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AI-Driven Predictive Analytics for Crop Health Management

Edited by Sachi Nandan Mohanty, Prity Kumari, Sarita Mohanty, Shu Hu
Copyright: 2026   |   Expected Pub Date:2026/04/30
ISBN: 9781394384457  |  Hardcover  |  
904 pages

One Line Description
By bridging state-of-the-art machine learning with practical, field-proven farming applications, this comprehensive volume gives researchers, professionals, and policymakers the roadmap they need to deploy AI solutions that boost crop yields, cut waste, and secure a sustainable agricultural future.

Audience
AI researchers, environmental engineers, agribusiness professionals, policymakers, and postgraduate students. It bridges academia and industry by presenting rigorous content for both researchers and practitioners looking to innovate in the fields of agricultural and data science.

Description
Agriculture today faces complex challenges driven by climate variability, pest outbreaks, soil degradation, and water scarcity. Feeding a growing population while protecting our environment is a complex challenge. AI applications in agriculture have developed significantly in recent years, moving from experimental models to field-level deployment. Techniques such as convolutional neural networks, decision tree-based models and ensemble forecasting methods are being used to manage crop health, predict outcomes and optimize inputs. These technologies are reshaping farm decision-making, promoting efficiency, reducing losses and supporting environmental sustainability. This book captures this evolution, contextualizing the role of AI within the broader agri-tech industry. Divided into three comprehensive sections, it introduces the fundamental principles of AI and machine learning, highlights practical applications that demonstrate how these technologies are implemented on the ground, and provides real-world case studies from diverse farming systems, offering insights into challenges, outcomes and lessons learned from field-level adoption of AI tools. Bringing together experts from agriculture, computer science, and environmental science makes this volume an invaluable reference for researchers, professionals, and policymakers aiming to integrate AI into sustainable crop health management practices.
Readers will find the volume:
• Focuses on AI-driven approaches to crop disease detection, yield prediction, and pest control;
• Combines theoretical knowledge with applied case studies across real agricultural systems;
• Integrates AI with IoT, robotics, and environmental sensing technologies;
• Suitable for readers from both technical and non-technical agricultural backgrounds.

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Author / Editor Details
Sachi Nandan Mohanty, PhD is an Associate Professor in the School of Computer Science and Engineering at the Vellore Institute of Technology, Andhra Pradesh, India. He has published 42 books and more than 120 articles in international journals of repute. His research interests include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence.

Prity Kumari, PhD is an Assistant Professor in the College of Horticulture at Anand Agricultural University, Gujarat, India. She has published several research papers in reputable international journals and conferences, as well as several books and book chapters that reflect her academic credit. Her research spans time series analysis, cutting-edge deep learning AI techniques, image analysis, and applications of AI in agriculture.

Sarita Mohanty, PhD is an Assistant Professor in the Department of Master in Computer Application in the Centre for Post Graduate Study at the Odisha University of Agriculture and Technology, India, with more than ten years of experience. She has authored and edited several books. Her research interests include digital forensics and cybersecurity.

Shu Hu, PhD is an Assistant Professor in the Department of Computer and Information Technology and the Director of the Purdue Machine Learning and Media Forensics Lab at Purdue University, USA. He has more than 100 publications to his credit, including numerous articles in international journals and conferences of repute. His research focuses on machine learning, media forensics, and computer vision.

