Plant Disease Forecasting Systems: Procedure, Application and Prospect 9819912091, 9789819912094

Table of contents :
Preface
Acknowledgements
Contents
About the Authors
Abbreviations
1: Historic Plant Disease Epidemics
1.1 Irish Famine
1.2 Bengal Famine
1.3 African Famine
References
2: Epidemic Factors
References
3: Predicting Variables
3.1 Initial Inoculum
3.2 Weather
3.3 Shrun´s Hypothesis
References
4: Criteria to Develop Forecast
4.1 Capable of Inflicting Significant Damage
4.2 Sporadic in Nature
4.3 Seasonal Variation of the Disease Incidence
4.4 Effect of Genetic Diversity of Mango Cultivars on the Disease Incidence
4.5 Availability of Predicting System
4.6 Availability of Appropriate Control Measures
References
5: Modelling of Epidemic Dynamic
5.1 Multiple Regression Equation
5.2 The Polynomial Model
5.3 Validation of the Model
5.4 Chi-square (χ2) test
5.5 Goodness of Fit
5.6 Non-linear Regression
5.7 Non-linear Regression Equation
5.8 Some Important Non-linear Growth Models
5.9 Logistic Model
5.9.1 Gompertz model
5.9.2 Weibull Model
5.10 Monomolecular Model
5.11 Monomolecular Model
5.12 Exponential Model
5.13 Infection Rate
5.13.1 Before `Take Off´ of the Disease
5.13.2 During `Take Off´ Stage of the Epidemic
5.13.3 Before `Take Off´ of the Epidemic
5.13.4 During `Take Off´ of the Epidemic
5.14 Disease Growth Rate (DGR)
5.15 Area Under Disease Progress Curve (DPC)
5.16 Predicted Success of a Single Spore Inoculum of Causing Infection
5.17 Doublet Analysis (for Determining `d´ Value) (van der Plank 1960)
5.18 Optimum Value
References
6: Decision Support Systems (DSSs)
6.1 Reasons for the Low Rate of Implementation of Decision-Support Systems for Plant Protection
6.2 Effects of the Nature of the Cropping System on Managers´ Decisions
6.2.1 Decision-Support Systems for Extensive Crops
6.2.2 Decision Support Systems for Intensive Crops
6.3 Case Study
6.3.1 DSS Based on Several Variables
6.3.1.1 Methodology for Construction of Decision Models
6.3.2 DSS Based on the Most Influential Variable
6.4 Model Data Fitting
References
7: Expert System
7.1 Expert System Frame Work
7.1.1 Knowledge Acquisition
7.1.2 Knowledge Structuring
7.1.3 Knowledge Representation
7.1.4 Inference Engine
7.1.5 User Interface
7.2 Methodology
7.2.1 Diversity of Methodologies Used in Previous Works
7.3 Case Study
7.4 Disease Identification
7.5 Correlation Between Disease and Environmental Variables
7.6 Knowledge Verification and Validation
7.7 KMS (Knowledge Management System) for Crop Diseases Management: A User Interface Design
7.7.1 Software Requirements
7.7.2 System Functionality and Structure
7.8 Expert System for Management of Malformation Disease of Mango
7.9 Architecture
7.9.1 Knowledge Acquisition
7.9.2 Knowledge Structuring
7.9.3 Knowledge Representation
7.9.4 Software Requirements
7.9.5 Knowledge Verification and Validation
7.9.6 Inference Engine
7.9.7 System Functionality and Structure
References
8: Geographic Information Systems: Web-Based Disease Forecasting
8.1 A Web-Based Interactive System for Risk Management of Potato Late Blight in Michigan
8.2 Overall System Design
8.2.1 Linux Operational System
8.2.1.1 PostgreSQL Relational Database Management System
8.2.1.2 Apache Webserver, Using the Hypertext Pre-Processor (PHP) and Perl Programming Languages
8.2.1.3 The Google Maps Application Programming Interface (API; http://www.google.com/apis/maps/documentation/)
8.3 Operational System
8.4 Limitations
8.5 Merit
References
9: Decision Support Systems and Expert Systems: A Comparison
9.1 Definition of DSS
9.2 Definition of ES
9.3 Characteristics of DSS (Sprague and Carlson 1982)
9.4 Characteristics of ES (Fisher 1984)
9.5 Structure of DSS (Sprague and Carlson 1982)
9.6 Structure of ES (Ford 1985)
9.7 Objectives and Intents of DSS and ES
9.8 Programming Language Used to Construct DSS and ES (Ford 1985)
References
10: Forecasting in Changed Climate
10.1 Climate Change
10.2 Causes of Climate Change and Its Effects on Disease Incidence
10.3 Global Warming and Its Impact on Pest Risk
10.4 Disease Management Strategy in Changed Climate
10.5 Forecasting Model Tailored for Climate Change
10.5.1 Multi-Model Ensembles
10.6 Integrated Modeling Approach
10.7 The Site-Specific Model (CLR)
10.8 The Spatial Model (hhh4)
10.9 Linked Process-Based Models
10.10 Our Observations (Mittal and Chakrabarti, Unpublished)
References
11: Disease Detection: Imaging Technology and Remote Sensing
11.1 Imaging Techniques and Spectroscopic for Disease Detection
11.2 Monitoring Weather
11.3 Microprocessor-Based Data Recording System
11.4 On Farm Weather Station
11.5 Components of Weather Stations
11.6 Automatic Weather Station
11.7 The Data-Logger
11.8 Mast
11.9 Power Supply
11.10 Remote Sensing
11.11 Remote Sensing in India
11.12 Disease and Pest Management in Potato
11.12.1 Tea Pests
References
12: Classical Disease Forecasting Systems
12.1 Examples of Few Well Known Forecasting Systems
12.1.1 Potato Late Blight
12.1.2 Apple Scab
12.2 Some Other Important Forecasting Systems
12.2.1 Grape Downy Mildew
12.2.2 Wheat Stripe Rust
12.2.3 Blossom Blight of Apples and Pears
12.2.4 Coffee Rust
12.3 Indian Scenario
12.3.1 Rice Diseases
12.3.2 Oilseeds
12.3.3 Pulse Crop
12.3.4 Mango
12.3.5 Alien Expert Systems Adopted in India
12.4 Conclusion
References

Citation preview

Dilip Kumar Chakrabarti Prabhat Mittal

Plant Disease Forecasting Systems Procedure, Application and Prospect

Plant Disease Forecasting Systems

Dilip Kumar Chakrabarti • Prabhat Mittal

Plant Disease Forecasting Systems Procedure, Application and Prospect

Dilip Kumar Chakrabarti N. D. University of Agriculture & Technology Faizabad, Uttar Pradesh, India

Prabhat Mittal Department of Commerce & Management Stayawati College, University of Delhi Delhi, India

ISBN 978-981-99-1210-0 ISBN 978-981-99-1209-4 https://doi.org/10.1007/978-981-99-1210-0

(eBook)

# The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Plant diseases are considered as a great threat as it can cause significant reduction in both quality and quantity of agricultural products. The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and the Department of Science and Technology (DST) of the Central Government launched a plant protection project to carry out research on diseases and insect pests that cause huge crop loss. The study recorded that diseases and insect pests cause a crop loss of over USD 8.48 billion annually, and this loss is likely to grow at least fourfold under the climate change scenario (The Times of India 29. 2. 2012). The colossal losses in agriculture produces raise the accusing finger towards the abysmal state of plant protection system of the country. It may be stated that pesticide consumption in India is limited to about 25% of the arable land, which is the lowest in the world (Credit Analysis Research Limited, September, 2011, p. 84). However, the traditional way of application of agrochemical fungicides on fixed calendar dates to prevent the disease has some attendant side effects. The fungicides pollute the environment and reduce the quality of the produces. Moreover, as the intensity of the disease between seasons suffers major variations, the use of agrochemicals on fixed calendar dates is not justified. In recent years, an increasing consciousness about environmental pollution due to pesticides and development of fungicide-resistant strains in plant pathogens have challenged plant pathologists to search for eco-friendly tools for disease management. Due to the aforementioned problems, it is necessary to have adequate economic and environmentally acceptable strategies to manage the epidemic development of plant diseases in order to decrease the crop losses and minimize the use of chemical pesticides. Early and accurate diagnoses and pathogen surveillance on local, regional and global scales, vis-à-vis prediction of outbreaks of the disease in epidemic form, may allow time for development and application of mitigation strategies. A forecast of the need for, and the correct timing of, any preventative treatment should, therefore, result in reduced pesticide usage. Thus, plant disease forecasting, also known as forewarning system, is a management system that predicts the occurrence of disease and increase or decrease in disease severity. The prediction of disease outbreaks allows sprays to be applied at times when they are most effective. It may allow the number of sprays to be decreased, reducing the cost of crop production and the environmental impact of pesticides. In commercial agriculture, prediction of disease is also important for predicting crop yields. This v

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Preface

knowledge is used to predict the long-term prices of a commodity in the marketplace. Hence, the forecasting systems guide the growers to make economic decisions about disease treatments for control. Then, in order to obtain sustainable practices for strategic and tactical management of diseases and also to decrease its environmental impact, it is necessary to understand the determining factors of epidemics. The interaction of susceptible host plant, virulent pathogen and favourable environmental conditions leads to the development of the disease. The amount of each of the three components of disease and their interaction in the development of the disease are affected by a fourth component, time. Besides, humans affect disease development in various ways. They affect the type of plants grown in an area, their level of resistance, time of planting, density of planting, etc. However, usually, the forecasting systems accurately predict when the three factors—host, environment and pathogen—(also known as the disease triangle) interact in such a fashion that the disease can occur and cause economic losses. The successful development of plant disease forecasting systems also requires proper development of models and their validation. Forecasting models are often based on relationship like various types of regression analysis. Other relations can be modelled using population growth curves. Polycyclic epidemics are usually modelled by using logistic model, whereas monocyclic epidemics can be best modelled by monomolecular models. Computers are now valuable tools for simulation and modelling of plant disease forecasting. There is a special class of computer program, called expert system, that emulates decision-making logic which human experts use to solve problems in their respective fields. Web-based expert systems provide a comprehensive model delivery system through the internet. The use of near-real-time and forecast weather data is key to true forecast disease outbreak at local and regional bases. This is designed to access and retrieve weather data from an automatic weather station and from a remote database with 7-day weather forecast for the same locality. The model is initiated through a web interface, and the simulation starts by selecting heading date. The awareness, popularity and interest in modelling; derivation of equations for plant disease forecasting; or construction and use of web-based expert systems among plant pathologists in India, unlike the developed countries, are not very encouraging. Apparently, less understanding in mathematics and computer programming of agricultural scientists and lack of appropriate instrumental facilities in most of the state agriculture universities (SAUs) and Krishi Vigyan Kendras (extension centre) are the main impediments. Besides, the construction of the forecasting system is a rigorous interdisciplinary field, employing sophisticated techniques and requiring a good knowledge of its core areas. But it is obvious that without application of the disease forewarning technology, full advantage of integrated pest management (IPM) strategy cannot be achieved. The book aims to review the current scenario of the use of forecasting system in precision agriculture in India and the potentiality to construct new ones to suit the

Preface

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country’s need. It has also attempted to provide easy guidelines to the scientists who wish to step into the plant disease forecasting research. Faizabad, Uttar Pradesh, India Delhi, India

Dilip Kumar Chakrabarti Prabhat Mittal

Acknowledgements

When I attempted to develop a forecasting system for malformation disease of mango, due to my less understanding in mathematics and lack of skill in advanced computer application, I faced an uphill task until Sunil Kumar, Head (ex) of the Department of Agricultural Statistics and founder incharge of the Agricultural Research Information System cell (ARIS, ICAR) in N. D. University of Agriculture & Technology, Uttar Pradesh, and Dr. Pinaki Chakraborty, Assistant Professor, Department of Computer Sciences of Netaji Subhas University of Technology, New Delhi, extended their unstinted cooperation. Later, we jointly published many research papers, and with the help of Dr. Pinaki, it became possible to develop an expert system that can predict the outbreak of mango malformation 6 months prior to the disease appearance. The field data used in this book have been taken from the Ph. D. thesis of Dr. Mukesh Pandey, Scientist (Plant Pathology), Sher-e-Kashmir University of Agricultural Sciences and Technology, Jammu, and also from the M.Sc. (Ag) thesis of Rajesh Kumar, Plant Protection Officer, Govt. of Uttar Pradesh. I express my gratitude to Professor (Math.) Prabhat Mittal who is also a co-author. Without his active and enthusiastic involvement, this project could not be realized. Dr. Rina Chakrabarti, senior Professor (Fishery), and HOD, Zoology of Delhi University, Delhi, is the soul of this endeavour. I express my indebtedness to all of them. In 2011, while I approached the famous publishing house like Springer for publication of my first monograph on mango malformation, I was indeed very nervous. In that critical juncture, Madam Zuzana Bernhart, the then Senior Publishing Editor, instilled confidence and provided guidance to a novice author. And thus, my dream was realized, which initially seemed to be impossible. A decade has past, and now she is Executive Editor, but her zeal to stand up for struggling authors/ scientists has not been eroded. This time also, her helping hand took me to my esteemed readers. I am under her deep obligation. I hope that in future those who step in the epidemiological research will not face hurdles like me and this book will come to their aid and play the same role what Sunil Kumar, Dr. Pinaki and Prof. Prabhat did for me.

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Contents

1

Historic Plant Disease Epidemics . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Irish Famine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Bengal Famine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 African Famine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 2 3 4

2

Epidemic Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 9

3

Predicting Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Initial Inoculum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Weather . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Shrun’s Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11 11 12 14 15

4

Criteria to Develop Forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Capable of Inflicting Significant Damage . . . . . . . . . . . . . . . . 4.2 Sporadic in Nature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Seasonal Variation of the Disease Incidence . . . . . . . . . . . . . . 4.4 Effect of Genetic Diversity of Mango Cultivars on the Disease Incidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Availability of Predicting System . . . . . . . . . . . . . . . . . . . . . . 4.6 Availability of Appropriate Control Measures . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17 17 18 18

Modelling of Epidemic Dynamic . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Multiple Regression Equation . . . . . . . . . . . . . . . . . . . . . . . . 5.2 The Polynomial Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Validation of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Chi-square (χ2) test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Goodness of Fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Non-linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Non-linear Regression Equation . . . . . . . . . . . . . . . . . . . . . . . 5.8 Some Important Non-linear Growth Models . . . . . . . . . . . . . . 5.9 Logistic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23 27 33 34 36 37 37 37 38 38

5

19 20 20 20

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5.9.1 Gompertz model . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.9.2 Weibull Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.10 Monomolecular Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.11 Monomolecular Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.12 Exponential Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.13 Infection Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.13.1 Before ‘Take Off’ of the Disease . . . . . . . . . . . . . . . 5.13.2 During ‘Take Off’ Stage of the Epidemic . . . . . . . . . 5.13.3 Before ‘Take Off’ of the Epidemic . . . . . . . . . . . . . 5.13.4 During ‘Take Off’ of the Epidemic . . . . . . . . . . . . . 5.14 Disease Growth Rate (DGR) . . . . . . . . . . . . . . . . . . . . . . . . . 5.15 Area Under Disease Progress Curve (DPC) . . . . . . . . . . . . . . . 5.16 Predicted Success of a Single Spore Inoculum of Causing Infection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.17 Doublet Analysis (for Determining ‘d’ Value) (van der Plank 1960) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.18 Optimum Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

7

Decision Support Systems (DSSs) . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Reasons for the Low Rate of Implementation of DecisionSupport Systems for Plant Protection . . . . . . . . . . . . . . . . . . . 6.2 Effects of the Nature of the Cropping System on Managers’ Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Decision-Support Systems for Extensive Crops . . . . . 6.2.2 Decision Support Systems for Intensive Crops . . . . . 6.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 DSS Based on Several Variables . . . . . . . . . . . . . . . 6.3.2 DSS Based on the Most Influential Variable . . . . . . . 6.4 Model Data Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Expert System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Expert System Frame Work . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Knowledge Acquisition . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Knowledge Structuring . . . . . . . . . . . . . . . . . . . . . . 7.1.3 Knowledge Representation . . . . . . . . . . . . . . . . . . . 7.1.4 Inference Engine . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.5 User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Diversity of Methodologies Used in Previous Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Disease Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Correlation Between Disease and Environmental Variables . . .

