Trends in Plant Disease Assessment

Preface
Contents
About the Editors
1: Phytopathometry: A Transdisciplinary Concept
1.1 Introduction
1.2 Role of Phytopathometry in Precision Agriculture
1.3 Phytopathometry Tools in the Modern Era
1.4 Phytopathometry Approaches
1.4.1 Visual Disease Assessment
1.4.1.1 Nominal Scales
1.4.1.2 Ordinal Scales
1.4.1.3 Interval Scales
1.4.1.4 Ratio Scales
1.4.1.5 Advantages and Disadvantages of Visual Disease Assessment
1.4.2 Digital Imaging in Phytopathometry
1.4.2.1 Principles of Photography
1.4.2.2 Factors Responsible for Image Quality
1.4.2.3 Pros and Cons of Digital Photography
1.4.3 Android Applications to Quantify Plant Disease Severity
1.4.3.1 Image Processing Software
1.4.4 Statistical Methods in Phytopathometry
1.4.4.1 Regression Analysis
1.4.4.2 Concordance Correlation Coefficient
1.4.4.3 Analysis of Variance and General Linear Modeling
1.4.4.4 Correlation Coefficient
1.5 Hyperspectral Imaging
1.5.1 Spectral Resolution Range of Hyperspectral Sensors
1.5.2 Setup of Hyperspectral Sensors
1.5.3 Pathogenesis and Reflectance Signatures
1.5.4 Potential of Hyperspectral Imaging in Phytopathometry
1.5.4.1 Preprocessing and Data Handling
1.5.4.2 Vegetation Indices: An Important Tool in HSI
1.5.5 Machine Learning
1.5.6 Deep Learning
1.5.7 HSI for Disease Resistance Breeding
1.5.8 HSI in Protected Horticulture
1.6 Challenges in Automated Field Sensing
1.7 Other Indirect Methods of Plant Disease Detection in Phytopathometry
1.8 Conclusion
References
2: Visual Estimation: A Classical Approach for Plant Disease Estimation
2.1 Introduction
2.2 Significance of Precise Quantification of Plant Disease Severity
2.3 Terms and Definitions Regarding Disease Estimation and Quantification
2.4 Visual Assessment of Plant Diseases
2.4.1 Role of Human Eye and Brain in the Visual Estimation of Plant Disease
2.4.2 Factors Inducing Error in the Visual Assessment of Diseases in Plants
2.4.2.1 Variability in Raters´ Disease Assessment Ability
2.4.2.2 Preferences of Disease Ratings for Severity
2.4.2.3 Amount of Lesions and Their Size Give an Overestimation of Disease Severity
2.4.2.4 Structural Characteristics of Host and its Size
2.4.2.5 Time Spent on Estimating Plant Disease
2.4.2.6 Color Blindness
2.4.2.7 Interactions Between Multiple Factors
2.4.3 Methods for the Visual Assessment of Infection Severity in Plants
2.4.3.1 Nominal or Descriptive Scales
2.4.3.2 Ordinal Scales
2.4.3.3 Ratio Scales
2.4.4 Strategies to Enhance the Accuracy of Visual Estimates
2.4.4.1 Computer-Based Training
2.4.4.2 General Field/Lab Training
2.4.4.3 Role of Standard Area Diagrams (SADs) Towards Improved Accuracy
2.4.5 Implementation in Experimentation and Its Future
2.5 Estimation of Plant Diseases by Adopting Image Analysis Technique
2.5.1 Role of Digital Equipment Toward Image Acquisition
2.5.2 Protocols for Analysis of Images and Their Processing
2.5.2.1 Common Software Used for Image Analysis
2.5.2.2 Processing of the Image
2.5.2.3 Validation of Assessment
2.5.3 The Preciseness of Image Analysis
2.5.4 Error Sources Impacting Accuracy
2.5.4.1 Machinist
2.5.4.2 Differences in Infection Signs, Host, and Background
2.5.4.3 Actual Values
2.5.4.4 System Limitations
