This book focus on creating popularity and interest in modeling, derivation of equations for plant disease forecasting or construction and use of Web-based Expert Systems among plant pathologists. This book covers descriptions of many historic plant disease epidemics, various forecasting systems and methods of their construction, instruments required for study of plant disease epidemics, widely used commercial forecasting systems and present global scenario of forecasting.
In the human history plant disease epidemics have brought unsurmountable misery many a times. Still breaking out of epidemic in any time in any part of the world is a stark reality. The panic spraying of chemical pesticides is not a panacea. Only the IPM technology may give relief. This technology if backed by the disease forewarning system may yield the desired results. Hence, an in depth understanding of plant disease forecasting system and its successful implementation may bring the global food security.
This title provides a useful background for all students, practitioners, and researchers interested in the field of epidemiology, food security and agriculture sciences.
Author(s): Dilip Kumar Chakrabarti, Prabhat Mittal
Publisher: Springer
Year: 2023
Language: English
Pages: 134
City: Singapore
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