39 40 43 43 44 46 47 47 48 48 49 50 51 53 54 56 59 60 61 61 61 62 62 63 64 64 67 69 69 69 70 71 71 72 72 74 74 75

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7.6 7.7

75

Knowledge Verification and Validation . . . . . . . . . . . . . . . . . KMS (Knowledge Management System) for Crop Diseases Management: A User Interface Design . . . . . . . . . . . . . . . . . . 7.7.1 Software Requirements . . . . . . . . . . . . . . . . . . . . . . 7.7.2 System Functionality and Structure . . . . . . . . . . . . . 7.8 Expert System for Management of Malformation Disease of Mango . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.9 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.9.1 Knowledge Acquisition . . . . . . . . . . . . . . . . . . . . . . 7.9.2 Knowledge Structuring . . . . . . . . . . . . . . . . . . . . . . 7.9.3 Knowledge Representation . . . . . . . . . . . . . . . . . . . 7.9.4 Software Requirements . . . . . . . . . . . . . . . . . . . . . . 7.9.5 Knowledge Verification and Validation . . . . . . . . . . 7.9.6 Inference Engine . . . . . . . . . . . . . . . . . . . . . . . . . . 7.9.7 System Functionality and Structure . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

9

10

Geographic Information Systems: Web-Based Disease Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 A Web-Based Interactive System for Risk Management of Potato Late Blight in Michigan . . . . . . . . . . . . . . . . . . . . . 8.2 Overall System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Linux Operational System . . . . . . . . . . . . . . . . . . . . 8.3 Operational System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Merit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

76 76 76 76 77 78 79 79 80 80 80 80 81 83 84 86 86 86 87 88 88

Decision Support Systems and Expert Systems: A Comparison . . . . 9.1 Definition of DSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Definition of ES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Characteristics of DSS (Sprague and Carlson 1982) . . . . . . . . . 9.4 Characteristics of ES (Fisher 1984) . . . . . . . . . . . . . . . . . . . . . 9.5 Structure of DSS (Sprague and Carlson 1982) . . . . . . . . . . . . . 9.6 Structure of ES (Ford 1985) . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7 Objectives and Intents of DSS and ES . . . . . . . . . . . . . . . . . . 9.8 Programming Language Used to Construct DSS and ES (Ford 1985) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

89 89 89 90 90 90 90 91

Forecasting in Changed Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Causes of Climate Change and Its Effects on Disease Incidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Global Warming and Its Impact on Pest Risk . . . . . . . . . . . . . 10.4 Disease Management Strategy in Changed Climate . . . . . . . . .

93 93

91 92

93 95 97

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10.5

Forecasting Model Tailored for Climate Change . . . . . . . . . . . 10.5.1 Multi-Model Ensembles . . . . . . . . . . . . . . . . . . . . . 10.6 Integrated Modeling Approach . . . . . . . . . . . . . . . . . . . . . . . . 10.7 The Site-Specific Model (CLR) . . . . . . . . . . . . . . . . . . . . . . . 10.8 The Spatial Model (hhh4) . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.9 Linked Process-Based Models . . . . . . . . . . . . . . . . . . . . . . . . 10.10 Our Observations (Mittal and Chakrabarti, Unpublished) . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97 97 100 101 101 101 102 103

Disease Detection: Imaging Technology and Remote Sensing . . . . . 11.1 Imaging Techniques and Spectroscopic for Disease Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Monitoring Weather . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Microprocessor-Based Data Recording System . . . . . . . . . . . . 11.4 On Farm Weather Station . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5 Components of Weather Stations . . . . . . . . . . . . . . . . . . . . . . 11.6 Automatic Weather Station . . . . . . . . . . . . . . . . . . . . . . . . . . 11.7 The Data-Logger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.8 Mast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.9 Power Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.10 Remote Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.11 Remote Sensing in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.12 Disease and Pest Management in Potato . . . . . . . . . . . . . . . . . 11.12.1 Tea Pests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

105 105 107 108 108 109 109 109 110 110 110 112 112 113 113

Classical Disease Forecasting Systems . . . . . . . . . . . . . . . . . . . . . . . 12.1 Examples of Few Well Known Forecasting Systems . . . . . . . . 12.1.1 Potato Late Blight . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.2 Apple Scab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Some Other Important Forecasting Systems . . . . . . . . . . . . . . 12.2.1 Grape Downy Mildew . . . . . . . . . . . . . . . . . . . . . . 12.2.2 Wheat Stripe Rust . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.3 Blossom Blight of Apples and Pears . . . . . . . . . . . . 12.2.4 Coffee Rust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3 Indian Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3.1 Rice Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3.2 Oilseeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3.3 Pulse Crop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3.4 Mango . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3.5 Alien Expert Systems Adopted in India . . . . . . . . . . 12.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

117 117 117 118 118 118 119 120 120 121 121 122 122 123 123 124 124

About the Authors

D. K. Chakrabarti graduated (Plant Pathology) from West Bengal. He was a Prof. at ND Agri. Univ., Uttar Pradesh. Currently, he is a Visiting Prof. at B. C. Agri. Univ., West Bengal. He received nine national/professional society awards including Indian National Sci. Acad. Young Sci. Medal (1980), Kothari Research Award (1980), Indian Sci. Cong. Young Sci. Award (1982), and U.P. (Govt.) Best Agri Sci. Award (2001). Prabhat Mittal is a Professor in the Department of Commerce and Management, Satyawati College, Delhi University. He did his Ph.D. from the Faculty of Management Studies (FMS), University of Delhi, and has published many research articles in the fields of supply chain management, quantitative finance, and big data analytics.

xv

Abbreviations

AI AIC ASOS AUDPC AWS CCD CLR COMAX DEX DON DSSs DSV ES ESMMDM ESTA FHB GCM GIS GPS hhh4 I/O ICT IPM IRS KMS LBRM MAWN MCDM MS NAPDFC NIR NMR O-A-V

Artificial intelligence Akaike information criterion Surface weather observing network Area under disease progress curve Automatic weather station Charge-coupled device Site-specific model Crop management expert system Decision expert Deoxynivalenol Decision support systems Disease diversity value Expert system Expert system for management of malformation of disease of mango Expert system for text animation Fusarium head blight Global climate model Geographic information system Global positioning system The spatial model Input-output Information and communication technologies Integrated pest management Indian remote sensing satellite Knowledge management system Late blight risk management Michigan Automated Weather Network Multi-criteria decision models Mass spectroscope North American Plant Disease Forecast Center Near-infrared range Nuclear magnetic resonance Object-attribute-value xvii

xviii

PHP PLASMO PLB SVIs TMV UC VIS VOC WDCA

Abbreviations

Hypertext pre-processor Plasmopara simulation model Potato late blight Spectral vegetation indices Tobacco mosaic virus University Carolina Visible range Volatile organic compound Wheat disease control advisory

1

Historic Plant Disease Epidemics

The outbreak of diseases on various crops in an epiphytotic form has been reported occasionally from different parts of the world. These are of course of great concern but cannot be termed as a disaster. Earlier when plant protection measures were either lacking or diffuse, two plant disease epidemics appeared in rapacious form and ravaged the social and economic fabrics of the mankind so severely that the wounds inflicted by these epidemics have not been yet healed up. The two epidemics referred here are (i) Irish potato famine triggered by late blight disease in 1845 in which one and half million people starved and a similar number emigrated during the famine and (ii) the epidemic of brown spot disease of rice in 1942 resulting into a calamitous famine in undivided Bengal in 1943 when altogether about three million people died.

1.1

Irish Famine

In the uplands of the Andes in the Lake Titicaca situated between Peru and Bolivia, the potato crop (Solanum tuberosum) originated. And its pathogen, the fungus, Phytophthora infestans, which causes potato late blight seems to be originated in nearby Mexico where the occurrence of both mating types of the fungus is very common and abundant. Then the fungus moved towards north-eastern region of the USA and caused their late blight epidemic of potato in 1843. From North America, the pathogen travelled to England with cargo on a ship. Winds from southern England further carried the fungus to the countryside around Dublin of Ireland. The fungal spores settled on leaves of healthy potato plants, multiplied and carried by cool breeze to surrounding plants. Incidentally at that time, Ireland relied primarily upon two high-yielding potato varieties. Thus, a single infected plant under ideal moist conditions could infect thousands in just few days in vast stretch of monoculture of susceptible potato crop and break out an epidemic. Failures of potato crops were reported for a number of years during the cool and rainy 40 s. Between 1800 and 1845, there were 16 incidents of food shortages in various parts of # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 D. K. Chakrabarti, P. Mittal, Plant Disease Forecasting Systems, https://doi.org/10.1007/978-981-99-1210-0_1

1

2

1

Historic Plant Disease Epidemics

Ireland. But these were mostly regional and short-lived. In 1845, a dense fog wafted across the field of entire Ireland, and the fungus getting an ideal cool and moist condition spread like wildfire. Soon the potato leaves turned black, curled and rotted and there was crop failure at high magnitude nationwide for the first time. Everyone was hoping that the worst would be over in the next crop season and to harvest a good blight-free crop. But unfortunately, the blight devastated potato crops for the next 5 consecutive years (1845–1850) in Ireland. In the 1840s, almost half of the population in Ireland depended on potatoes to survive. Thus, the consecutive crop failures for 5 years brought about concomitant catastrophic consequences (Anon 2008). In the first year of famine, deaths from starvation were kept down due to the imports of Indian corn and the survival of about half the original potato crop. But later apathy and callous attitude of the British government officials and administrators failed to bring relief to the million starving. A loss of 1 million lives due to starvation and diseases. A loss of 1.5 million due to emigration resulted in a large Irish diaspora in many parts of North America. Ireland’s 1845 population of eight million dropped to 5.5 million by 1860 (Schumann and D’Arcy 2000). Initially, the disease appeared to be a mysterious one and was attributed to many factors that are mostly unscientific and full of superstition. Finally, the research evidence of Anton deBary in 1861 (DeBarry 1876) conclusively proved that the fungus P. infestans with the help of its millions of white spores ravaged the potato crop in Ireland. It is for the first time a microorganism was proven to be the cause of a plant disease and this marked the initiation of a new paradigm of science, i.e. Plant Pathology.

1.2

Bengal Famine

The principal cause of the short supply of rice in 1943 was the epidemic of brown spot disease that attacked the rice crop in Bengal in 1942 (Padmanabhan 1973). The disease caused 40–90% yield reductions in rice. Nothing as devastating as the Bengal rice brown spot epidemic of 1942 has been recorded in the history of plant pathology. The situation was further worsened by administrative failure. At that time, food administration in India was the responsibility of provincial governments. The Bengal provincial government failed to make up the food shortage through imports because World War II had cut off imports of rice from Burma. Britain, Canada and the USA were sending food elsewhere according to wartime priorities. In provinces, like Punjab, rice was not in short supply, but they prohibited rice exports to other regions. There was very little marketable surplus from 1942 harvest, thus, the price of rice in Bengal soared beyond the reach of the ordinary people. The Bengal government made the feeding of the urban Calcutta population a priority and requisitioned rice supplies from rural areas. The populations from the villages migrated to the cities in search of employment and rice. Finding neither they slowly died of starvation. And the heart rendering starvation death of several thousands of men, women and children continued from October to December in all the important cities in Bengal.

1.3 African Famine

3

The nature and extent of the abnormal weather parameters in the epiphytotic year of 1942 were studied to explain the outbreak (Padmanabhan 1973). The environmental factors of 1942 were compared with those of the years of 1941, 1943 and 1944. The salient observations of his (Padmanabhan 1973) studies are enumerated below: 1. In November 1942, the average minimum temperature was higher but on the contrary, the average daily range of temperature was lower. 2. In September 1942, the cloudiness in daytime was greater with a subsequent lesser number of bright sunshine hours in both September and November. 3. In November 1942, there were 2–6 rainy days while in other 2 years, i.e. 1943 and 1944, there were no rains during the same period. However, in 1943, there were rains but that was only for 2–3 days which was lesser than that in 1942. 4. The average relative humidity both in the morning and evening hours during the whole September of the epiphytotic year as compared to other years was higher. 5. Deficiency of nutrients like nitrogen and potassium and upsetting of iron–manganese ratio due to leaching of the nutrients from the soil caused by heavy rainfall in 1942 predisposed the crop to the Helminthosporium – infection. Thus, in November 1942, the weather conditions viz. cloudy days, slight drizzle and high minimum temperature provided exceptionally favourable conditions for abundant continuous spore release and infection. Further, rice shows increased susceptibility to Helminthosporium oryzae with increase in age and thus attains the maximum susceptibility at the time of flowering and maturity. Incidentally throughout Bengal, the crop reached the flowering and maturing stages in November– December (Padmanabhan 1973). Based on the observations on meteorological conditions in the epiphytotic year and simultaneous experimental evidence on the relation of the environmental parameters with different phases of the infection process, a condition for possible outbreak of the epidemic was envisaged (Padmanabhan 1973). Excessive rainfall in September, uniformly favourable temperature of 20–29.4 °C throughout the days continuously for 2 months (October–November), unusually cloudy weather and rains in November, reduced hours of sunshine (85%) (X1), number of rainy days (X2) and total rainfall (X3) (Table 5.3) through multiple regression analysis (Gomez and Gomez 1984) (Table 5.3). The estimating equation The multiple regression equation Y = b0 + b1X1 + b2X2 + b3X3. The data represented in Table 3 in matrix notation: 2

C 11 61 6 6 14 7 6 6 7 61 6 7 6 6 27 7 6 7 1 Y =6 6 27 7; X = 6 6 6 7 6 6 7 61 4 27 5 6 41 27 1 2

3

X1 4

X2 10

5

5

7 5

11 3

13 4

16 1

3 X3 2 3 e1 7 73:2 7 2 3 7 6 7 b0 6 e2 7 105:2 7 7 6 6b 7 7 6 e3 7 6 17 7 7 132:7 7; b = 6 7e = 6 6e 7 4 b2 5 7 6 47 7 25:2 7 6 7 4 e5 5 b3 7 236:8 5 6 8:4

28

5 Modelling of Epidemic Dynamic

Table 5.3 Number of malformed shoots on naturally malformed plants cv. Amrapali under different environmental parameters

Parameters Flushing period

Post flushing period

15.6.2000– 0.06.2000 1.7.2000– 15.7.2000 16.7.2000– 31.7.2000 1.8.2000– 14.8.2000 15.8.2000– 15.9.2000 16.9.2000– 15.10.2000

Number of days with optimum temp. & RH (X1) 4

Number of rainy days (X2) 10

Total rains (mm) X3 73.2

Number of malformed shoots (Y ) 11

5

5

105.2

14

7

11

132.7

27

5

3

25.2

27

13

16

236.8

27

4

1

8.4

27

The normal equation to estimate the regression coefficient in matrix form can be expressed as:  -1 T b = XT X X Y 2 3 b0

6 b1 7 7 b¼6 4 b2 5 b3

8 > > > 2 > > 1 1 > > >

4 10 5 > > > > > 73:2 105:2 > > :

2 3 1 1 1 1 7 5 13 4 7 7 7 11 3 16 1 5 132:7 25:2 236:8 8:4

1 61 6 6 61 6 61 6 6 41

4 5 7 5 13

1

4

1

2 2

1

6 4 6 6 4 10 73:2

1

1

1

1

5 5

7 11

5 3

13 16

105:2 132:7

25:2 236:8

39 73:2 > - 1 > > > 105:2 7 > 7> > 7> 132:7 7 = 7 > 25:2 7 7> > 7> > 236:8 5 > > > ; 8:4

10 5 11 3 16

11

3

36 7 6 14 7 6 7 7 4 7 6 27 7 76 7 7 1 56 6 27 7 6 7 8:4 4 27 5 27 1

5.1 Multiple Regression Equation

29

2

3 b0 6 7 6 b1 7 6 7 b¼6 7 6 b2 7 4 5 b3 1:3736

¼

- 0:2922

- 0:0925 0:0140

1

1

1

1

1

1

11

- 0:2922 0:1026

0:0111

- 0:0046 4

5

7

5

13

4

14

- 0:0925 0:0111

0:0377

- 0:0028 10

5

11

3

16

1

27

132:7

25:2 236:8

0:0140

- 0:0046

- 0:0028 0:0004

73:2 105:2

8:4 27 27 27

2

b0

3

2

10:234

3

6 b 7 6 4:336 7 6 17 6 7 b=6 7=6 7 4 b2 5 4 - 0:243 5 b3

- 0:141

The equation obtained was Y = 10.234 + 4.336X1 - 0.243X2 - 0.141X3. A unit change in optimum temperature and moisture (X1) influences the disease incidence up to an extent of 4.336 unit (b1) in positive direction. But a unit of change in rainy days (X2) and total rainfall (X3) influenced the disease incidence by 0.234 (b2) and 0. 141 (b3) unit in the opposite direction. The intercept (b0 = 10.234) denotes the expected value of y when x = 0. Total sum of squares (TSS) = Explained sum of squares (ESS) + Residual sum of squares (RSS) X

ð y - yÞ 2 =

X

ðby - byÞ2 þ

X

ðy - byÞ2

The squared multiple correlation coefficient (R2) calculates the variance in Y explained by the variations in the independent variables X1, X2, and X3 and is expressed as: R2 =

P ðby - byÞ2 284:83 ESS ðExplained sum of squaresÞ =P = = 0:699 TSS ðTotal sum of squaresÞ ðy - yÞ2 199:38

The value of R2 between number of malformed shootlets and weather variables (Table 5.4) indicates that 69.9% changes in disease incidence were caused by the weather parameters.