2.5.5 Prospects of Image Analysis for Plant Disease Estimation
References
3: Remote Sensing: A New Tool for Disease Assessment in Crops
3.1 Introduction
3.2 History
3.3 Remote Sensing Techniques on the Basis of Different Sensors
3.3.1 Imaging Approaches
3.3.1.1 RGB-Imaging
3.3.1.2 Hyperspectral and Multispectral Reflectance Sensors
3.3.1.3 Thermal Sensors
3.3.1.4 Fluorescence Imaging
3.3.2 Non-imaging
3.3.2.1 Vis-NIR Spectroscopy
3.3.2.2 Fluorescence Spectroscopy
3.4 Remote Sensing Features for Plant Diseases and Pest Monitoring
3.4.1 VIS-NIR Spectral Features
3.4.2 Fluorescence and Thermal Parameters
3.4.3 Image-Based and Landscape Features
3.4.4 Features Associated with Habitat Characteristics
3.4.5 Sensitivity Analysis for Selection of Features
3.5 Relevant Areas for Sensors in Plant Disease Detection
3.5.1 Field Systems
3.5.2 Resistance Screening
3.5.3 Assessment of Plant Defense Reactions
3.6 Use of Different Remote Sensing Methods for Different Diseases
3.6.1 RGB Camera
3.6.1.1 Use of Airborne Remote Sensing for Plant Disease Detection
3.6.1.1.1 ADAR System for the Detection and Diagnosis of Rice Sheath Blight Disease
3.6.2 Hyperspectral Imaging
3.6.2.1 Early Detection of Wheat Yellow Rust Disease Caused by Puccinia striiformis by Using Hyperspectral Imaging
3.6.3 Thermography for Plant Disease Detection
3.6.3.1 Thermographic Assessment ofAppleScab Disease
3.6.4 Fluorescence Spectroscopy for Plant Disease Detection
3.6.4.1 Early Detection of the Hypersensitive Reaction to Tobacco Mosaic Virus Using Multicolor Fluorescence Imaging
3.6.5 Multi-temporal Remote Sensing for Plant Disease Detection
3.6.5.1 Use of Multispectral Remote Sensing for Multi-temporal Wheat Disease Detection
3.7 Conclusion
References
4: Image Analysis and Processing Approach: An Automated Plant Disease Recognition Technology
4.1 Basics of Plant Disease Development
4.2 Structural Insight Is Important
4.3 Pathogen Assessment at the Surface
4.4 Explanation of Plant Defense
4.5 Resistant Plant and Disease Severity Concept
4.6 Defense Induction in the Plant
4.7 Callose Production and Image-Based Quantification
4.8 Why Image Analysis Is Important
4.9 Conclusion
References
5: Hyperspectral Imaging Through Spatial and Spectral Sensors for Phytopathometry
5.1 Introduction
5.2 Terminology
5.3 Visual Estimation of Plant Disease Incidence and Severity
5.3.1 From the Observation to Remote Sensing
5.3.2 Quantification of Disease Severity and Importance
5.4 Digital Imaging and Hyperspectral Imaging
5.4.1 Disease Indices in Use
5.4.2 Complete Spectrum-Based Classification
5.4.3 Use of Hyperspectral Data
5.5 Early Detection of Citrus and Solanaceae Plant Diseases Using Remote Sensing
5.5.1 Remote Sensing in Citrus Diseases: Case Study
5.5.2 Remote Sensing in Solanaceae Plant Diseases: Case Study
References
6: Fluorescent Imaging System-Based Plant Phenotyping for Disease Recognition
6.1 Introduction
6.1.1 Plant Phenotyping
6.2 Fluorescent Imaging System and Its Principle
6.3 Florescent Image Processing Techniques
6.3.1 Image Segmentation
6.3.2 Feature Extraction
6.3.3 Analysis of Data
6.4 Application of Fluorescent Imaging
6.5 Disadvantages
6.6 Prospects
References
7: Concept and Application of Infrared Thermography for Plant Disease Measurement