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Table of Contents
Preface
Part 1: General AI and ML in Agriculture – Reviews, Overviews, and Conceptual Studies
1. Introduction to AI and Machine Learning in Agriculture
Divya Agarwal, Sneh J. Devra and Raj Siddharajsinh R.
1.1 Introduction
1.2 Artificial Intelligence and Machine Learning
1.3 Applications of AI and ML in Agriculture
1.3.1 Soil Management
1.3.2 Water Management
1.3.3 Crop Health Management
1.3.3.1 Crop Disease Identification
1.3.3.2 Pest Identification
1.3.3.3 Weed Detection and Management
1.3.4 Quality Assessment
1.3.5 Livestock Management
1.3.6 Crop Yield Prediction
1.3.7 Weather Forecasting
1.4 Framework for Working Methodologies of AI and ML in Agriculture
1.5 Advantages and Challenges of Using AI and ML in Agriculture
1.6 Conclusion
References
2. A Review on Artificial Intelligence of Things in Agriculture: Challenges, Benefits, and Future Perspective
Olakunle Elijah, Mu’azu Jibrin Musa, Yahaya Otuoze Salihu and Ahmed Eltayeb
2.1 Introduction
2.2 AIoT Concept in Agriculture
2.2.1 IoT Devices
2.2.2 Data Collection and Transmission
2.2.3 Data Analytics and AI
2.2.4 Decision-Making and Automation
2.3 Review of Related Works
2.4 Challenges of AIoT Adoption
2.4.1 Security and Privacy
2.4.2 Interoperability
2.4.3 Ethical Consideration
2.4.4 Lack of Adequate Infrastructure
2.5 Benefits of AIoT Adoption
2.5.1 Enhanced Precision Farming
2.5.2 Improved Crop Yield
2.5.3 Real-Time Monitoring and Decision-Making
2.5.4 Reduced Labor Costs
2.5.5 Sustainable Farming Practices
2.5.6 Livestock Health and Management
2.5.7 Predictive Maintenance
2.5.8 Climate Adaptation
2.5.9 Enhanced Market Access and Supply Chain Efficiency
2.5.10 Pest and Disease Management
2.6 Future Works
2.7 Conclusion
Acknowledgement
References
3. Artificial Intelligence (AI) and Its Significance in Smart Agriculture: A Comprehensive Study
Sushree Bibhuprada B. Priyadarshini, Sachi Nandan Mohanty,
Sucheta Panda and Debabrata Singh
3.1 Introduction
3.2 AI and Smart Sustainable Agriculture
3.3 AI and Major Applications
3.3.1 Irrigation Management
3.3.2 Weather Forecasting
3.3.3 Plant Growth and Yield Administration
3.4 Usage of AI in Modern Agriculture and Open Research Challenges
3.5 Conclusion
References
4. SMART Crop Analytics: Revolutionizing Agriculture through Data Analytics
Deepak Gupta and Satyasundara Mahapatra
4.1 Introduction
4.1.1 Traditional Farming vs. Modern Data-Driven Approaches
4.1.2 Agriculture 4.0: Rising Need of Technology in Agriculture
4.1.3 Key Digital Technologies Transforming Agriculture
4.1.3.1 Precision Agriculture
4.1.3.2 Internet of Things (IoT) and Sensors
4.1.3.3 Big Data Analytics and AI
4.2 Analytics in Agriculture: Enhancing Crop Health Monitoring across Four Stages
4.2.1 Descriptive Analytics
4.2.2 Diagnostic Analytics
4.2.3 Predictive Analytics
4.2.4 Prescriptive Analytics
4.2.5 Data Flow and Analytics Pipeline
4.3 Building Blocks Data-Driven Agriculture
4.3.1 The Data Ecosystem
4.3.2 Data Challenges and Solutions
4.3.3 From Raw Data to Actionable Insights
4.3.3.1 Data Preprocessing
4.3.3.2 Analytics Models
4.3.3.3 Insight Generation
4.4 From Insights to Action: Implementing Agricultural Intelligence
4.4.1 Decision Support Systems
4.4.1.1 Alert Generation
4.4.1.2 Risk Assessment
4.4.1.3 Treatment Recommendation
4.4.2 Resource Optimization