30

5 Modelling of Epidemic Dynamic

Table 5.4 Residual output (The value of R2 between number of malformed shootlets and weather variables) S. No. 1 2 3 4 5 6 Total

y

y

by

11 14 27 27 27 27 133

22.17 22.17 22.17 22.17 22.17 22.17 133.00

14.83 15.86 19.20 27.63 29.32 26.15 133.00

ðy - byÞ2 14.64 3.48 60.83 0.40 5.39 0.72 85.46

ðby - byÞ2 53.93 39.76 8.82 29.85 51.15 15.86 199.38

ðy - yÞ2 124.77 66.75 23.33 23.33 23.33 23.33 284.83

The multiple pffiffiffiffiffiffiffiffiffiffifficorrelation coefficient is the positive square root of r-square and is equal to 0:699 = 0:836. Similar multiple correlation coefficient with three variables can be performed to obtain the following: r X 1 X 2 X 3 = 0:960; r YX 2 X 3 = 0:237; r YX 1 X 3 = 0:833; r YX 1 X 2 = 0:715 Partial correlation coefficient: Partial correlation analysis is aimed at finding the correlation between two variables after removing the effects of other variables. A partial correlation measures the strength of the linear relationship between two variables, while adjusting for the effect of other variables. This type of analysis helps to spot spurious correlations, i.e. correlations explained by the effect of other variables as well as to reveal hidden correlations, i.e. correlations masked by the effect of other variables. Partialing represents a method of exerting statistical control over variables. It is important to distinguish statistical control from experimental control (e.g. random assignment to treatments, control by constancy, etc.). Generally, experimental control provides stronger evidence than statistical control because it is directly managed by the researcher and planned a priori. Partial correlation coefficient can be obtained using the following formula: RAB - RAC :RBC RAB:C = qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   ffi 1 - R2AC 1 - R2BC RYX 1 - RY:X 2 X 3 :RX 1 :X 2 X 3 RYX 1 :X 2 X 3 = rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   ffi 2 2 1 - RY:X 2 X 3 1 - RX 1 :X 2 X 3 0:420 - 0:237  - 0:686 = pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi = 0:826 ð1 - 0:237  0:237Þð1 - 0:686  0:686Þ

5.1 Multiple Regression Equation

31

RYX 2 - RY:X 1 X 3 :RX 2 :X 1 X 3 RYX 2 :X 1 X 3 = rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi    1 - R2Y:X 1 X 3 1 - R2X 2: X 1 X 3 - 0:125 - 0:833  0:073 = pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi = - 0:134 ð1 - 0:833  0:833Þð1 - 0:073  0:073Þ RYX 3 - RY:X 1 X 2 :RX 3 :X 1 X 2 RYX 3 :X 1 X 2 = rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   ffi 2 2 1 - RY:X 1 X 2 1 - RX 3 :X 1 X 2 0:086 - 0:715  0:604 = pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi = - 0:620 ð1 - 0:715  0:715Þð1 - 0:604  0:604Þ The data presented in Table 5.3 were computed for the partial correlation analysis. The data show that controlling the other variables the partial correlation coefficients between the malformed shoots with optimum temperature and RH, number of rainy days and total rainfall were 0.8256, -0.134 and -0.720, respectively. Thus, the two variables viz. number of days with optimum temperature and RH, total rains have significant correlations with the malformed shoots and may be used as predictors. Stepwise regression: A common problem in regression analysis is that of variable selection. Often, there are a large number of potential independent variables, and one has to select among them to create a ‘best’ model. One common method of dealing with this problem is some form of automated procedure, such as stepwise selection. Stepwise regression is a semi-automated process of building a model by successively adding or removing variables based solely on the t-statistics of their estimated coefficients. The program automatically enters the variable with the highest F-to-enter statistic or removes the variable with the lowest F-to-remove statistic. The stepwise approach is much faster. Stepwise methods are frequently employed to evaluate the order of importance of variables. The incidence of malformation disease of mango caused by Fusarium moniliforme var. subglutinans (=F. mangifera) is promoted by its mite vector, Tyrolichus casei while its development is adversely affected by the host defense chemical compound mangiferin. Chakrabarti et al. (1997) recorded the population of F. mangiferae, T. casei and mangiferin content in young buds and subsequently developed malformed panicles in mango cv Banarasi Langra (Table 5.5). Then correlation and regression coefficient matrix for the variables was obtained using M STAT-C software package. The data presented in Table 5.6 show the way the variables interact with each other and identify the variable(s) that influenced the development of malformed panicles significantly. The data presented in Table 5.5 show that the population of F. mangiferae and T. casei were negatively correlated with mangiferin content and the incidence of malformed panicles. But these two variables were positively correlated with each other. However, mangiferin content and the incidence of malformed panicles were positively correlated. But mangiferin

32

5 Modelling of Epidemic Dynamic

Table 5.5 The population of F. mangiferae, T. casei and mangiferin content in young buds and subsequently developed malformed panicles in mango cv Banarasi Langra Sl no. of test plants 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Mangiferin content (g 5 g-1) 0.061 0.067 0.079 0.119 0.135 0.139 0.170 0.170 0.178 0.329

Malformed panicles (%) 0.08 0.22 0.55 0.61 0.63 3.56 5.02 7.66 14.96 0.40

No. of colony of F. mangiferae (g-1 buds) 97 434 379 319 304 49.93 48.22 47.00 38.00 10.66

No. of T. casei (g-1 buds) 2.00 2.33 2.66 2.33 1.66 1.60 1.33 1.33 1.20 1.00

Table 5.6 Correlation matrix Parameters Malformed panicles Fusarium mangiferae Tyrolichus casei Mangiferin Critical value (Two-tail, 0.05 = ±0.7613 Cases correlated 2–9

Malformed panicles 1.000 -0.7598 0.7939 0.7613

No of F. mangiferae

No of T. casei

Mangiferin

1.0000 0.8253 0.9141

1.0000 08583

1.0000

showed positive correlation with F. mangiferae while it adversely affected the population of T. casei. Multivariate regression: It is the most useful for more special problems such as compound tests of coefficients. For example, Pandey et al. (2003) tested to know if the age of mango plants has the same predictive power for incidence of floral malformation in a mango orchard in Lucknow as it does in the orchard situated nearby mango belt in Faizabad. One option would be to run two separate simple regressions and eyeball the results to see if the coefficients look similar. But if somebody wants a formal probability test of whether the relationship differs, one could run it instead as a multivariate regression analysis. The coefficient estimates will be the same, but it will enable us to directly test for their equality or other properties of interest. Analysis can be used if the relation between Y and X is not so linear. However, multiple linear regression differs from multivariate regression. A multiple regression typically refers to regression models with a single-dependent variable and two or more predictor variables. In multivariate regression, by contrast, there are multiple dependent variables and any number of predictors.

5.2 The Polynomial Model

5.2

33

The Polynomial Model

A polynomial model is in fact the extension of regression model that shows the curved relationship between Y and X. The second-degree polynomial is known as quadratic or curvilinear regression (U-shaped curve called a parabola). In such model, the regression line has one bend. The third-degree polynomial which is known as the cubic model possesses two turning (critical) points and three real roots (where the curve crosses the horizontal axis). With the addition of more bends the polynomial is known as fourth-degree (Quartic) polynomial and so on. They fit a wide range of curvature. They show the relation of a single independent variable with Y. Polynomial equations are of the following types: Y = a + b1X1 Linear Y = a + b1X1 + b2X12 Quadratic Y = a + b1X1 + b2X12 + b3X13 Cubic Linear = straight line, no curve or inflections Quadratic = one parabolic curve, no inflections Cubic = two parabolic rates of curvature with the possibility of an inflection point. Inflections (the inflection point is where the acceleration of the regression line switches from positive to negative) (Fig. 5.2) for Polynomial Regression lines. Each additional term allows for another change in the rate of curvature and allows for an additional inflection. The slopes of the regression line become less steep as X increases, but line does not actually begin to descend.

Fig. 5.2 Inflection point

34

5 Modelling of Epidemic Dynamic

Table 5.7 Effect of host age on the incidence of floral malformation in ‘Dashehari’ mango in Faizabad in 2003

Age group (year) 0–5 6–10 11–15 16 and above

Malformation (%) 12.69 28.25 23.87 6.49

Malformaon (%) 30 25 20

y = -2.298x + 23.57

15 10 5 0 0-5

6-10 Malformaon (%)

11-15

16 and above

Linear (Malformaon (%))

Fig. 5.3 Linear relation host age (X) and incidence of malformation (Y )

For example, Pandey et al. (2003) observed that the number of malformed panicles (Y) increased with the increase in the age (X) of mango plants. But after a point of time, the number of diseased panicles started declining nevertheless the plant age went on increasing (Table 5.7). Consequently, accelerated growth of the regression line after certain time bends downwards. The data were fitted to a set of linear (Fig. 5.3) and polynomial equation of second (quadratic) (Fig. 5.4; Table 5.8) and third order (cubic) (Fig. 5.5). And polynomial equation of the third order (cubic) (Fig. 5.5; Table 5.9) was the best fitted. Thus, the relationship between host age (X) and incidence of malformation (Y ) forms a cubic polynomial equation (Fig. 5.5).

5.3

Validation of the Model

Once a predictive model has been developed, it has to be thoroughly tested by statistically comparing its predictions with what actually happens. After developing such a model, if an additional value of X is then given without its accompanying value of y, the fitted model can be used to make a prediction of the value of y.

5.3 Validation of the Model

35

Malformaon (%) 30 y = -8.235x2 + 38.877x - 17.605

25 20 15 10 5 0 0-5

6-10 Malformaon (%)

11-15

16 and above

Poly. (Malformaon (%))

Fig. 5.4 Quadratic relation host age (X) and incidence of malformation (Y ) Table 5.8 Polynomial equations of second order (quadratic) between host age (X) and incidence of malformation (Y ) Age group (year) 0–5 6–10 11–15 16 and above

Malformation (%) 12.69 28.25 23.87 6.49

Quadratic = -8.235*12 + 38.87*1-17.60 = 13.04 = -8.235*22 + 38.87*2-17.60 = 27.20 = -8.235*32 + 38.87*3-17.60 = 24.90 = -8.235*42 + 38.87*4-17.60 = 6.12

Malformaon (%) 35 30

y = 1.1567x3 - 16.91x2 + 58.193x - 29.75

25 20 15 10 5 0 0-5

6-10 Malformaon (%)

11-15

16 and above

Poly. (Malformaon (%))

Fig. 5.5 Cubic relation host age (X) and incidence of malformation (Y )

36

5 Modelling of Epidemic Dynamic

Table 5.9 Polynomial equations of third order (cubic) between host age (X) and incidence of malformation (Y ) Age group (year) 0–5 6–10 11–15 16 and above

Malformation (%) 12.69 28.25 23.87 6.49

Cubic =1.156*13-16.91*12 + 58.19*1-29.75 = 12.69 =1.156*23-16.91*22 + 58.19*2-29.75 = 28.24 =1.156*33-16.91*32 + 58.19*3-29.75 = 23.84 =1.156*43-16.91*42 + 58.19*4-29.75 = 6.43

Table 5.10 Validation of the prediction model explains the relationship of host age and the incidence of malformation Host age (years) 5 10 15 20

5.4

Observed value (O) 12.69 28.25 23.87 06.49

Predicted value (E) 13.03 27.20 24.91 06.14

a = (O-E)2/E 0.008 0.041 0.043 0.019 χ2 = 0.1125

Chi-square (x2) test

Chi-square test (Panse and Sukhatme 1967) is used to test the validity of the model. The validity of the model is evaluated statistically based on the predicted (E) and actually observed (O) values. Chi-square (χ2) value is calculated by the formula: a = Σ (O-E)2/E. On comparing the ‘a’ value with the tabular value at the appropriate degree of freedom, it is verified that if the null hypothesis is rejected and there is a wide difference between the predicted and observed values. If the hypothesis is accepted there will be no wide difference between the predicted and observed values, thus the validity of the model is established. For example, Pandey et al. (2003) reported the pattern of changes in incidence of floral malformation disease ( y) in mango with the plant age (X) in Faizabad and developed the following regression equation: Y = 17.605 + 7.775X - 0.329X2, R2 = 0.998, where Y = number of malformed panicles and X = host age. The observed and calculated values for floral malformation progress curves show close resemblance (Table 5.5). The χ2 value (0.112) also supports that the model is adequate to describe the progress of the floral malformation in different age groups of plants (Table 5.10). Chi-square table value at 3° of freedom and 5% level of significance = 7.81. Since the calculated value = 0.1125 which is less than 7.81, the model is valid. Note that the degree of freedom = n-1.

5.7 Non-linear Regression Equation

5.5

37

Goodness of Fit

The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically indicate if there is any discrepancy between observed values and the values expected under the model in question. One statistical test that gives some information about the goodness of fit of a model is R2. R-squared measures between zero and one. An R2 of 1.0 indicates that the regression line perfectly fits the data. Generally, a higher value of R-squared means that the model has the ability of a better prediction. R-Squared is most often used in linear regression. Values of R2 outside the range 0–1 can occur where the modelled values are not obtained by linear regression and depending on which formulation of R2 is used. Another statistical test that addresses this issue is the chi-square (χ2) goodness of fit test.

5.6

Non-linear Regression

The basic idea of nonlinear regression is the same as that of linear regression, namely, to relate a response Y to a vector of predictor variables X. Non-linear regression is characterized by the fact that the prediction equation depends nonlinearly on one or more unknown parameters. If something is growing exponentially, which means growing at a steady rate, the relationship between X and Y is curved. To fit something like this, non-linear regression is needed. Whereas linear regression is often used for building a purely empirical model, non-linear regression usually arises when there are physical reasons for believing that the relationship between the response and the predictors follows a particular functional form.

5.7

Non-linear Regression Equation Y = AebX

X is often a measure of time. X goes up by 1 for each second, hour, day, year, or whatever time unit are used. Sometimes, X is a measure of dosage. X can be positive, 0, or negative. A is the starting amount, the amount expected when X is 0. b is the growth rate, with continuous compounding. b is the estimate of the relative change in Y associated with a unit change in X. Y is the growth For example, if b is 0.05 and X increases by 1, Y is multiplied by e5, which is about 1.051, so Y really increases by 5.1%. Let the starting amount y = 1 at x = 0 implies Y = A = 1 At x = 1, Y = A*e0.05*1 = 1*1.0512 = 1.0512 (e0.05 = 1.0512) so y increased by 5.12% in one unit change of x.

38

5.8

5 Modelling of Epidemic Dynamic

Some Important Non-linear Growth Models

These models describe the growth behaviour over time, i.e. evolution of a quantity over time. Values for the measured property are plotted on a graph as a function of time. In general, growth models are mechanistic in nature. Growth can be modelled using several mathematical functions. Growth models provide a range of curves that are similar to disease progress curves and represent one of the most common mathematical tools to temporal disease epidemiology.