7.1 Introduction
7.2 Background
7.3 Precision Agriculture for Managing Plant Diseases
7.4 Thermography
7.4.1 Principles of Infrared Thermography
7.4.1.1 Passive Infrared Thermography
7.4.1.2 Active Infrared Thermography
7.4.1.2.1 Lock-in Mode Thermography
7.4.1.2.2 Pulse Mode Thermography
7.4.2 Application of Infrared Thermography
7.5 Disease Detection and Measurement
7.5.1 Disease Detection and Measurement in Grapevines
7.5.2 Disease Detection and Measurement in Apple
7.5.3 Disease Detection and Measurement in Rose Plant
7.5.4 Disease Detection and Measurement in Sweet Potatoes
7.5.5 Disease Detection and Measurement in Wheat
7.5.6 Disease Detection and Measurement in Peanut
7.5.7 Disease Detection and Measurement in Oil Palm
7.5.8 Disease Detection and Measurement in Cucumber
7.5.9 Disease Detection and Measurement in Tea Plants
7.6 Conclusion
References
8: Application of Biosensors in Plant Disease Detection
8.1 Introduction
8.2 Plant Disease Detection
8.2.1 Traditional Methods of Plant Disease Detection
8.2.2 Modern Methods of Plant Disease Detection
8.3 What Are Biosensors?
8.3.1 History
8.3.2 Components of Biosensors
8.3.3 Design of Biosensor
8.3.4 Biological Receptor
8.3.5 Transducer
8.3.6 Working of Biosensor
8.4 Characteristics of Biosensors
8.4.1 Sensitivity
8.4.2 Selectivity
8.4.3 Stability
8.4.4 Detection Limit
8.4.5 Reproducibility
8.4.6 Response Time
8.4.7 Range or Linearity
8.5 Classification of Biosensors
8.5.1 Enzyme-Based Biosensors
8.5.2 Antibody-Based Biosensors
8.5.3 Aptamer-Based Biosensors
8.5.4 Whole Cell-Based Biosensors
8.5.5 Nanoparticle-Based Biosensors
8.5.6 Electrochemical Biosensors
8.5.7 Optical Biosensors
8.6 Biosensor-Based Diagnosis of Plant Diseases
8.7 Biological and Technical Limitations in Detection Using Biosensors
8.8 Future Work
8.9 Conclusion
References
9: Immunotechnology for Plant Disease Detection
9.1 Introduction
9.2 Immunotechnology to Detect Plant Diseases
9.2.1 Immunotechnology Based on Serological Methods
9.2.2 Immunotechnology Based on Nucleic Acids
9.2.2.1 Advantages
9.2.2.2 Limitations
9.2.2.2.1 False Positives and Negatives are Brought on by Improper Specificity
9.2.2.2.2 Collection of Samples
9.2.2.2.3 Risks of Contamination
9.2.2.2.4 Inhibition of PCR
9.2.2.2.5 Viability of PCR Positive
9.2.2.2.6 Other
9.3 Conclusion
References
10: Molecular Phytopathometry
10.1 Introduction
10.2 PCR-Based Detection Methods
10.2.1 Conventional Polymerase Chain Reaction
10.2.2 End-point PCR
10.2.3 Bio-PCR
10.2.4 Nested PCR (N-PCR)
10.2.5 Co-operational PCR (Co-PCR)
10.2.6 Multiplex PCR (M-PCR)
10.2.6.1 Multiplex RT-PCR
10.2.6.2 Multiplex Nested PCR
10.2.7 Reverse Transcription PCR (RT-PCR)
10.2.8 Magnetic Capture Hybridization PCR (MCH-PCR)
10.2.9 PCR-ELISA
10.2.10 In situ PCR
10.2.11 PCR-DGGE
10.2.12 Real-Time or Quantitative PCR (qPCR)
10.2.13 Droplet Digital PCR (ddPCR)
10.3 DNA or RNA Probe-Based Methods
10.3.1 Northern Blotting
10.3.2 In Situ Hybridization
10.3.3 Fluorescence In Situ Hybridization
10.4 Post-amplification technique
10.4.1 Macroarray
10.4.2 DNA Microarray
10.5 Isothermal Amplification-Based Methods
10.5.1 Rolling Circle Amplification
10.5.2 Loop-Mediated Isothermal Amplification
10.5.3 Nucleic Acid Sequence-Based Amplification (NASBA)
10.6 RNA Interference
10.7 Next-Generation Sequencing
10.7.1 DNA-Seq-Based Next-Generation Sequencing
10.7.2 RNA-Seq-Based Next-Generation Sequencing
10.8 DNA Barcoding
References
11: Microarray Technology for Detection of Plant Diseases
11.1 Introduction
11.2 Principle and Types of Microarrays
11.2.1 Types of Microarray
11.2.2 DNA Microarrays
11.2.3 Protein Microarrays
11.2.4 Peptide Microarrays
11.3 DNA Microarray Experiment Design and Implementation
11.3.1 Sequence-Specific DNA Probes
11.3.2 Expansion and Application of DNA to Arrays
11.3.3 Target Preparation
11.3.4 Hybridization
11.3.5 Detection of Images
11.3.6 Data Analysis
11.4 Application of Microarrays