4.4.3 Intervention Timing
4.4.4 Cost-Benefit Analysis
4.5 Revolutionizing Crop Health with Data-Driven Analytics
4.6 Impact of Data-Driven Agriculture on Global Food Security
4.7 Challenges in Implementing Predictive Analytics in Crop
Health
4.7.1 Data-Related Issues
4.7.2 Adoption Barriers
4.7.3 Technological and Environmental Limitations
4.8 Conclusion
References
5. Smart IoT in Agri and Food Industry: Use of Sensors and
Automation in Refrigerators for Preservation of Fresh Produce
D. Ramesh Babu, K. V. Narasimha Rao and Usha Desai
5.1 Introduction
5.2 AI-ML Integration in Refrigeration Technology
5.3 Specialized Sensors for Fruit and Vegetable Preservation
5.3.1 Gas Techniques in Controlled Atmosphere Refrigeration
5.3.2 Enhanced Food Safety Measures
5.3.3 Integration with Online Grocery Platforms
5.3.4 Privacy and Security Considerations
5.4 Humidity Sensors
5.5 Temperature Sensors
5.6 UV-C Light Sensors
5.7 Weight Sensors
5.8 Oxygen Sensors
5.9 CO2 Sensors
5.10 Positioning Sensors
5.11 AI-ML in Food Spoilage Prevention
5.12 Ehylene Sensors
5.13 Fruit Ripening Detection Sensors
5.13.1 How the Sensors Work
5.13.2 Benefits of Ripening Detection Sensors
5.13.3 Challenges and Future Developments
5.14 Case Study Survey Results on Traditional Refrigerator Usage
5.14.1 Introduction
5.14.2 Review of Literature
5.14.3 Materials and Methods
5.14.4 Results and Discussion
5.15 Conclusion
5.16 Recommendations
References
6. Artificial Intelligence Application for Sustainable
Agricultural Agreements
Wasswa Shafik, Ali Tufail, Rosyzie Anna Awg Haji Mohd Apong
and Chandratilak De Silva Liyanage
6.1 Introduction
6.1.1 Background and Rationale
6.1.2 Research Objectives
6.1.3 Scope and Limitations
6.2 Sustainable Agriculture and Agreements
6.2.1 Principles of Sustainable Agriculture
6.2.2 Importance of Crop Health Monitoring
6.2.3 Impact of Crop Diseases on Agriculture
6.3 Traditional Methods vs. AI in Crop Health Monitoring
6.3.1 Challenges of Traditional Methods
6.4 Artificial Intelligence Techniques for Crop Health Monitoring
6.4.1 Machine Learning Algorithms
6.4.2 Benefits and Challenges of AI in Agriculture
6.5 Remote Sensing and IoT in Crop Health Monitoring
6.5.1 Role of Remote Sensing
6.6 Data Collection and Analysis in Crop Health Monitoring
6.6.1 Big Data in Agriculture
6.7 Applications of AI in Crop Health Agreements
6.7.1 Precision Agriculture
6.8 Regulatory and Ethical Considerations
6.8.1 Data Privacy and Security
6.9 Future Trends and Innovations
6.9.1 Integration of AI with Robotics
6.9.2 Summary of Key Findings
6.10 Conclusion
References
7. Application of AI in Agriculture
Raj S. R., Divya Agarwal and Devra S. J.
Introduction
Importance of Agriculture in the Global Economy
Definition of AI
Definition of AI in Agriculture
Application of AI in Agriculture
Detection of Plant Diseases, Pests, and Nutrient Deficiencies
Weed Detection and Management
Irrigation and Water Management
Soil Health Monitoring
Crop and Grain Quality Assessment
Livestock Management
Crop Yield Prediction
Bibliography
8. Application of AI in Agriculture: A Critique through the Lens of Practicality
Ananya Pandey and Jipson Joseph
8.1 Introduction
8.2 Applications of AI in Agriculture
8.2.1 Precision Agriculture
8.2.1.1 Management and Monitoring of Crops
8.2.1.2 Analyzing Soil Health
8.2.2 Autonomous Machinery