5.9

Logistic Model

It was first proposed by Verhulst in 1838 to represent human population growth. The second type of logistic model was proposed by Van der Plank (1963), being more appropriate for most polycyclic diseases, meaning that there is a secondary spread within a growing season. A logistic function or logistic curve is a common sigmoid curve that describes population growth. In a logistic curve, growth of a population is modelled as ‘S-shaped’ (Fig. 5.6). The initial stage of growth is approximately exponential; then, as saturation begins, the growth slows, and at maturity, growth stops. This growth model is the most widely used for describing epidemics of plant disease. A generalized logistic curve is as follows: lt = 

L  1 þ Ae - kt

where l = length and t = time. L is the upper asymptote (maximum size reach at t → infinity). Fig. 5.6 Logistic (S shaped) curve

5.9 Logistic Model

39

Table 5.11 Sequential development (in wt.) of malformed panicles from the emergence of panicle to fruit setting stage in mango cv. Amrapali Year of observation 20.02.2001 27.02.2001 06.0320 01 13.03.2001 20.03.2001

Wt. of panicles in g 0.799 3.083 13.003 37.123 43.230

Logistic growth curve 42:564 lt = ð1þ7256:38e - 2765t Þ 0.093 1.429 15.135 38.204 42.260

Fig. 5.7 Sequential development (in wt.) of malformed panicles from the emergence of panicle to fruit setting stage in mango cv. Amrapali

k is the growth rate. Pandey et al. (2003) recorded the wt of malformed panicles form the emergence up to the fruit setting (Table 5.11) and a graph was plotted (Fig. 5.7) using MS Excel programme. The graph showed close resemblance with the Logistic model (Fig. 5.6)

5.9.1

Gompertz model

This growth model is appropriate for polycyclic diseases as an alternative to logistic models. It has an absolute rate curve that reaches a maximum more quickly and declines more gradually than the logistic model. Gompertz and logistic models have a characteristic sigmoid form and an inflection point meaning secondary inoculation or plant-to-plant spread within the crop. Gompertz curve:

40

5 Modelling of Epidemic Dynamic

Table 5.12 Temporal progression of length of malformed panicle of mango cv. Amrapali Date of observation t 20.2.2001 27.2.2001 6.3.2001 13.3.2001 20.3.2001 27.3.2001 3.4.2001

Length of panicle (cm) Y 2.63 5.90 9.60 14.36 16.00 16.70 17.00

Gompertz growth curve = 18:053e - e 2.137 6.084 10.371 13.610 15.632 16.776 17.390

1:432 - 0:674t

Fig. 5.8 The growth curve of increase in length of malformed panicles of mango cv. Amrapali at weekly interval

Y = Ae - e

B - Ct

Y = growth, t = time, A = asymptote (t → infinity), C = growth rate and B = intercept of x axis. e = 2.71828. Pandey et al. (2003) recorded an increase in length of malformed panicles of mango cv. Amrapali at the weekly interval (Table 5.12) and plotted a graph using MS Excel programme. The graph (Fig. 5.8) with short lag phase, gradual and long exponential growth phase and with short stationary phase showed a closed resemblance with Gompertz growth curve (Fig. 5.9).

5.9.2

Weibull Model

The Weibull model is suitable if there are a larger number of parameters, and so can describe more complicated curves The Weibull growth model is described by the equation:

5.9 Logistic Model

41

24

Length of Panicle (cm) 19 14 9

Length of panicle (cm)

3.4.2001

27.3.2001

20.3.2001

13.3.2001

6.3.2001

27.2.2001

-1

20.2.2001

4

Gompertz Growth curve .

Gompertz Model:

− .

Fig. 5.9 Gompertz growth curve Table 5.13 Sequential development in length (cm) of malformed panicles in mango cv. Amrapali Date of observation t 20.2.2001 27.2.2001 6.3.2001 13.3.2001 20.3.2001 27.3.2001 3.4.2001

Length of panicle (cm) Y 2.63 2.80 4.50 14.36 16.0 16.7 17.0

Weibull growth 8:942 curve = 16:567 - ð16:567 þ 2:687Þ  e - ð0:268tÞ 2.687 2.739 4.526 14.411 16.567 16.567 16.567

lt = L - ðL- βÞe - ðktÞ

δ

where l = length and t = time. The four parameters are: β, is the lower asymptote (maximum size reach at t → 0); L, is the upper asymptote (maximum size reach at t → infinity); k, is the growth rate and δ, is a parameter that controls the x-ordinate for the point of inflection. Pandey et al. (2003) recorded the temporal progression of intensity of floral malformation in the mango cv Amrapali from the month of February the time of emergence of flower buds up to the month of April when the panicles normally ceased to grow further. The results have been presented in Table 5.13. The data were plotted using MS Excel programme and the graph (Fig. 5.10) with long lag phase,

42

5 Modelling of Epidemic Dynamic

Fig. 5.10 The growth curve of length of malformed panicles of cv. Amrapali in different months

24

Length of Panicle (cm)

19

14

9

Length of panicle (cm)

3.4.2001

27.3.2001

20.3.2001

13.3.2001

6.3.2001

27.2.2001

-1

20.2.2001

4

Weibull Growth curve

Fig. 5.11 Weibull growth curve

stiff slope and prolonged stationary phase showed close similarity with the Weibull growth curve (Fig. 5.11).

5.11

5.10

Monomolecular Model

43

Monomolecular Model

The growth curve depends on the nature of the epidemic. Polycyclic epidemics that are caused by pathogens capable of several infection cycles a season, e.g. late blight of potato are usually best modelled by logistic model whereas monocyclic epidemic that has only one infection cycle per season (they are typical of soil-borne diseases such as Fusarium wilt of flax) may be best modelled using monomolecular model. This model is also called negative exponential model. The correct choice of model is essential for a disease forecasting system to be successful.

5.11

Monomolecular Model dy = QR ð100- yÞ dt

where Q = amount of initial inoculum, R = infection efficacy of the inoculum, y = disease intensity Downy mildew of opium poppy caused by Peronospora arborescence perpetuates through infected plant debris in soil. When seeds are sown in the infested soil, the seedlings emerge in a highly distorted form (systemic infection). Within fortnight, the pathogen multiplies over the systemically infected seedlings and the secondary disease cycle starts. Chakrabarti (unpublished) counted the systemically infected seedling as they started appearing and continued the recording at 3 days interval from 7.11.2003 to 16.11.2003 (Table 5.14). The data were plotted. The graph (Fig. 5.12) showed close similarity with the Monomolecular growth curve of monocyclic disease (Fig. 5.13) or Malthusian mode (Fig. 5.14). The model was successfully validated (Table 5.15). Equation y = y0ekt y = 0.936 exp. (0.312*X) shows that primary infected plants are undergoing exponential growth and is an exponential function of time. Y0 = 0.936 represents the primary infected plants at 0.94% at time t = 0, and k = 0.312 > 0 is a constant, called the growth constant. It can be seen that at t = 4 (12 days), primary infected plants have grown to almost four times, i.e. 3.26%. Table 5.14 Incidence of primary infected downy mildew seedlings from soil bore inoculum of opium poppy

Date of observation 7.11.2003 10.11.2003 13.11.2003 16.11.2003

Primary infected plants (%) 1.1 2.1 2.6 2.9

44

5 Modelling of Epidemic Dynamic

Fig. 5.12 Opium poppy: primary downy mildew infected plants from soilborne oospores

Fig. 5.13 Monomolecular growth curve

3.5 3 2.5 2 Series1

1.5 1 0.5 0 1

5.12

2

3

4

Exponential Model

This model is also known as the logarithmic, geometric or Malthusian model. This growth model is appropriate when newly diseased (infected) individuals lead to more diseased (infected) individuals and has been used to model changes in disease prevalence on a geographic scale, it can be applied to describe the very early stages of most polycyclic epidemics. Kumar and Chakrabarti (1997a) recorded the dissemination of malformation disease from the source plant in a mango orchard of the cv Dashehari (Table 5.16). Plant to plant distance was 10 m. The data were plotted and the

5.12

Exponential Model

45

Fig. 5.14 Exponential growth of primary infected plants of opium poppy Table 5.15 Validation of the model for growth curve of primary infected plants of opium poppy Time (interval of 3 days) 0 1 2 3 4

Actual Y 1.1 2.1 2.6 2.9

Predicted Y (Growth model) 0.94 1.28 1.75 2.39 3.26

Table 5.16 Floral malformation (%) at varying distance from the source of infection in mango cv. Dashehari Distance of plants from the source of infection (m) 0 (Malformed plant that serves as source of infection) 10 20 30 40

Incidence of floral malformation (%) 9.81 2.85 1.87 1.26 0.56

graph (Fig. 5.15) showed close similarities with exponential growth curve (Fig. 5.16). Equation y = y0ekt y = 7.615 exp. (-0.06*X) shows that incidence of floral malformation is undergoing exponential growth (negative) (Fig. 5.17). Y0 = 7.615 represents the initial incidence of floral malformation 7.64% near the source t = 0, and k = -0.06 < 0 is a constant, called the growth (negative) constant. It can be seen that incidence of floral malformation at 40 m away from the source plant (t = 40 days) has declined to 0.56%.

5 Modelling of Epidemic Dynamic

Disease Incidence

Fig. 5.15 Spread of malformation disease from source to other plants in an orchard of mango cv. Dashehari

0.002 0.003 0.004 0.005 0.006 0.007 0.008

46

0

20

40

60

80

100

Time

Fig. 5.16 Exponential growth curve

12

10 8 6

Series1

4 2 0 1

5.13

2

3

4

5

Infection Rate

van der Plank (1960) proposed two equations to calculate the rate of infection during the disease progress. The rates of infection (‘r’) during normal distribution trial, i.e. before ‘take off’ of the disease and that of during the exponential growth phase, i.e. ‘take off’ stage were calculated by the following formula.

5.13

Infection Rate

47

12 10 8

y = 7.615e-0.065x 6 4 2

0 0

5

10

15

20

25

30

35

40

45

Incidence of floral malformaon (%) Expon. (Incidence of floral malformaon (%)) Fig. 5.17 Equation for incidence of floral malformation (%) at different distances from the source plants

5.13.1 Before ‘Take Off’ of the Disease et 1 - t 2

r I = 100 I o

where t1 – t2 is the time interval, r is the infection rate and I and I0 are number of infected plant parts at the initial stage and at the time of onset of the epidemic, respectively.

5.13.2 During ‘Take Off’ Stage of the Epidemic r=

X ð1 - X 1 Þ 230 log 2 t1 - t2 X 1 ð1 - X 2 Þ

where X1 and X2 are the proportion of the susceptible tissues and t1 – t2 is the interval in dates. Examples In the cv. Mallika (Table 5.11; Fig. 5.18), the disease progression period during 1991–1992 may be termed as pre-take off stage of the epidemic. The exponential

48

5 Modelling of Epidemic Dynamic

40.14

19.75

8.71 3.92

4

1991

1992

1993

1994

1995

Fig. 5.18 Number (%) of malformed panicles during the year 1991–1995 on Mangifera indica cv. Mallika

growth phase was in between 1992 and 1994 (Fig. 5.18). The infection rate (r) during these periods was calculated in the following way.

5.13.3 Before ‘Take Off’ of the Epidemic t1 – t2 = 1 year, I (percentage of malformed panicles at the initial stage) = 3.92, Io (percentage of malformed panicles at the time of onset of the epidemic) = 4.00 et 1 - t 2 e1 r=

r I = 100 I o

r 3:92 = = 0:98 100 4:00

0:98  100 = 36:05% 2:718

where e1 = 2.718, e2 = 7.389

5.13.4 During ‘Take Off’ of the Epidemic Amount of disease present (X1) in 1992, at the time initial stage of take-off (t1), was 4.0%. Proportion of the susceptible tissues left (1-X1) is 96%. Similarly, the amount of disease at the peak point (X2) at the time t2 in 1994 was 40.14. Proportion of the susceptible tissues left (1-X2)) is 59.86%.

5.14

Disease Growth Rate (DGR)

49

r=

X ð1 - X 1 Þ 230 log 2 t1 - t2 X 1 ð1 - X 2 Þ

where X1 = 4.0% and 1-X1 = 96% X2 = 40.14% and 1-X2 = 59.86% r=

40:14ð1 - 4Þ 230 40:14  96 = 115 log log = 115 log 16:09 = 115  1:2 2 4  59:86 4ð1 - 40:14Þ

= 138%

5.14

Disease Growth Rate (DGR)

Disease growth rate is estimated using the following equation: DGR =

A:D:I × 100 b

where ADI = Average disease incidence and b – regression Example Average disease incidence (average number of malformed panicles) in the cvs. Himsagar and Neelam between 1991 and 94 have been presented in Tables 5.17 and 5.18. Thus applying the above equation, the disease growth rate for both the cvs were estimated.

b=

P P P nð xyÞ - ð xÞð yÞ 4 × 256:36 - 10 × 76:89 292:54 = = = 14:63 P 2 P 4 × 30 - 10 × 10 20 nð x Þ - nð xÞ 2

Table 5.17 Estimation of disease growth rate of the cvs Himsagar and Neelum Year of observation 1991 1992 1993 1994 Average disease incidence ADI Regression coefficient ‘b’ Disease growth rate = ADI/b

cv. Himsagar 4.00 7.33 15.54 50.02 (4.00 + 7.33 + 15.54 + 50.02)/ 4 = 19.22 14.63* =19.22/14.63 = 1.31%

cv. Neelum 1.48 3.51 3.62 48.81 14.35 14.21 =14.35/ 14.21 = 1.01%

50

5 Modelling of Epidemic Dynamic

Table 5.18 Estimation of average disease incidence of the Himsagar and Neelum Year of observation 1 2 3 4 Sum = 10

5.15

cv. Himsagar 4 7.33 15.54 50.02 Sum = 76.89

xy 4 14.66 46.62 200.08 Sum = 265.36

x^2 1 4 9 16 Sum = 30

Area Under Disease Progress Curve (DPC)

Area under disease progress curve (DPC) is used to assess quantitative disease resistance in crop cultivars and as a descriptor for the epidemic to measure crop losses. It is also useful for quantitative summary of disease intensity over times, for comparison across years, locations and management measures. AUDPC is most easily and commonly measured using the trapezoidal method. The higher value of AUDPC indicates more susceptibility or yield loss.   XN i - 1 yi þ yiþ1 ðt iþ1 - t 1 Þ AUDPC = i=1 2 where Y1 is disease severity and t1 is time. In this method, one starts with disease severity data (Y1) collected at various times t1. Then the average disease severity at the midpoint between each two-time points is calculated. After this, the average disease severity is multiplied by the length of time between the two points. Finally, those products across all time intervals are sum up to get AUDPC. Examples The number of malformed panicles of an alternate (Himsagar) and one regular (Neelum) bearing cvs during 1991–1994 were collected four times at 1 year interval (Table 5.19). The AUDPC of these two cvs. are presented in Tables 5.20 and 5.21. The incidence of downy mildew-affected (primary infection) plants was recorded at 3 days interval four times under field condition (Table 5.14), and its AUDPC was determined (Table 5.22).

Table 5.19 Number of malformed panicles in alternate (Himsagar) and regular (Neelum) bearing cultivars of mango during the crop season 1991–1994

Year of observation 1991 1992 1993 1994

cv. Himsagar 4.00 7.33 15.54 50.02

cv. Neelum 1.48 3.51 3.62 48.81

5.16

Predicted Success of a Single Spore Inoculum of Causing Infection

51

Table 5.20 AUDPC of the cv. Himsagar Sample Year Severity T0 0 Y0 = 4 T1 1 Y1 = 7.33 2 Y2 = 15.54 T2 3 Y3 = 50.2 T3 For Himsagar AUDPC

Time interval

Avg. Sev

Time Severity

(T1-T0) = 1 (T2-T1) = 1 (T3-T2) = 1

(Y0 + Y1)/2 = 5.66 (Y1 + Y2)/2 = 11.43 (Y2 + Y3)/2 = 32.87

5.66*1 = 5.66 11.43*1 = 11.43 32.87*1 = 32.87 49.96

Time interval

Avg. severity

Time severity

(T1-T0) = 1 (T2-T1) = 1 (T3-T2) = 1

(Y0+ Y1)/2 = 2.49 (Y1+ Y2)/2 = 3.56 (Y2+ Y3)/2 = 26.22

2.49*1 = 2.49 3.56*1 = 3.56 26.22*1 = 26.22 32.27

Table 5.21 AUDPC of the cv. Neelum Sample Year Severity T0 0 Y0 = 1.48 T1 1 Y1 = 3.51 2 Y2 = 3.62 T2 3 Y3 = 48.81 T3 For Neelum AUDPC

Table 5.22 AUDPC of downy mildew of opium poppy Sample Days Severity Time interval T0 0 Y0 = 1.1 (T1-T0) = 3 T1 3 Y1 = 2.1 (T2-T1) = 3 T2 6 Y2 = 2.6 (T3-T2) = 3 T3 9 Y3 = 2.9 For downy mildew of opium poppy AUDPC

Avg. severity

Time severity

(Y0 + Y1)/2 = 1.6 (Y1 + Y2)/2 = 2.35 (Y2 + Y3)/2 = 2.25

1.6*3 = 4.80 2.35*3 = 7.05 2.25*3 = 6.75 18.60

The AUDPC of the cv. Himsagar was higher than that of Neelum. Hence, it may be concluded that Himsagar is more susceptible than the cv. Neelum. On the other hand, AUDPC of opium poppy against the downy mildew was the minimum. The mango malformation is polyetic in nature, i.e. over the times it behaves like a polycyclic disease. While the downy mildew of opium poppy at the initial stage behaves like a monocyclic disease.

