diseases. Among them, the data recorded in table form are stored in the form of conventional table; the data containing geographic information are stored in the form of vector or raster dataset. 3. Meteorological data. Meteorological data are an important parameter for pests and diseases forcast. National meteorological stations and data are in units of days, recording daily average temperature, maximum temperature, minimum temperature, precipitation, wind speed, and sunshine time. It can calculate and generate monthly and annual data as needed. 4. Remote sensing data. Remote sensing data are the main data for pests and diseases monitoring. It is mainly used to monitor crop growth and habitat information, including vegetation index, surface temperature, soil moisture content, etc. 5. User information. It records user registration information, login information and historical browsing information, etc. To facilitate the management of data, a back-office management system has also been developed and set up; the main functions of the back-office management system are listed as follows. Data management: Data management functions include basic operations such as retrieval, addition, deletion, and modification of spatial data based on data tags, in which data retrieval can be carried out according to time or type. Personnel management: Personnel management functions can modify the account and password information of all users of the system, including the administrator’s information; the administrator can modify, add and delete the user’s account and password operations in the background management system.

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Crop Pest and Disease Remote Sensing Monitoring and Forecasting

National Locust Remote Sensing Monitoring and Early Warning System

Locust is one of the main pests that threaten food production (Loew et al., 2016). The locust belongs to the order of Orthoptera, and there are more than 10000 species of locusts in the world, mostly distributed in tropical and temperate regions (Salih et al., 2020). A locust has one pair of wings on each side of the middle and hind thorax, which are called forewing and hindwing respectively (Dkhili et al., 2019). Locusts have been known to cause serious damage to agriculture, forestry and grass, and there are more than 300 species of locusts in China (Zhao et al., 2020). In addition, China is a vast area with a wide variety of plants, which is suitable for locusts to live and reproduce, so the control of locusts has always been a serious problem. The flying locusts have the habit of migrating over long distances, and they are one of the main pests in food production. Living in the new era of the rapid development of computers, the rapid, efficient and accurate dissemination of information has become the goal that people pursue in social life. When computers appeared in the 20th century, it was a leap forward for the whole world, and it made human society’s informatization degree improve continuously (Ivanko et al., 2019). From national military to personal applications, computers play an important role in every field of society, leading the world to the fast-developing information age and revolutionizing the whole society (Wang and Lu, 2019). In the context of the rapid development of computers, using computer networks to promote agricultural knowledge and information is undoubtedly an effective and practical way to bring agriculture into the information age, which will certainly promote the speed and efficiency of agricultural informatization (Tian et al., 2020). This chapter builds a National Locust Remote Sensing Monitoring and Early Warning System based on the spectral characteristics, habitat characteristics and distribution of locusts by combining software engineering and plant protection knowledge with information technology. JAVA is the programming language; Microsoft Visual Studio 2005 is the development environment; and SQL Server 2000 is the data platform. This system can present the locust monitoring and forecasting results, and thematic reports of China’s locusts quickly, easily, and conveniently to the requesting users and organizations through network services. The framework of the system is based on the required data, user requirements for the system; the design of the software program and the overall structure are shown in figure 9.18. The system is structured in B/S mode. External users can access the system through computer network or other communication facilities to perform distributed operations such as thematic query, service retrieval, data update, analysis, and processing. This requires the system database to handle concurrent access for multiple users, putting forward higher requirements on database security. The types of data used for locust monitoring and forecasting are complex and time-sensitive, which require large amounts of storage space. At present, it is difficult

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FIG. 9.18 – Framework of National Locust Remote Sensing Monitoring and Early Warning System.

for general database software to meet the efficient storage and management of this data. Spatial database engine technology solves this problem well. The spatial database engine ArcSDE launched by the American ESRI company is currently one of the most widely used and most stable spatial database engines (Zhao et al., 2019). It does not store data itself, but serves between the GIS platform (users) and the relational database management systems (RDBMs). It provides a way to store and manage multi-user spatial data, and is a bridge for ecological environment data to access the database. The system’s database uses SQL Server database, which is a database management system from Microsoft Corporation. SQL Server 2000 database engine provides more secure and reliable storage for relational and structured data. The database not only helps users to perform large-scale online transactions efficiently, but also performs many challenging tasks, such as data warehousing and e-commerce applications. The logging interface of the National Locust Remote Sensing Monitoring and Early Warning System is shown in figure 9.19, and the main interface is shown in figure 9.20. The system includes five modules in terms of functions, i.e., user retrieval module, scientific report module, monitoring and forecasting methodology module, data analysis module and user feedback module.

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FIG. 9.19 – Login in interface of National Locust Remote Sensing Monitoring and Early Warning System.

FIG. 9.20 – Interface of National Locust Remote Sensing Monitoring and Early Warning System.