11.4.1 Gene Expression Analysis
11.4.2 Applications in Agriculture
11.4.3 Food Quality
11.4.4 Food-Borne Pathogens
11.4.5 Detection of Microorganisms
11.5 Applications of Microarray Technology
11.5.1 Microarray-Aided Microbial Diagnostics
11.5.2 Plant-Pathogen Interactions
11.6 Cellular Perspective in Biological Stress Pathways
11.6.1 Symbiosis and Fungi-Induced Infections
11.6.2 Rust Diseases
11.6.3 Plant Virus-Host Interactions
11.6.4 Maladies caused by Bacteria
11.6.5 Nematode-Induced Changes in Host Plants
11.7 Cataloguing Host Responses
11.8 Advantages and Disadvantages of Microarrays
11.9 Microarray Costs in Comparison
11.10 Future Perspectives
References
12: Predictive Models for Plant Disease Assessment
12.1 Introduction
12.2 Plant Disease Forecasting
12.3 Fundamental Elements of Plant Disease Prediction
12.3.1 Host
12.3.2 Pathogen/Inoculum
12.3.3 Environment
12.4 Requirements for Disease Prediction
12.5 Assessment/Measurement of Disease for Forecasting
12.5.1 Disease Intensity
12.5.2 Disease Incidence
12.5.3 Disease Severity
12.5.4 Disease Prevalence
12.6 Data for Plant Disease Prediction
12.7 Modeling of Plant Disease
12.8 Challenges
12.9 Methods for Disease Prediction
12.9.1 Disease Prediction Based on Inoculum
12.9.2 Disease Prediction Based on Weather Conditions
12.9.3 Disease Prediction based on Comparative Information
12.10 Computer Simulation
12.11 Prediction Scheme and Models
References
13: Extension Plant Pathology
13.1 Phyto Healthcare for Poverty-Stricken Farmers Across the Globe: A Pressing Need Extension Plant Pathology
13.1.1 Extension Plant Pathologists
13.1.2 Need for Extension Phytopathology
13.1.3 Scope of Extension Phytopathology
13.1.4 An Overview of Plant Healthcare in Developed and Developing Countries
13.2 An Insight into Participatory Approaches and Phytopathological Problems of Developing Countries
13.3 Emerging Plant Diseases in Developing Countries
13.4 Participatory Methods
13.4.1 Adopting Participatory Training
13.4.2 Participatory Research
13.5 Impact Assessment
13.6 Technology Espousal: Farmers´ Participation and Training in Pakistan
13.6.1 The Cotton Industry in Pakistan and Constraints Faced by Farmers
13.7 Participatory Action Research
13.7.1 Significant Management Options for CLCuV
13.8 SoTL Projects in Phytopathology
13.8.1 Scholarship of SoTL Projects in Our Classroom
13.8.2 First Example: Productiveness of Various Media and Instructional Methods
13.8.3 Second Example: Effectiveness of Web-Based Assignments
13.8.4 Third Example: Significance of Course Information to Daily Lives of Students
13.8.5 Need for Scholarship of Teaching and Learning
13.9 A Glimpse into Experience of Technology Transfer in Extension
13.9.1 Technology Transfer in Extension
13.9.1.1 Role of Internet
13.9.2 Alterations in the Role of Extension Specialist and Information Flow
13.9.3 Extension Programs in the USA
13.9.4 Privatization of Extension Services in USA
13.10 Diagnostic Networks: A fruitful Tool for Plant Biosecurity
13.10.1 Significance of Plant Health
13.10.2 Constraints to Sustainable Phytohealth
13.10.3 NPDN (The National Plant Diagnostic Network)
13.10.4 Communications Infrastructure and Operations By NPDN
13.10.5 Training and Education Program Developed By NPDN
13.11 An Overview of International Cooperation for Global Plant Biosecurity
13.11.1 Plant Health Clinics and Plant Pathology Training
13.11.2 Training in Crop Protection at UCL
13.11.3 The Plant Clinic Service: A bridge that connects the University and Farmers
13.11.4 The Linkage Between the Phyto Clinic Course and Phyto Clinic Service
13.12 Technology Exchange Between China and Italy for Sustainable Crop and Environment Protection
13.12.1 Significance of Agriculture and Agricultural Research in China
13.12.2 Technology Exchange Between China and Italy in the Discipline of Sustainable Crop Protection
13.12.3 Main Aim of Projects
References
Index