5.16

Predicted Success of a Single Spore Inoculum of Causing Infection

The efficiency of an inoculum of causing infection under a new environmental condition is estimated following the equation of Baker (1971): p = 1–0:51=d , where p = probability that one spore will succeed in infecting, ‘d’ = effective inoculum population causing 50% infection (EID50).

52

5 Modelling of Epidemic Dynamic

Wastie (1962) placed spores of Botrytis fabae and B. cinerea on broad bean leaves at various densities and observed lesions. The value of d at ED50 for B. fabae was 4, and for B. cinerea was 500. Substituting in the above equation the calculated per cent success in single spore inoculum is 16% and 0.14%, respectively. The actually observed values were 13 and 0.9%, respectively. B:fabae p = 1 - 0:51=d = 1 - 0:51=4 = 1 - 0:8409 = 0:1591 ≈ 16% B:cinerea p = 1 - 0:51=d = 1 - 0:51=500 = 1 - 0:9986 = 0:00139 ≈ 0:14% Similarly, Chakrabarti and Kumar (1997) conducted an experiment with Amrapali and Mallika, two dwarf but highly malformation-susceptible hybrids of mango developed in New Delhi, a hot spot for the malformation disease. The experiment was conducted to find out if these infected plant materials on being introduced from N. Delhi to West Bengal, an area that is believed to have unfavourable weather conditions for the disease development could produce the disease symptoms and subsequently spread the disease to other cvs. Growing in the locality, i.e. the spread of malformation disease of mango from the foci (Table 5.1). For this, they first determined the ‘d’ value for Amrapali and Mallika. Then by applying the equation of Baker (1971), the ‘p’ values of Amrapali and Mallika were estimated. The experiment was conducted in an orchard with 81 plants of mixed cvs. The plants were 10–12 years old, and plant-to-plant distance was 10 m. The data were recorded in ‘on years’. The number of doublets (‘d’ values) was calculated by the following equation: D ð50%infectionÞ = 1=n μ ðμ - 1Þ where n = number of plants examined, μ = number of adjacent pairs of infected plants The ‘d’ values for Amrapali and Mallika were 60 and 26.92%, respectively. By applying the equation of Baker (1971), the ‘p’ values of Amrapali and Mallika were estimated p = 1 - 0:51=d = 1 - 0:51=60 = 1 - 0:9885 = 0:01149 ≈ 1:14% p = 1 - 0:51=d = 1 - 0:5ð26:92Þ = 1 - 0:9746 = 0:0254 ≈ 2:54% 1

Thus, the ‘p’ values of Amrapali and Mallika were estimated as 1.14% (EID50 60) and 2.54% (EID50 26.92), respectively.

5.17

5.17

Doublet Analysis (for Determining ‘d’ Value) (van der Plank 1960)

53

Doublet Analysis (for Determining ‘d’ Value) (van der Plank 1960)

In doublet analysis, the observed number of adjacent pairs of infected plants is compared with a value expected if infections occurred at random, or from outside sources. Numbers of adjacent pairs are greater than this value if infections have spread from foci within the field. Differences are greater at lower levels of infection and decrease with increasing disease level (Table 5.23). To find out if there was a spread of the infection from the source plant van der Plant (1960) suggested ‘doublet’ test. In doublet analysis, the observed number of adjacent pairs of infected plants is compared with a value expected if infections occurred at random, or from outside sources. Numbers of adjacent pairs are greater than this value if infections have spread from foci within the field. Differences are greater at lower levels of infection and decrease with increasing disease level. Chakrabarti and Kumar (1997) conducted an experiment to find out the spread of malformation disease of mango from the foci (Table 5.23). The experiment was conducted in an orchard with 81 plants of mixed cvs. The plants were 10–12 years old, and plant-to-plant distance was 10 meters. The data were recorded in ‘on years’. Number of observed and calculated adjacent pairs of infected (doublet) plants during the years 1991, 1993 and 1995 are presented in Figs. 5.19, 5.20 and 5.21, respectively and in Table 5.23. The expected number of doublets was calculated by the following equation: d = 1/n μ (μ - 1) where n = number of plants examined, μ = number of adjacent pairs of infected plants The results presented in Table 5.23 show that the number of observed doublets in all the 3 years were greater than the number of expected (calculated) doublet. Further, the observed doublet in 1991, 1993 and 1995 was more than the expected doublet by 125, 90 and 81 times. Thus, with increased disease incidence the difference between ‘O’ and ‘C’ decreased. Hence, the results fulfilled Van der Plank’s equation for doublet analysis. Therefore, it may be concluded that the disease was spread from the source plant. Table 5.23 Number of observed and calculated doublets (adjacent pairs of infected plants) during the years 1991, 1993 and 1995

Year of observation 1991 1993 1995

Total plants (T) 81 81 81

Infected plants with more than 50% malformed panicles (A) 5 13 29

No. adjacent pairs of infected (doublet) plants (O) 3 10 21

No. adjacent pairs of infected (doublet) plants calculated (C) 0.024 0.111 0.27

Difference between ‘O’ and ‘C’ 2.976 9.989 21.973

‘O’ value is greater than ‘C’ value (‘C’/‘O’) by 125 times 90 times 81 times

54

5 Modelling of Epidemic Dynamic

• •

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•= malformed plants with > 50% malformed panicles (source plants) • = healthy plants Fig. 5.19 Incidence of malformation disease in a mango orchard of mixed cvs. in the ‘on year’ of 1991

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•= malformed plants with > 50% malformed panicles (source plants) •= malformed plants with 50% malformed panicles (source plants) •= malformed plants with 0.5 mm) in a two-day window. Hence, daily risk index is the product of the proportion of susceptible tissue and infection rate. Climatic Data—In each one of the selected sites historical weather data included daily values of maximum and minimum temperature, precipitation, and solar radiation from 1970 to 2000. Climate change scenarios were obtained based on LARSWG and HadleyCM3 projections. LARS-WG is a stochastic weather generator that may be used for the simulation of weather data at a single site, under both current and future climate conditions. In the present investigation, LARS-WG was used to obtain synthetic weather series taking into account the changes that occurred in climate during the last century (comparing the periods 1930–1960 to 1970–2000). By means of Hadley CM3, under A2 emissions scenario centered in 2020, the second climate change scenario was obtained. For this purpose, the rate of change of each variable (from the comparison between GCM projections and the baseline period (1960–1990) was applied to the daily climate record in each site.

10.10 Our Observations (Mittal and Chakrabarti, Unpublished) In case of multi-model ensembles, the tools like excel, spss are unsuccessful in implementing the multi-model ensemble functions hence to implement the model a code for statistical computing in R environment can be used (https://www.r-project. org/). R-code can have the use of libraries “rgdal,” “readr,” “tidyverse,” “ggplot,” “ensembleBMA,” “ensembleMOS,” and “EnsembleML.” EnsembleML is an R package for ensemble in machine learning is able to combine and build regression and classification models and combine those models to be an ensemble. Similarly in the integrated modeling approach also, the tools like excel, spss are unsuccessful in implementing the function of HHR and hence in this case also for implementation the model a code for statistical computing in R environment may be used (https://www.r-project.org/). R-code can have the use of libraries “rgdal,” “readr,” “tidyverse,” “spdep,” “surveillance,”and “hhh4 contacts.” For Linked process-based model, the development of a program in R environment can help implement a stochastic weather generator. A few packages can be

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loaded help the run the simulators in R “dplyr,” “lubridate,” “tidyyr,” “ggplot2,” “moments,” and “weathergen.”

References Ahn JB, Hong JH (2013) Projection of fine-resolution climate changes over Korean Peninsula based on RCP scenarios using dynamic downscaling with WRF. Poster Abstract. The International Conference on Regional Climate- CORDEX 2013, Po-P3-03 Bourgeois G, Bourque A, Deaudelin GJCJ (2004) Modelling the impact of climate change on disease incidence: a bioclimatic challenge. Can J Plant Pathol 26(3):284–290 Bregaglio S (2012) Definition and implementation of plant disease simulation models in interaction with crop models, aiming at forecasting the impact of climate change scenarios on crop production. Ph.D. Thesis, submitted to Department of Plant Production – University of Milan, Milan, Italy Dixon GR (2012) Climate change – impact on crop growth and food production, and plant pathogens. Can J Plant Pathol 34:362–379 Evans N, Baierl A, Semenov AM, Gladders P, Fitt BDL (2007) Range and severity of a plant disease increased by global warming. J R Soc Interface 5:525–531 Evans N, Gladders P, Bruce DL, von Tiedemann A (2013) Climate change in Europe: altered life cycles and spread of major pathogens in oilseed rape. https://www.gcirc.org/fileadmin/ documents/Bulletins/B25/B25_03Climate_change_in.pdf Fernandes J M, Cunha G R, Del Ponte E, Pavan W, Pires JL, Baethgen W, Gimene A, Magrin G, Travasso MI. (2004) Modeling fusarium head blight in wheat under climate change using linked process-based models. Proceedings of the 2nd International Symposium on Fusarium Head Blight incorporating the 8th European Fusarium Seminar, USA 11–15 December, 2004, pp. 441–44 Garofalo EW (2019) Apple disease forecasting models: when climate changes the rules. M.Sc. Thesis submitted to University of Massachusetts, Amherst Gautam HR, Bhardwaj ML, Kumar R (2013) Climate change and its impact on plant diseases. Curr Sci 105:1685–1691 Inter-Governmental Panel on Climate Change (IPCC) (2007) Impact, adaptation and vulnerability. Cambridge University Press, Cambridge, p 976. https://www.ipcc.ch Kim KH, Cho J (2015) Predicting potential epidemics of rice diseases in Korea using multi-model ensembles for assessment of climate change impacts with uncertainty information. Clim Chang 134(1–2):327–339. https://doi.org/10.1007/s10584-015-1503-2 Kim KH, Jung I (2020) Development of a daily epidemiological model of rice blast tailored for seasonal disease early warning in South Korea. Plant Pathol J 36(5):406–417 Kim KH, Cho J, Lee YH, Lee WS (2015) Predicting potential epidemics of rice leaf blast and sheath blight in South Korea under the RCP 4.5 and RCP 8.5 climate change scenarios using a rice disease epidemiology model, EPIRICE. Agric For Meteorol 203:191–207 Kobayashi T, Ishiguro K, Nakajima T, Kim HY, Okada M, Kobayashi K (2006) Effects of elevated atmospheric CO2 concentration on the infection of rice blast and sheath blight. Phytopathology 96:425–431 Kudela V (2009) Potential impact of climate change on geographic distribution of plant pathogenic bacteria in Central Europe. Plant Prot Sci 45:S27–S32. Kobayashi T, Ishiguro Lee CK, Kim J, Shon J, Yang WH, Yoon YH, Choi KJ, Kim KS (2012) Impacts of climate change on rice production and adaptation method in Korea as evaluated by simulation study. Korean J Agric for Meteorol 14(4):207–221 Lee FN, Rush MC (1983) Rice sheath blight: a major rice disease. Plant Dis 67:829–833

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Luo Y, Teng PS, Fabellar NG, TeBeest DO (1998) The effects of global temperature change on rice leaf blast epidemics: a simulation study in three agroecological zones. Agric Ecosyst Environ 68:187–196 Mahapatra S, Saha P, Das S (2018) Plant disease forecasting in the era of climate change: trends and applications. https://www.researchgate.net/publication/323725 Mina U, Sinha P (2008) Effects of climate change on plant pathogens. EnviroNews Newsletter of ISEB India 14(4) Newbery F, Qi A, Bruce DL (2016) Modelling impacts of climate change on arable crop diseases: progress, challenges and applications. Curr Opin Plant Biol 32:101–109 Newlands NK (2018, 2018) Model based forecasting of agricultural crop disease risk at the regional scale integrating airborne inoculum, environmental and satellite based monitoring data. Front Environ Sci; https://www.frontiersci.org Pandey S, Shanna M (2010) Climate change: potential impact on chickpea and pigeon pea diseases in the rainfed semi-arid tropics (SAT). In: Proceedings of the 5th International Food Legumes Research Conference (IFLRC V) and 7th European Conference on Grain Legumes (AEP VII), Antalya, Turkey Parthasarathy N, Ou SH (1965) International approach to the problem of blast. The rice blast disease. The Johns Hopkins Press, Johns Hopkins University, Baltimore, pp 1–5 Rosenzweig C, Tubiello FN (2007) Adaptation and mitigation strategies in agriculture: an analysis of potential synergies. Mitig Adapt Strat Glob Change 12:855–873 Savary S, Nelson A, Willocquet L, Pangga I, Aunario J (2012) Modeling and mapping potential epidemics of rice diseases globally. Crop Prot 34:6–17 Sharma IM (2012) Changing disease scenario in apple orchards: perspective, challenges and management strategies. In: Proceedings of the National Symposium on Blending Conventional and Modern Plant Pathology for Sustainable Agriculture, Indian Institute of Horticultural Research, Bengaluru Shekhar M, Singh N (2021) The impact of climate change on changing pattern of maize diseases in Indian subcontinent. In: El-Eswai MA (ed) Maize genetics resources: a review. Tanta University, Egypt. https://doi.org/10.5772/intechopen.101053 Shim KM, Lee DB, Min SH, Kim GY, Jeong HC, Lee SB, Kang KK (2011) Assessing impacts of temperature and carbon dioxide based on A1B climate change scenario on potential yield of winter covered barley in Korea. Clim Change Res 2(4):317–331 Webb KM, Ona I, Bai J, Garrett KA, Mew T, Cruz V, Leach JE (2010) A benefit of high temperature: increased effectiveness of a rice bacterial blight disease resistance gene. New Phytol 185:568–557

Disease Detection: Imaging Technology and Remote Sensing

11.1

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Imaging Techniques and Spectroscopic for Disease Detection

The spectroscopic and imaging techniques are unique disease monitoring methods that have been used to detect diseases and stress due to various factors, in plants and trees. Current research activities are toward the development of such technologies to create a practical tool for large-scale real-time disease monitoring under field conditions. Spectroscopic technology has been successfully applied for plant stress detection such as water-stress detection and nutrient-stress detection. In addition, there have also been significant applications for monitoring the quality of postharvest fruits and vegetables. Various spectroscopic and imaging techniques have been studied for the detection of symptomatic and asymptomatic plant diseases. Some of the methods are: hyperspectral imaging, hyperspectral imaging, fluorescence imaging, fluorescence spectroscopy, visible and infrared spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy. Spectral analysis is relatively easy to apply as it is a noninvasive method and does not require further operations. For a long time, it has been used for the analysis of the physiological status of plants and for the determination of chlorophyll and carotenoids contents in leaves. It also reflects changes in the contents and the relative proportions of other quality-relevant compounds of crops. Spectral analysis is also commonly applied in remote sensing. Besides, the utilization of spectral analysis for the detection of fungal and bacterial diseases is extensively being investigated. Both fungi and bacteria usually cause damage at molecular, cellular, and/or tissue levels, which, in turn, can be detected as changes in the spectral signatures. The high-resolution imaging such as videography and digital image analysis at various spectral ranges have made it possible to detect different plant diseases. The spectroscopic and imaging techniques include fluorescence spectroscopy, visible-IR spectroscopy, fluorescence imaging, and hyperspectral imaging.

