Trends in Plant Disease Assessment

Description

This edited book provides the readers with the concepts and in-depth knowledge of plant disease assessment and conventional and modern technologies that aid in precise and accurate phytomathometery. This book discusses the evolution of plant disease assessment procedures from the primary visual estimation-based assessment to modern approaches, their practical application for reliable disease quantification, yield loss estimation, and the efficacy of disease control strategies for sustainable crop protection. Significant information is provided on the major aspects of the topic, including remote sensing, imaging techniques, molecular phytopathometery, microarray, and immunotechnology. The book helps plant scientists, plant pathologists, practitioners, researchers, and students in disease quantification, developing predictive models for plant disease epidemics, assessing crop losses, and the magnitude of plant disease control methods. This book describes the classical plant disease assessment methods based on visual observations. It Provides information regarding the modern and emerging technologies in Phytopathometery, precision, and accuracy. This book also discusses the application of disease assessments in predictive models, disease warning systems, expert systems, and decision support systems in applied plant pathology. 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