The user retrieval module allows users to search for locust remote sensing monitoring and forecasting results on demand. The user selects the specific year, month, and region as needed. Based on the time and location selected by the user, the server will retrieve the data from the database and finally display it in the user’s

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browser. Figure 9.21 showed the display of locust forecasting result of Shandong province by using user retrieval module. The scientific reports module allows users to view and download scientific reports and thematic maps for locust monitoring and forecasting as needed. The user needs to first filter the scientific reports by time and type as needed, and when the filtering is completed, the system will display the locust monitoring and forecasting scientific reports that meet the criteria. The user can select a specific report to view, and then the scientific report and the corresponding thematic maps will be displayed in the browser terminal for the user to view. The user can download the entire scientific report, or specific locust monitoring and forecasting thematic maps as required. The monitoring and forecasting methodology module describes the process of locust monitoring and forecasting, as well as the data and methodological models used in the system. This module can provide users with an overview of the overall monitoring and forecasting process of the system (figure 9.22). In the data analysis module, users also need to select a specific year, month and region to query the locust remote sensing monitoring and forecasting results (figure 9.23). However, unlike the user retrieval module, this module displays not only the results of locust monitoring and forecasting, but also the land use type, total area of locusts, area of locusts of different severity and corresponding thematic map of the area. The module provides users with a deeper and more comprehensive understanding of locust occurrence in the area of interest. In the user feedback module (figure 9.24), users should first select a specific year, month and region to inquire about the area of locust in that region. If the user has doubts about the data of that region, or if the user finds deviation of the data in the

FIG. 9.21 – Display of locust forecasting result of Shandong province by using user retrieval module.

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FIG. 9.22 – Interface of monitoring and forecasting methodology module.

FIG. 9.23 – Interface of data analysis module. system, he/she can give feedback in this module and the result will be directly transferred to the backend of the system. The system administrator will verify the data of the corresponding region after getting the feedback from the user, and correct the locust monitoring and forecasting results when needed. The National Locust Remote Sensing Monitoring and Early Warning System is a system for collecting information on locust occurrence with the aim of realizing information-based locust monitoring and forecasting. The system uses database

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FIG. 9.24 – Interface of user feedback module.

technology to build a database, which is programmed in JAVA under the framework of ASP.NET. The development environment is Microsoft Visual Studio 2005, and the database management platform is SQL Server 2000. Locust Remote Sensing Monitoring and Early Warning System enables nationwide locust monitoring and forecasting and provides the results of monitoring and forecasting in the form of scientific reports and thematic maps to a wide range of users. The system is of great significance to the management of locust areas and locust control in China. Easy access to field data of crop pests and diseases and automated production of pests and diseases monitoring and forecasting products are essential for the promotion of remote sensing monitoring and forecasting technology for crop pests and diseases. This chapter builds the intelligent systems to achieve rapid data collection and automatic production of pests and diseases monitoring and forecasting products. The “Field Data Acquisition System” can provide sufficient field data. “Crop Pest and Disease Remote Sensing Monitoring and Forecasting System” is capable of automated pests and diseases monitoring and forecasting at multiple scales, and produces thematic maps and scientific reports. “National Locust Remote Sensing Monitoring and Early Warning System” can monitor locusts on a time-series basis and forecast their migration process. These systems are the integration and application of crop pests and diseases monitoring and forecasting models. They are developed to combine theoretical models with practical applications so that remote sensing technology can serve the development of modern agriculture.

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Summary

Food security, as a hot spot of the international community, is important to economic development and social stability. Due to global climate change, crop pests and diseases have become obviously more prevalent with expanded scope. Pests and diseases have become one of the important factors which threatens food security and restricts agricultural production, posing challenges for its monitoring, forecasting, prevention, and control. This book brings together the related scientific research achievements of Vegetation Remote Sensing & Pest and Disease Application Research Team in the field of pests and diseases monitoring and forecasting in recent years. For pests and diseases monitoring, multi-scale remote sensing identification, differentiation and monitoring models have been established to achieve enhanced quantitative monitoring of pests and diseases at leaf, ear, canopy, regional, and national scales. For pests and diseases forecasting, the remote sensing, meteorology, plant protection and other multi-source datasets were integrated with pests and diseases migration, prevalence, and spread modes to construct the models for pests and diseases quantitative forecasting. Then, Crop Pest and Disease Remote Sensing Monitoring and Forecasting System was built by gathering multi-source data, monitoring and forecasting models, pests and diseases thematic products, etc., to achieve long-term and continuous online release of pests and diseases services, and support major national decisions and global pests and diseases prevention and control. Effective prevention and control of pests and diseases has always been one of the biggest international concerns. We will continue to carry out monitoring and forecasting of pests and diseases to provide spatial products and services. The traditional point-to-area monitoring methods and agro-meteorological forecasting models cannot meet the needs of large-scale spatiotemporal continuous pests and diseases monitoring and forecasting, and green prevention and control. Remote sensing technology with the characteristics of real-time, fast, and wide coverage has advantages in large-area and rapid spatiotemporal continuous observation of pests and diseases. The development of emerging technologies such as artificial intelligence and big data also provides a guarantee for improving the accuracy of multi-source data fusion and the timely and effective production of time-space

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Summary

continuous pests and diseases monitoring and forecasting products. Research on early warning and dynamic monitoring of pests and diseases will help to achieve efficient green prevention and control of pests and diseases to ensure food security and ecological safety. In future, the field of remote sensing monitoring and forecasting of pests and diseases will still face many problems to be solved. It is necessary to enhance the mode for integrating remote sensing radiation transmission mechanisms with pests and diseases occurrence and development mechanisms to promote the development of basic theory of pests and diseases remote sensing monitoring and forecasting. Then, according to the actual needs of prevention and control, building the bridge from scientific study results to prevention strategies and precision agricultural machinery operations is the key to the development of smart agriculture. Moreover, establishing comprehensive spatial information platform for global and regional pests and diseases monitoring and forecasting could provide decision-making services for the joint prevention and control of major international and domestic institutions and government functional departments, to effectively ensure agricultural production safety and regional stability.