# The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 D. K. Chakrabarti, P. Mittal, Plant Disease Forecasting Systems, https://doi.org/10.1007/978-981-99-1210-0_11

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(i) Hyper-spectral imaging. Local variations in the disease-induced physiological alterations of the plant tissues may help to early identify the development of the infection. For example, distinct changes in the visible (VIS) and nearinfrared (NIR) range in spring wheat leaves were recorded after infection with Drechslera triticirepenti (Muhammed and Larsolle 2003). Similarly, three Puccinia rust species could be determined by image analyses of different visible and near-infrared wavelength ranges in wheat leaves (Devdas et al. 2009). Franke et al. (2005) observed that the hyper-spectral imaging was more effective than the multispectral approaches for the detection of leaf rust. The system for the detection of plant diseases and infection-related damages in crops could be further improved by combining spectral and spatial information provided by image analysis facilities. Bravo et al. (2004) succeeded to detect yellow rust on winter wheat with a very high accuracy of only 5–6% error rate with the help of combined hyper-spectral and fluorescence image analysis (Nilsson 1995). Wiwart et al. (2001) found a high correlation between the hue saturation intensity (HSI) color components of RGB (red, green, and blue) images and the Fusarium disease-reduced thousand seed mass of healthy and infected wheat. Employing a model-based statistical analysis of NIR spectra, mold-damaged wheat kernels could be separated with an accuracy of 95% (Delwiche 2003). Peiris et al. (2009) and Dowell et al. (1999) successfully applied spectral analyses in the NIR range to determine the DON (deoxynivalenol, a mycotoxin) content of Fusarium-infected grains. Polder et al. (2005) subsequently analyzed spectral information of transmission spectral imaging in both VIS and NIR range and Fusarium culmorum DNA content by PLS (partial least square modeling). Up to now, research on Fusarium infection was mainly focused on very basic approaches to detect this fungal disease in cereal kernels. Early analysis of potential crop infection by spectral imaging may, indeed, provide the means to fulfill these demands. The spectroscopic and imaging technology could be integrated with an autonomous agricultural vehicle for reliable and real-time plant disease detection to achieve superior plant disease control and management. (ii) Fluorescence imaging is an advancement of fluorescence spectroscopy, where fluorescence images (rather than single spectra) are obtained using a camera. A xenon or halogen lamp is used as a UV light source for fluorescence excitation, and the fluorescence at specific wavelengths is recorded using the chargecoupled device (CCD)-based camera system (Bravo et al. 2004). The chlorophyll fluorescence imaging can be an effective tool in monitoring leaf diseases (Chaerle et al. 2004; Scharte et al. 2005; Lenk et al. 2007). Chaerle et al. (2004) used blue-green fluorescence to evaluate the effectiveness of this technique in observing the development of tobacco mosaic virus (TMV) infection in tobacco plants. (iii) Fluorescence spectroscopy refers to a type of spectroscopic method, where the fluorescence from the object of interest is measured after excitation with a beam of light (usually ultraviolet spectra). Belasque Jr et al. (2008) employed fluorescence spectroscopy to detect stress caused by citrus canker (bacterial disease

11.2

Monitoring Weather

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caused by Xanthomonas citri and X. axonopodis pv. citri) and mechanical injury. (iv) Visible and infrared spectroscopy has been used as a rapid, noninvasive, and cost-effective method for the detection of plant diseases. The visible and infrared regions of the electromagnetic spectra are known to provide the maximum information on the physiological stress levels in the plants (Muhammed and Larsolle 2003; Muhammed 2005; Xu et al. 2007) and thus, some of these wavebands specific to a disease can be used to detect plant diseases (West et al. 2003), even before the symptoms are visible. In general, visible spectroscopy is used for disease detection in plants in combination with infrared spectroscopy (Malthus and Madeira 1993; Bravo et al. 2003). (v) Nuclear magnetic resonance (NMR) spectroscopy. Plants produce large number of secondary metabolites to interact with beneficial or harmful organisms. Secondary metabolites may provide plant resistance. NMR-based metabolomics has been used to identify candidate compounds for host resistance. It offers noninvasive analysis of metabolites in crude plant extract or in intact tissues. NMR spectra are specific for every single compound. NMR spectroscopy measures the resonance of magnetic nuclei such as 1H, 13C, and 15 N. NMR method provides access to both qualitative as well as quantitative information of the signal compound. For the identification of metabolites, database of NMR spectra of common plant metabolite is needed (Leiss et al. 2011). Profiling of Plant Volatile Organic Compounds for Disease Detection. The volatile organic compounds (VOC) released by plants and trees contribute about two-thirds of the total VOC emissions present in the atmosphere (Guenther 1997). Prithiviraj et al. (2004) assessed the variability in the volatiles released from onion bulbs infected with bacterial (Erwinia carotovora causing soft rot) and fungal species (Fusarium oxysporum and Botrytis allii causing basal and neck rots) using HAPSITE, commercial portable GC–MS (Gas chromatography-Mass spectrometry) instrument. The study indicated that 25 volatile compounds (among the 59 consistently detected compounds) released from onion can be used to identify the disease based on VOC profiling.

11.2

Monitoring Weather

The weather parameters have an overriding effect on disease development. Thus, weather has been the most critical factor in disease monitoring and forecasting. Weather is routinely recorded by government agencies, State Agricultural universities, and Krishi Vigan Kendras (Extension center) in India and is used, along with satellite photos and other data sources, for broad-scale weather forecasting. But the microclimate, i.e., the weather within the crop has a more direct impact on disease than broad-scale weather. A variety of devices has been employed for monitoring microclimatic factors such as duration of leaf wetness and

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temperature that influence greatly the plant disease development. In recent times, the development of electronic monitors for many aspects of microclimate, along with compact and reliable electronic data recorders, has been highly advantageous for monitoring the microclimatic factors that are crucial for particular diseases. With further development, these devices will become sufficiently cheap and compact for widespread use on individual farms, resulting into very accurate monitoring of particular fields.

11.3

Microprocessor-Based Data Recording System

As mentioned above, the field study of plant diseases usually requires equipment to monitor environmental parameters. The instruments that are used in present day have increased in technical sophistication from the clockwork-driven chart types to more recent microprocessor-based equipment. The disease warning systems are programmed to predict the response of the pathogen to prevailing weather conditions from previously derived epidemiological data. There are some models that have been formulated to monitor disease progress as a function of climatic influences. In order to predict the disease incidence quantitatively in individual orchards from flowering to fruit set, it was necessary to adopt an in-field computer system that not only acts as a data storage facility for the large quantities of climatic data required but also computes infection levels. In field, data recording was also required for daily updates of disease predictions and weather data. With such systems, the timing of fungicide applications can be more precise and unnecessary spray applications during periods when environmental conditions do not favor the growth of the pathogen can be avoided. Microprocessor systems for disease warning are used for the prediction of a number of diseases including apple scab caused by Venturia inaequalis (Cke.) Wint. (Jones et al. 1980, 1984) and potato late blight caused by Phytophthora infestans (Mont.) de Barry (MacKenzie 1981).

11.4

On Farm Weather Station

For forecasting, plant diseases importance and necessity of weather data can hardly be overemphasized. Actual weather on the ground varies a lot, even over a few kilometers. And even a few degrees of difference in temperature or millimeters of rainfall can have huge effects on disease outbreak. This is where the on-farm weather station comes in handy. For accurate disease prediction and making more effective IPM strategy having accurate weather data is prerequisite. Today’s weather sensors and stations are largely digital and can feed information to a personal computer or mobile device instantly. A list of a few major components and features of on-farm weather station is presented here.

11.7

11.5

The Data-Logger

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Components of Weather Stations

Anemometer—Measures wind direction and speed. Thermometer—Measures atmospheric temperature. Hygrometer—Measures relative humidity using a percentage measure of water vapor in the air. Barometer—Measures atmospheric pressure to predict precipitation. Rain Gauge—Measures liquid precipitation using an open container. They usually empty automatically and measure the amount of rainfall over a given time interval. Pyranometer—Measures solar radiation levels from the sun in watts per square meter (used to calculate “evapotranspiration,” the rate at which water evaporates from the soil). UV Sensor—Measures UV rays from the sun. These sensors are used for precision growing in particular crops like cannabis, where overexposure to UV rays can stunt leaf growth or affect potency. Leaf Wetness Sensor—Measures surface moisture of the plants on a scale of 0–15 (dry to saturated). Data from these sensors are used in fungal disease control. Soil Moisture Sensor—Measures water levels in the soil. Soil Temperature Sensor—Monitors the soil temperature to detect freezing, or high temperatures that can put crops at risk. Also used to calculate the rate of evapotranspiration.

11.6

Automatic Weather Station

An automatic weather station (AWS) is an automated version of the traditional weather station, either to save human labor or to enable measurements from remote areas. An AWS will typically consist of a weather-proof enclosure containing the data logger, rechargeable battery, telemetry, and the meteorological sensors with an attached solar panel or wind turbine and mounted upon a mast.

11.7

The Data-Logger

The data-logger is the heart of the Automatic Weather Station. The main functions of a data-logger are: 1. Measurement: The data-logger collects the information from every sensor and archives it. 2. Calculation: The data-logger processes most of the meteorological data for the users (avg, min, and max). 3. Data storage: The data-logger saves all the data either on its own memory or on uSD memory card.

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4. Power supply: The data-logger manages the power supply of the Automatic Weather Station, using a solar panel for instance. 5. Communication: The data-logger manages the communication protocols with the remote server.

11.8

Mast

Usually, the 3 m (9.8 ft.) mast is used for the measurement of parameters that affect crops.

11.9

Power Supply

Many stations with lower power equipment usually use one or more solar panels connected with one or more rechargeable batteries. The output from the solar panels may be supplemented by a wind turbine to provide power during periods of poor sunlight, or by direct connection to the local electrical grid.

11.10 Remote Sensing Plant protection involves the correct and timely identification and control of diseases. The identification of diseases is a difficult task and often requires consultation with specialists. An accurate and rapid diagnosis can avoid costly mistakes by timely applications of appropriate management practices. Assessment of crop disease damage is typically determined using field scouting, which is expensive, time-consuming, and difficult for large farms. Field scouting is usually conducted on a weekly interval, and treatments are applied if disease is detected. For large-scale operations, conventional ground scouting is incapable of providing efficient disease monitoring in an economical manner. Remote sensing has the potential to detect crop diseases for large-scale operations in a rapid and spatially specific manner. Remote sensing permits to obtain information about an object/a phenomenon through the analysis of data obtained through sensory devices without being in physical contact with that object. In the 1970s, the well-known Land sat series of satellites was in use for biomass sensing and crop/soil moisture sensing, based on spectral analysis of the solar radiation reflected by plants and soils. As the performance of radiation sensors has improved, satellite and airborne receivers have provided increasingly detailed information on the reflected spectra, while fast digital processing of their output data, coupled with data fusion techniques, has led to a variety of powerful, thematic mapping presentations. The maximum size of the image pixels has reduced and can now be 10 m, or less, in the case of satellite and aircraft platforms. Remote sensing has an interdisciplinary approach. It efficiently detects the anomalous locations within a field or orchard that have been differentially affected

11.10

Remote Sensing

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Table 11.1 The spectral regions used in satellite-based remote sensing Region Visible (blue, red, green) Reflective infrared Thermal infrared Microwave

Wavelength 0.4–0.7 mm 0.7–3.0 mm 3.0–15.0 mm 0.1–30 cm

Property Reflectance Reflectance Radiative temperature Brightness temperature

by weeds, diseases, or arthropod pests (Hatfield and Pinter 1993). In fact, earlier scientists were using aerial color-infrared photography for this purpose and relating their findings to laboratory spectra of pest-damaged leaves (Hart and Meyer 1968). The symptoms of plant diseases like rust and wilt can be related to changes in leaf pigments, internal leaf structure, and moisture. Chlorophyll degradation and alterations in the vascular system of the crop plants profoundly influence the reflectance patterns. Thus, spectral vegetation indices (SVIs) that focus on one or more attributes associated with these symptoms could be useful for the identification of the diseased plants. The spectral regions used in satellite-based remote sensing are presented in Table 11.1. Leaf reflectance of sunlight in the visible (VIS, 400–700 nm), near-infrared (NIR, 700–1100 nm), and short-wave infrared (SWIR, 1100–2500 nm) are dependent on multiple interactions. The VIS range is characterized by low reflectance, due to absorption by photoactive plant pigments. Observations made on the rust-affected plants of Vigna spp revealed increased reflectance in red region due to the degradation of chlorophyll by brown-colored pustules on the leaf surface. This was also accompanied by a significant reduction in the reflectance in the near-infrared bands and increase in the water absorption regions due to the disruption of vascular tissues. This information is useful for developing spectral indices that would be highly associated with the incidence and severity of plant diseases. Similar observations were made in response to the incidence of orange rust of sugarcane. The pathogen often induces several physiological changes in plant metabolism which manifest as disease syndrome (Apan et al. 2005; Nilsson 1995; Oerke et al. 2006). The impact of plant diseases on the physiology and phenology of plants, however, varies with the host-pathogen interaction and may cause modifications in pigments, water content, and tissue functionality of plants or in the appearance of pathogen-specific structures (Gamon and Surfus 1999; Jing et al. 2007; Pinter et al. 2003). All these individual impacts may alter the spectral pattern of plants. Knowledge on the physiological effects of diseases on the metabolism and tissue structure of plants is therefore essential for the hyperspectral discrimination of healthy and diseased leaf and canopy elements (Moran et al. 1997). The best results for the detection of diseases were obtained in the VIS and NIR range of the spectrum. Steddom et al. (2005) demonstrated that multispectral disease evaluation can be used effectively to measure necrosis caused by Cercospora leaf spot in sugar beets. Similarly, Yellow rust decreases the chlorophyll a concentration, which leads to an increase in canopy reflectance in the VIS range and a decrease in the NIR (Jing et al.

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2007). Bravo et al. (2003), using a quadratic discriminating model based on reflectance, identified yellow rust infestation on winter wheat with a reliability of 96%. Attempts were also made to measure increasing or decreasing disease severity with the change of reflectance. Cercospora leaf spot increased reflectance in the VIS between 450 and 700 nm. A shift of the red edge position was monitored. Reflectance in the NIR decreased with increasing disease severity, whereas an obvious increase of reflectance in the SWIR (short-wave infrared) was measured. Powdery mildew caused an increase in reflectance over the entire range. This effect was most pronounced in the VIS, and minor in the NIR and SWIR. Sugar beet rust slightly increased reflectance from 550 to 700 nm, reflectance in the NIR and SWIR decreased during pathogenesis. But the spectral data have some limitations in disease detection. Most stress factors, such as diseases, nutrient deficiency, or water stress, induce symptoms with little distinguishing spectral characteristics (Stafford 2000).

11.11 Remote Sensing in India In Indian context (Panigrahy 2007), the launch of the first land resource satellite IRS (Indian Remote Sensing Satellite)-1A in 1988, provided data from two sensors: LISS I (Linear Imaging Self Scanning I) with 72.5 m spatial resolution and LISS II (36.25 m resolution). Since then, a series of satellites from 1B, 1C, 1D, P4, and P6 have been launched. Currently, more than a dozen orbiting satellites exist that provide data in a range of spectral, temporal, and spatial resolutions. Major satellites having optical multispectral sensors are Indian Remote Sensing (IRS) satellites. Today, satellite-borne sensors are used that exploit the full range of Electro Magnetic Radiation (EMR) from visible light, near-, mid-, and far-infrared (thermal), microwave, and long-wave radio energy. The capability of multiple sources of information is unique to remote sensing. Of specific interest is the spectral, temporal, and spatial resolution. Spectral resolution refers to the width or range of each spectral band being recorded. Each target affects different wavelengths of incident energy differently—they are absorbed, reflected, or transmitted in different proportions. Each band of information collected from a sensor contains important and unique data. The spectral region and its property have been highlighted in Table 11.1 (Panigrahy 2007).

11.12 Disease and Pest Management in Potato Panigrahy et al. (1999) successfully identified and classified potato crop using IRS LISS I/II data during 1992–1993 for major potato growing areas in West Bengal like Bardhman district with more than 95% accuracy. The use of RADARSAT Standard beam SAR data also resulted in more than 90% classification accuracy for potato crop in the Bardhaman district.

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Work carried out so far has shown the feasibility of the detection of potato fields damaged by late blight (Arora et al. 2004). However, the most important is the early detection of disease for forewarning purposes. In this direction, hyper-spectral remote sensing has a great role to play. Hyper-spectral can provide spectral information in narrow bands of 1–5 nm in a contiguous spectrum. Studies carried out using ground-based spectroradiometer with the spectral range of 325–1075 nm have shown encouraging results. The optimal bands, in 400–1075 nm spectral range, for discrimination of late blight disease intensities, were reported to be 540, 610, 620, 700, 710, 730, 780, and 1040 nm. This covered green, red, red edge, and nearinfrared region, whereas for lower disease intensity up to 25% the red edge bands (710, 720, and 750 nm) could discriminate the diseased plants from healthy ones.

11.12.1 Tea Pests Dutta et al. (2009) monitored tea plantations in India using remote sensing approaches. The study applies remote sensing technology to monitor pest infestations, water logging, and also pattern identification in an uprooted section. Data were provided from the sensors viz. LISS III (23.5 m resolution), LISS IV MONO (5.8 m resolution), CARTOSAT 1 (2.5 m resolution), and ASTER Data (15 m resolution): Acquired from ITC, The Netherlands, LANDSAT (30 m): Acquired from Global Land Cover Facility, Google Earth Images: Acquired from Google Earth. The delineation of pest-infested areas was successfully done. The gradual spread of infestation was observed during 2001–2004. LANDSAT, LISS III, and ASTER could delineate tea patches into healthy, moderately infested, and infested patches. The percentage of area under healthy, moderately infested, and infested tea was then calculated. LANDSAT and LISS III could delineate the tea areas as healthy and infested patches but lots of interclass mixing could be observed. ASTER could delineate the tea areas as healthy, moderately infested, and infested patches. Therefore, high-resolution images like the ASTER, IKONOS, LISS IV, etc. should be used.

References Apan A, Datt B, Kelly R (2005) Detection of pests and diseases in vegetable crops using hyperspectral sensing: a comparison of reflectance data for different sets of symptoms. In: Proc SSC 2005 Spatial Intelligence, Innovation and Paraxis. The National Biennial Conference of the Spatial Sci Inst. Melbourne 2005, Melbourne, p. 10–18 p. 10–18 Arora RK, Singh A, Panigrahy S (2004) Monitoring late blight affected potato crop through remote sensing. Natl Symp on Crop Surveillance Disease Forecasting and Management, Feb 19–21, 2004, IARI, New Delhi, p. 30–31 Belasque J Jr, Gasparoto MCG, Marcassa LG (2008) Detection on mechanical and disease stress in citrus plants by fluorescence spectroscopy. Appl Opt 47:1922–1926 Bravo C, Moshou D, West J, McCartney A, Ramon H (2003) Early disease detection in wheat fields using spectral reflectance. Biosyst Eng 84:137–145

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Bravo C, Moshou D, Oberti R, West J, McCartney A, Bodria L, Ramo H (2004) Foliar disease detection in the field using optical sensor fusion. Agric Eng Int 6:14 Chaerle L, Hagenbeek D, De Bruyne E, Vateke R, van der Straeten D (2004) Thermal and chlorophyll fluorescence imaging distinguish plant—pathogen interaction at an early stage. Plant Cell Physiol 45:887–896 Delwiche SR (2003) Classification of scab- and bother mold—damaged wheat kernel by near infrared reflectance spectroscopy. Trans ASAE 46:731–738 Devdas R, Lamb DW, Simpfendorfer S, Backhouse D (2009) Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precis Agric 10:459–470 Dowell FE, Ram MS, Seitz LM (1999) Predicting scab, vomitoxin and ergosterol infrared spectroscopy. Cereal Chem 76:573–576 Dutta R, Bhagat RM, Singh A (2009) Monitoring tea plantations in India using remote sensing approaches. The 7th FIG Regional Conference, Hanoi, Vietnam, October 19–22 Franke J, Menz G, Oerke EC, Rascheru. (2005) Comparison of multi- and hyper spectral imaging data of leaf rust infected wheat plants. Plant J 30:601–609 Gamon JA, Surfus JS (1999) Assessing leaf pigment content and activity with a reflectometer. New Phytol 143:105–117 Guenther A (1997) Seasonal and spatial variations in natural volatile organic compound emissions. Ecol Appl 7:34–45 Hart WG, Meyer VI (1968) Infrared aerial colour photography for detection of population of brown soft scale in citrus groves. J Econ Entomol 61:617–624 Hatfield JL, Pinter PJ (1993) Remote sensing of crop protection. Crop Prot 12:403–413 Jing L, Jinbao J, Yunhao C, Yuanyuan W, Wei S, Wenjiang H (2007) Using hyperspectral indices to estimate foliar chlorophyll a concentrations of winter wheat under yellow rust stress. New Zealand J Agric Res 50:1031–1036 Jones AL, Lillevik SL, Fisher PD, Stebbins TC (1980) A microcomputer based instrument to predict primary apple infection periods. Plant Dis 64:69–72 Jones AL, Fisher PD, Seem RC, Kroon JC, DeMotter PG, van. (1984) Development and commercialization of an infield microcomputer delivery system for weather driven predictive models. Plant Dis 68:458–463 Leiss S, Choi YH, Verpoorte R, Peter GL (2011) An overview of NMR based metabolomics to identify secondary plant compounds involved in host plant resistance. Phytochem Rev 10:205– 216 Lenk S, Laury C, Erharol EP, Langsdorf G, Lichtenthaler HK, Buschmann C (2007) Multispectral fluorescence and reflectance imaging at the leaf level and its possible applications. J Exptl Bot 58:807–881 MacKenzie DR (1981) Scheduling fungicide application for potato late blight with BLITECAST. Plant Dis 65:394–399 Malthus J, Madeira AC (1993) High resolution spectra-radiometry: spectral reflectance of field beans leaves infected by Botrytis fabae. Remote Sens Environ 45:107–116 Moran MS, Inoue Y, Barnes EM (1997) Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens Environ 61:319–346 Muhammed HH (2005) Hyperspectral crop reflectance data for characterizing and estimating fungal disease severity in wheat. Biosyst Eng 91:9–20 Muhammed HH, Larsolle A (2003) Feature vector based analysis of hyperspectral crop reflectance data for the discrimination and quantification of fungal disease severity in wheat. Biosyst Eng 86:125–134 Nilsson HE (1995) Remote sensing and image analysis in plant pathology. Annu Rev Phytopathol 15:489–527 Oerke EG, Steiner U, Dehne HW, Lindenthal M (2006) Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. J Expt Bot 57:2121–2132 Panigrahy S (2007) Use of remote sensing and geographic information system for management and planning of potato production in India. Potato J 34:10–15

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Panigrahy S, Manjunath KR, Chakraborty M, Kundu N, Parihar JS (1999) Evaluation of RADARSAT standard became data for identification of potato and rice crops in India. ISPRS J Photogram Remote Sensing 54:254–262 Peiris KHS, Pumphery MO, Dowell FE (2009) NIR absorbance characteristics of deoxynivalenol in aggressiveness of Fusarium graminearum and F culmorum and in resistance to fusarium head blight. Eur J Plant Pathol 108:675–684 Pinter PJ, Hatfield JL, Schepers JS, Barnes EM, Moran M, Daugthry CST, Upchurch DR (2003) Remote sensing for crop management. Photogramm Eng Remote Sens 69:647–664 Polder G, van der Heijden GW, Waalwijk C, Waalwik E, Young IT (2005) Detection of Fusarium in single wheat kernel using spectral imaging. Trans ASAE 45:1155–1161 Prithiviraj B, Vikram A, Kushalappa AC, Yaylayan V (2004) Volatile metabolite profiling of onion inoculated with Eriwinia carotovora, Fusarium oxysporum and Botrytis allii. Eur J Plant Pathol 110:371–377 Scharte J, Schon H, Wels E (2005) Photosynthetic and carbohydrate metabolism in tobacco leaves during an incompatible interaction with Phytophthora nicotiana. Plant Cell Environ 28:1421– 1345 Stafford JV (2000) Implementing precision agriculture in the 21st century. J Agric Eng Res 76:267– 275 Steddom K, Bredehoeft WM, Khan M, Rush MC (2005) Comparison of visual and multispectral radiometric disease evaluations of Cercospora leaf spot of sugar beet. Plant Dis 89:153–158 West JS, Bravo C, Oberti R, Lemaire D, Moshou and McCartney H A. (2003) The potential of optical canopy measurement for targeted control of field crop diseases. Annu Rev Phytopathol 41:593–614 Wiwart M, Koczowka I, Borusiewicz A (2001) Estimation of fusarium head blight of triticale using digital image analysis of grain. Int Conf Comp Analysis Images Patterns 2124:563–569 Xu HR, Ying YB, Fu XP, Zhu SP (2007) Near infrared spectroscopy in detecting leaf miner damage on tomato leaf. Biosyst Eng 96:447–454

Classical Disease Forecasting Systems

12.1

12

Examples of Few Well Known Forecasting Systems

Attempts to forewarn the farmers against the impending onslaught of epidemic were initiated perhaps in the eighteen century, when F. C. Stewart (1897) predicted the break out of the bacterial disease of sweet corn on the basis of temperature prevailing in the “three critical winter months.” Since then, the development of disease forecasting systems has been an ongoing process and already forecasts for a large number of crops have been developed. The forecasting systems that were started as mathematical equations or models based on growth curves have been evolved into computerized knowledge-based expert systems and finally into today’s Web-based disease forecasting. Among the numerous forecasts developed, a few may be considered as classic. For some of them, 80 years have been passed since after their development. But still these are relevant to forecast correctly. Based on their intrinsic rules, various modified versions of forecasts to suit various agro-climatic conditions have been developed in later years.

12.1.1 Potato Late Blight Late blight was a major culprit in the 1840s European, 1845 Irish, and 1846 Highland potato famines. Thus, for historic reason, there was great urgency to develop a forecasting system so that the farmers are not caught unaware. Such a system was developed in Holland following the work of Van Everdingen (1926) who first proposed using a set of “Dutch rules” based upon the presence of dew at night, night-time temperature, mean cloudiness, and rainfall. The work was then taken up by Beamount (1947) and came up with a simple version of Dutch rule, i.e., “Beaumont periods.” The first personal computer-based model on late blight forecast, NEGFRY, was developed in Denmark (Hansen et al. 1995). A system called “BLITECAST,” based on data collected by electronic weather monitors in farmers’ # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 D. K. Chakrabarti, P. Mittal, Plant Disease Forecasting Systems, https://doi.org/10.1007/978-981-99-1210-0_12

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fields, which is now widely used for predicting late blight world over was developed in 1975 by Krause et al. Recently, web-based interactive system is being used for the risk management of potato late blight. Thus, a great number of predictive systems have been developed to forecast the occurrence of late blight, perhaps more than with any other plant disease. A complete review regarding the chronological development of late blight forecasting systems is discussed in considerable detail elsewhere (Miller and O’Brien 1957; Fry and Speilman 1991).

12.1.2 Apple Scab Mills (1944) first laid the foundation of a practical predictive system for apple scab. This system has established a rational basis for management strategy for scab. Mills system has been reevaluated, embellished, and updated by many scientists. In 1992, one computerized, weather-based infection risk model (Beresford and Spink 1992), which identifies the periods of surface wetness that pose a high risk of infection was developed. This was followed by the second model which is an ascospore availability model. The model identifies the periods when large numbers of ascospores are likely to be released from overwintered apple leaves on the ground (Beresford 1999). These two computer-based models are currently used in the New Zealand apple industry to assist in the selection and timing of fungicides for the management of apple scab (black spot), caused by Venturia inaequalis. Both operate with hourly weather data and are currently available in the Metwatch TM software supplied by Hortplus Lt. Besides an expert system POMME has been developed and used by apple growers to manage their orchards. POMME advises growers about when and what to spray on their apples to avoid infestations (Roach et al. 1985).

12.2

Some Other Important Forecasting Systems

12.2.1 Grape Downy Mildew In 1993, Rosa et al. developed a simulation model, PLASMO (Plasmopara Simulation Model) for downy mildew (Plasmopara viticola Berl. et De Toni) of grapevine (Vitis vinifera L.). For the model, the inputs are: temperature and leaf wetness for the inoculation phase, temperature, and relative humidity for the incubation phase, and the type of treatment applied. The model simulates the development of downy mildew on the basis of climatic conditions. The computer program has been developed to facilitate validation and further improvements and to allow direct model use in vineyard management. Similarly, the “Vinemild” (Blaise and Gessller 1989; Gilles 2012) is another forecasting program for downy mildew of grapes. It is a simulation model of the asexual life cycle and a host growth model and both are fed into a progeny/parent ratio model to give an output of the proportional diseased leaf area. The other input for Vinemild is crop meteorological data (hourly temperature, relative humidity, and rainfall values) and at least one observation of the population

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Some Other Important Forecasting Systems

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of the proportion of diseased leaf area in a vineyard at the onset of an epidemic. The program can be implemented on a microcomputer and provides the necessary interface with the user. It is menu driven and allows interactive input of data as well as biological parameters. So far, the Vinemild model has been used as a research tool to investigate the interaction between downy mildew epidemic and crop growth and to understand the effect of fruit yield. Possibly, the aim of Vinemild needs to be changed to predicting when to apply fungicides. Park et al. (1997) developed DMCAST, the third prediction model for grape downy mildew. The DMCAST model uses the parameters like the number of days required for oospores germination and primary infection when almost 3% of oospores are ready to germinate and also the environmental conditions required for primary (oosporic) and secondary (sporangial) infection. It assumes that epidemics are driven by secondary cycles following the initial oosporic infection event. Recently, Orlandini et al. (2008) applied agrometeorology to the analysis and modeling of plant disease development and creating a decision-support system for the operational management of crop protection. The inputs to the models are represented by the fundamental agrometeorological variables of air temperature, relative humidity, rainfall, and leaf wetness. Tuning of parameters has been included to calibrate the models for any possible behavior differences in the patho-system. The main outputs of the model are infection intensity during the growing season, and the timing of the different infection events.

12.2.2 Wheat Stripe Rust Wheat stripe rust is the most important wheat disease in China. The prediction of this disease is significant for making control strategies and taking timely management measures to ensure high and stable yield of wheat. Besides the regression analysis method, support vector machine (SVM) method was used to predict wheat stripe rust (Wang and Ma 2011). The results showed that the support vector machine method can achieve higher fitting accuracy and prediction accuracy and that application of this method for the prediction of wheat stripe rust is feasible and efficient. In practical applications, the optimal combination of support vector machine (SVM) types (a powerful machine learning technique implemented through the software SVM-light. The software enables the users to define six weather parameters) and kernel functions can be selected to carry out the disease prediction. Therefore, a new approach was provided for the prediction of wheat stripe rust. Park and Wellings (2012) combined the grey theory and artificial neural network theory and established three combination forecasting models, and developed the web-based wheat stripe rust forecasting system using network technologies. Earlier Wei-Chang et al. (2008) established a model for forecasting conditions for the occurrence of wheat stripe rust using meteorological factors. Based on the degrees of wheat stripe rust occurrence in previous years and the meteorological data at corresponding periods, the methods of grey correlation analysis and fuzzy mathematics (deals with problems relating to ambiguous, subjective imprecise judgments) were employed to formulate the

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forecast model for four pathogenesis stages according to the time sequence before winter, early March, early April, and middle May.

12.2.3 Blossom Blight of Apples and Pears Fire blight a bacterial disease caused by Erwinia amylovora is prevalent worldwide. The pathogen infects many Rosaceous plants including apple and pear. The disease often appears in the form of a devastating epidemic. Several different systems for predicting fire blight outbreaks (Mills 1955; Luepschen et al. 1961; Powell 1965; Thomson et al. 1982; Zoller and Sisevich 1979; Billing 1980) have been developed from time to time. All of these models, however, deal only with the blossom blight phase of the disease and none are truly predictive. They indicate periods of high risk but could not identify specific infection events. The model of Thomson et al. (1982) was commercially available as the “Western Fire Blight” program on Envirocaster TM field units (Neogen Corp., Lansing, MI). But none of these models has been available as a computer program. Lightner and Steiner (1992) were the first to develop a computer model, MARYBLYT, to predict the disease outbreak at the University of Maryland. Since then, many prototypes of MARYBLYT have been developed. It forecasts specific infection events, identifies when they occur, and predicts symptom appearance for four distinct types of infections. In addition, infection risk assessments and predictions are generated in both real and simulated time, providing an interactive basis for logical decision-making in cost-effective disease management. The MARYBLYT model has been evaluated for the prediction of blossom blight in New Zealand since 1993 and has been shown to be accurate for predicting blossom blight.

12.2.4 Coffee Rust Kushalappa and Eskes (1989) developed a forecasting model incorporating the factors that influence rust development. Fundamental forecast models were developed for coffee rust, following a systems approach, and involved the prediction of amount of rust from net survival ratio for the monocyclic process (SRMP) of H. vastatrix. Later, the significant forecast parameters are identified based on empirical or fundamental disease prediction models. In 1990, Kushalappa improved his model by incorporating significant and practically feasible parameters, establishing action thresholds for these parameters, and recommending fungicides based on action thresholds. Zendaya et al. (2006) estimated the intensity of a coffee rust epidemic. A simple correspondence analysis was used to show that a link could be found between certain production situations and the intensity of coffee rust epidemics. These links were illustrated by a segmentation tree, which helped to define risk domains and rationalize coffee rust control. Recently, Lucas et al. (2011) used nondeterministic learners to alert on coffee rust disease. Their baseline approach was to build an alarm function to learn a regressor that predicts incidences.

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Indian Scenario

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They trained a regression support vector machine (SVM) with quite good results. The correlation between predicted and actual incidences is about 0.94 in a crossvalidation experiment.

12.3

Indian Scenario

In this chapter, a picture of the available forecasting systems in the country, their impact on Indian agriculture, the prospect and limitations of development of predicting equations, or expert systems have been discussed. In India, the first stem rust epidemic of wheat was recorded in 1786 A.D. in central India, and since then, many epidemics have occurred. In India attempts to develop a plant, disease forecasting system was initiated as early as 1925 when K.C. Mehata revealed the mode of recurrence of wheat rust in the Gangetic plains which was till then a baffling phenomenon. Mehata on the basis of trajectories of uredospores from the Himalayas forecasted the appearance of wheat rust at different places in the Indo-Gangetic plain. His work was continued by a number of scientists in the country with great enthusiasm. A team of scientists, namely, R. Prasad, L.M. Joshi, S. Nagrajan, and their associates (Nagarajan and Joshi 1975) further improved the system. The countrywide network of the disease monitoring through remote sensing and adaptation of efficient forecasting system virtually eliminated the menace of rust epidemic from the country. But till now it stands as a solitary success of the country in the field of plant disease forewarning system. Nevertheless, research work on epidemiology and prediction system has been pursued by Indian scientists. But mostly they remained as a matter of academic exercise and could not be employed unlike the wheat rust, in curbing a plant epidemic in large scale. A review of the literature shows that a number of computerized forecasting and Decision Supporting Systems have been developed in recent years and they were validated by limited field trials. The potentiality of some of the important forecasts is briefly mentioned here.

12.3.1 Rice Diseases In the early 1970s, at the Central Rice Research Institute, Cuttack, the relation of meteorological factors on development of rice diseases in respect of blast, Helminthosporiose, and False smut was studied and forecasting systems and control measures of rice diseases were suggested by Padmanabhan et al. (1971). The investigations continued in the ICAR institutes for rice as well as in various state agriculture universities resulting into the development of diverse modeling approaches viz. neural networks and multiple regression for disease prediction in plant populations. Recently Kaundal et al. (2006) used machine learning techniques in forecasting rice blast prediction. They developed an SVM-based web server for rice blast prediction, a first of its kind worldwide, which can help

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the plant science community and farmers in their decision-making process. The server is freely available at http://www.imtech.res.in/raghava/rbpred/ website.

12.3.2 Oilseeds Rapeseed-Mustard is an important oilseed crop in India. The crop production is widely affected by rapeseed-mustard diseases such as Alternaria blight (Alternaria brassicae (Berk.) Sacc.), white rust (Albugo candida (Pers.) Kuntze) and white rot (Sclerotinia sclerotiorum (Lib.) de Bary) downy mildew complex, powdery mildew, and white rot. Kumar et al. (2008) developed an Image Based Rapeseed-Mustard Disease Expert System particularly which may be highly useful for the agriculture extension workers. A multi-location trial on epidemiology and forecasting of oilseeds was conducted at 10 different research centers under the ICAR Coordinated Research Programme. In the study, the severity of different diseases on leaves and pods of different crops were correlated with different weather parameters. Regional and cultivar-specific models devised through step-wise regression could forecast the crop age at which different diseases first appeared on the leaves and pods, the highest disease severity on leave and pods, and the crop age when disease severity reaches its peak on leaves and pods at least 1 week ahead of the first appearance of the disease on the crop (Chattopadhyay et al. 2005). Saini et al. (2009) developed a knowledge management system, KMSCD, for crop disease. The aim of KMSCD is to provide a knowledge management tool for efficient knowledge acquisition, storage, knowledge engineering, processing, and proper maintenance of knowledge that can be ultimately used by the diagnostic expert system. The development of the KMSCD simplifies the complete process of knowledge management by providing user-friendly interface to the domain expert for entering and storing the domain-specific knowledge to solve the disease identification and control problem particularly for oil seeds crops. The system presently applies to the knowledge management of 25 prevalent diseases of three major oil seed crops of India viz. soybean, ground nut, and rapeseed mustard. Earlier Saini et al. (2002) developed another expert system—SOYPEST for the identification and management of major insect pests.

12.3.3 Pulse Crop Recently, Devraj and Jain (2011) designed and developed an expert system for the diagnosis and control of diseases in pulse crops (Puls Expert). Puls Expert is an operational automatic diagnostic tool that helps farmers/extension workers to identify diseases of major pulse crops, viz., chickpea, pigeon pea, mung bean, and urad bean and suggests the appropriate control measures. The automatic knowledge acquisition system of Puls Expert provides user-friendly interface to the domain experts for entering, storing, and structuring the domain-specific knowledge. The knowledge base has been designed after examining the type and structure of the

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Indian Scenario

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knowledge from different sources like literatures, books, databases, farmers, extension workers, etc. Puls Expert was evaluated by a team of field farmers and State Agriculture Officers and it was considered good.

12.3.4 Mango Rajkishore et al. (2006) described a rule-based expert system, AMRAPALIKA, using Expert System Shell for Text Animation (ESTA), for the diagnosis of the most common diseases occurring in Indian mango. The objective is to provide computer-based support for agricultural specialists or farmers. The proposed expert system makes a diagnosis on the basis of responses of the user made against queries related to particular disease symptoms. The knowledge base of the system contains knowledge about symptoms and remedies of 14 diseases of Indian mango tree appearing during the fruiting season and non-fruiting season. The picture based of the system contains pictures related to disease symptoms and is displayed along with the query of the system. The result given by the system has been found to be sound and consistent. Reddy et al. (2009) prepared the vector maps using the geographic information system (GIS). The Fusarium favorable areas are mapped by using “threshold values” of weather parameters and geostatistical techniques. The result so obtained after layering, the favorable conditions for the occurrence of mango malformation were found only in certain pockets of northwestern AP and entire state of UP. GIS tool was found to be very useful for the prediction of spatial distribution of mango malformation at regional scale. Chakrabarti and Chakraborty (2006, 2008) developed the Expert System for the Management of Malformation of Disease of Mango (ESMMDM). It helps to predict the disease incidence and suggests an appropriate integrated management strategy. The expert system is based on the information generated from long-term research on the etiology, epidemiology, and management under both laboratory and field conditions.

12.3.5 Alien Expert Systems Adopted in India For the forecasting systems of late blight disease of potato, scab of apple, and downy mildew of grapes, the Indian Council of Agricultural Research (ICAR) regional and crop-specific institutes have developed modified versions of the already established predicting systems making it suitable for Indian conditions. Jones et al. (1980) have earlier developed a microcomputer-based system to predict primary apple scab infection periods. This system has now been modified as per the Indian climatic conditions and adapted successfully for forecasting primary scab infections in apple orchards in India (Sharma and Gupta 1995). Due to the economic importance of the apple crop and the notoriety of the apple scab disease, the Reuter Stokes apple scab and de Wit leaf wetness recorder tests are also in use in India (Gupta 2000). Krause

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et al. (1975) have formerly developed a computerized system, called Blitecast, for forecasting the occurrence of the late blight of potato and for scheduling timely application of appropriate fungicides. Using the underlying principles of Blitecast, a decision support system(INDO-BLIGHTCAST) has been developed in India (Central Potato Research Institute, Shimla) for forecasting the outbreaks of the late blight disease in potato and prescribing suitable fungicides(Singh et al. 2016). Similarly, the North American Plant Disease Forecast Centre (NAPDFC) in Carolina developed the UC (University Carolina) model to provide continent-wide internet forecasting support by tracking the geographic presence and future spread of downy mildew pathogen (Main et al. 2001). Using principles and modern methods of related sciences a forecast system, Blue Mold Decision Support System, for downy mildew disease of grapes has been developed that suits Indian conditions (Kotte 2013).

12.4

Conclusion

In different Indian universities, plant pathologists have generated plethora of data on plant disease epidemiology underlining the effects of different variables on the progression of various diseases. Even the multi-location-trial data over the years of a particular crop disease are available. However, these data banks are yet to be transformed into an expert system and to be put in service of the farmers. The awareness, popularity, and interest in modeling, derivation of equations for plant disease forecasting or construction and use of Web-based Expert Systems among plant pathologists in India, unlike the developed countries, are not very encouraging. Apparently, less understanding in mathematics and computer programming of agricultural scientists and lack of appropriate instrumental facilities in most of the state agriculture university (SAUs) and Krishi Vigan Kendra (Extension Centre) are the main impediments. Besides the construction of the forecasting system is a rigorous inter-disciplinary field, employing sophisticated techniques and requiring a good knowledge of its core areas. But it is obvious that without the application of the disease forewarning technology, full advantage of integrated pest management (IPM) strategy could not be achieved.

References Beamount A (1947) Dependence on weather of the diseases outbreak of potato late blight epidemics. Br Mycol Soc Trans 31:45–53 Beresford RM, Spink M (1992) A national disease forecasting system for apple black spot (Venturia inaequalis) in New Zealand. Acta Hortic 313:285–296 Beresford RM (1999) Validation of an ascospore release prediction model for apple blackspot (Venturia inaequalis). Proc 52nd N Z Pl Prot Conf. 148–152 Billing E (1980) Fire blight (Eriwina caratovora) and weather: a comparison of warning systems. Ann Appl Biol 95:365–377

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Blaises P, Gessler C (1989) Development of a forecasting model of grape downy mildew on a microcomputer. IInd Int Symp Comp Modelling Fruit Res Orchard Manag 276:63–70 Chakraborty P, Chakrabarti DK (2006) Expert system for management of malformation disease of mango. ICAR News 12:18 Chakraborty P, Chakrabarti DK (2008) An example of agricultural expert system being used in India. Georgian Electron Sci J Comp Sci Telecommun 1(15):10–12 Chattopadhyay C, Agarwal R, Singh KA, Roy YP, Khan SK, Bhar SA, Chakravarty L, Patel A, Mehata SC (2005) Forecasting of Lipaphis erysimi on oilseed brassica in study—a case study. Crop Prot 24:1042–1053 Devraj, Jain R (2011) Puls expert: an expert system for the diagnosis and control of diseases in pulse crops. Expert Syst Appl 38:11463–11471 Fry WE, Speilman LJ (1991) Population biology of Phytophthora infestans. In: Ingram DJ, Willams PH (eds) Advances in plant pathology: phytophthora infestans, cause of late blight of potatoes, vol 7. Academic Press, London Gilles T (2012) Forecasting down mildew diseases. In: Spencer-Phillip P (ed) Advances in downy mildew research, vol 2. Springer, p 296 Gupta VK (2000) The concept of integrated disease management in apple. In: Proc Int Symp for Sustainable Agri. p. 679–684 Hansen JG, Anderson B, Hermansen A (1995) NegFry—A system for scheduling chemical control of late blight of potatoes. A Decision Support System for Scheduling the Chemical Control of Potato Late Blight, Denmark, pp. 201–208 Jones AL, Lillevik SL, Fisher PD, Stebbins TC (1980) A microcomputer based instrument to predict primary apple scab infection periods. Plant Dis 64:69–72 Kaundal R, Kapoor AS, Raghava GP (2006) Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC Bioinformatics 7:485 Kotte S (2013) Downy mildew of all horticultural crops-grapes, crucifer etc. https://www. slideshare.net/soujanyakotte/downymildewofgrapes–24039831 Krause RA, Massie LB, Hyre RA (1975) Blitecast: a computerized forecast of potato late blight. Plant Dis Rep 63:21–25 Kumar V, Lehri S, Sharma AK, Meena PD, Kumar M (2008) Image based mustard disease expert system- an effective extension tool. Indian Res J Ext Edu 8:10–13 Kushalappa AC, Eskes AB (1989) Coffee rust: epidemiology, resistance and management. CRC Press, Boca Raton, FL, p 360 Kushalappa AC (1990) Development of forecasts: timing fungicide applications to manage coffee rust and carrot blight. Can J Plant Pathol 12:92–99 Lightner, Steiner PW (1992) A computer model for predicting of fire blight disease in apples and pears. Comput Electron Agric 7:249–260 Lucas LH, Rodrigues A, Meira CAA, Bahamonde A (2011) Using non-determinstic learners to alert on coffee rust disease. Expert Syst Appl 38:14276–14283 Luepschen NS, Parker KG, Mills WP (1961) Five-year study of fire blight blossom infection and its control in New York. Cornell Univ Agr Expt Sta Bull 963:1–19 Main CE, Kever T, Holmes GJ, Davis JM (2001) Forecasting long-range transport of downy mildew spores and disease epidemics. https://doi.org/10.1094/APSnetFeature-2001-0501 Mehata KC (1925) Yellow and brown rusts of wheat. A preliminary note on their annual recurrence. Proc 12th Indian Sci Cong. p.191 Miller P, O’Brien M (1957) Prediction of plant disease epidemics. Ann Rev Microbiol 11:77–110 Mills WD (1944) Efficient use of sulfur dusts and sprays during rain to control apple scab. Cornell Ext Bull 630:4 Mills WB (1955) Fire blight development on apple in Western New York. Plant Dis Rep 39:206– 207 Nagrajan S, Joshi LM (1975) A historical account of wheat rust epidemics in India and their significance. Cereal Rust Bull 3:29–33

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Classical Disease Forecasting Systems

Orlandini SL, Massetti L, Marta AD (2008) An agrometeorological approach for simulation of Plasmopara viticola. Comput Electron Agric 64:149–161 Padmanabhan SY, Chakrabarty NK, Row RVSRK (1971) Forecasting and control of rice disease. Proc Indian Natl Sci Acad 37B:423–429 Park EW, Seem RC, Gadowry DM, Pearson RC (1997) DMCAST: a prediction model for grape downy mildew development. Vitic Enol Sci 52:182–189 Park R, Wellings C (2012) Cereal rust situation in early winter. Cereal Rust Rep 10:1–2 Powell D (1965) Factors influencing the severity of fire blight infections in apple and pear. Proc. 94th Ann Meeting Michigan State Hortic Soc. Rajkishore PK, Ranjan K, Sinha AK (2006) Amrapalika: an expert system for the diagnosis of pests, diseases and disorders in Indian mango. Knowl Based Syst 19:9–21 Reddy IVS, Usha K, Singh B, Chander S (2009 Agroecological zonation of Fusarium mangifera for Andhra Pradesh and Uttar Pradesh states of India. ISPRS Archives 38–8/W3 Workshop proceedings: Impact of Climate Change on Agriculture, pp. 68–71 Roach JW, Virkar RS, Weaver MJ, Drake CR (1985) POMME: a computer based consultation system for apple orchard management using prolog. Expert Syst 2:56–69 Rosa MR, Genesio B, Gozzini G, Maracchi and Orlandini S. (1993) PLASMO: a computer based programme for grape vine downy mildew development forecasting. Comput Electron Agric 9: 205–215 Saini HS, Kamal R, Gupta GK (2009) KMSCD: knowledge management system for crop diseases. Nature and Biologically Inspired Computing World Congress, Dec 9–11, pp. 812–817 Saini HS, Kamal R, Sharma AN (2002) Web based fuzzy expert system for integrated management in soybean. Int J Inform Technol 8:54–74 Sharma JN, Gupta VK (1995) Studies on apple scab forecasting in Himachal Pradesh. Indian Phytopath 48:325–330 Singh BP, Govind Krishna PM, Ahmad I (2016) INDO-BLIGHTCAST—a model for forecasting late blight across agroecologies. Int J Pest Manag 62:1–8 Stewart FC (1897) A bacterial disease of sweet corn. N Y Agric Exp Sta Bull 130:422–439 Thomson SV, Schroth M, Moller WJ, Reil WO (1982) A forecasting model for fire blight of pear. Plant Dis 66:576–579 van Everdingen E (1926) The relation between weather conditions and potato late blight Phytophthora infestans. Tijdschr Plantenziekten 32:129–140 Wang H, Ma Z (2011) Prediction of wheat stripe rust based on support vector machine. Conf 7th Int Conf on Natural Computation, pp. 26–28 Wei-chang L, Huailiang C, Wang J (2008) Forecast model for occurrence degree of wheat stripe rust using meteorological data. Agric Sci Technol 9:119–123 Zendaya AH, Merlo A, Pineda A, Ordonez M, Savary S (2006) The intensity of a coffee rust epidemic is dependent on production situations. Ecol Model 197:431–447 Zoller BG, Sisevich J (1979) Blossom populations Eriwina amylovora in pear orchards vs accumulated degree hours over 18.3 Celsius 1972–1976. Phytopathology 69:1050