8.2.2.1 Drones and Robotics
8.2.2.2 Intelligent Sprayers (Automated Irrigation System)
8.2.3 Data Analytics
8.2.3.1 Predictive Analytics
8.2.3.2 Supply Chain Optimization
8.3 Assessing Practicality of AI Application in Agriculture
8.3.1 Cost Effectiveness
8.3.2 Accessibility and Ease of Use
8.3.3 Integration with Existing Practices
8.4 Ethical and Social Considerations
8.4.1 Impact on Labor and Employment
8.4.2 Data Privacy and Ownership
8.4.3 Long-Term Sustainability and Environment Concerns
8.5 Future Scope
8.6 Conclusion and Suggestions
References
9. Leveraging AI for Agricultural Advancements
Mahender Singh, Manisha Mittal, Naman Shrivastava, Hazel Kapoor, Sanvi Aswal and Purva Jain
9.1 Introduction
9.2 Soil Management
9.2.1 DSS (The Decision Support System)
9.2.2 Fuzzy Logic System
9.2.3 ANN
9.3 Crop Data Management
9.3.1 Data-Driven Management for Advanced Farming: Principal Stages
9.3.2 Platforms Supporting Sensors
9.3.3 Aircraft Systems
9.3.4 Data Management for Different Types of Crops
9.3.5 Maps Containing Relevant Field Features
9.3.6 Decision-Making
9.4 Irrigation Water Management
9.5 Pest and Disease Management in Agricultural Production with Artificial Intelligence
9.5.1 Development and Application of Image Recognition Technology
9.5.2 Applications of Data Analysis and Machine Learning in Pest and Disease Prediction
9.5.3 The Role of Intelligent Decision Support Systems in Pest and Disease Management (PARA)
9.5.4 The Role of Intelligent Agricultural Robots in Pest and Disease Control
9.6 AI and IoT in Crop Management
9.6.1 Knowledge Gaps
9.6.2 Future Scope
9.7 The Prospect of Artificial Intelligence (AI) in Precision
Agriculture
9.7.1 Irrigation with Plant Health Monitoring
9.7.2 Yield Prediction and Weed Detection
9.7.3 Maximize the Output
9.8 Safety of Automated Agricultural Machineries
9.8.1 Environmental Perception
9.8.1.1 Single Perception Sensor
9.8.1.2 Multiple Perception Sensors
9.8.1.3 Datasets and Algorithm
9.8.2 Risk and Mitigation
9.8.3 Human Factors and Ergonomics
9.8.4 Internet of Things
9.8.5 Robotics in Agriculture
Bibliography
10. The Role of AI and ML in Smart Farming and Crop Management
Preethi Nanjundan, Lijo Thomas, Akhand Tiwari and Sumit Arun Hirve
10.1 Overview of AI and Machine Learning
10.1.1 Understanding Artificial Intelligence (AI)
10.1.2 Fundamentals of Machine Learning (ML)
10.1.3 Exploring the Intersections of AI, ML, and Data Science
10.1.4 Historical Evolution of AI in Agriculture
10.2 Importance of AI and ML in Agriculture
10.2.1 Challenges in Traditional Agriculture
10.2.2 The Impact of AI and Machine Learning on Modern Agriculture
10.2.3 Benefits of AI Adoption in Agriculture
10.3 Applications of AI and ML in Agriculture
10.3.1 Precision Agriculture
10.3.2 Crop Monitoring and Management
10.3.3 Pest and Disease Prediction
10.3.4 Weather Forecasting and Its Impact on Farming
10.3.5 Irrigation and Water Resource Management
10.3.6 Supply Chain and Market Analysis
10.4 Technologies Enabling AI and ML in Agriculture
10.4.1 IoT and Sensor Integration in Agriculture
10.4.2 Satellite Imaging and Drones for Data Collection
10.4.3 Big Data Analytics in Agriculture
10.4.4 Robotics and Automation in Farming
10.5 Machine Learning Techniques in Agriculture
10.5.1 Supervised Learning: Applications in Yield Prediction
10.5.2 Unsupervised Learning: Insights from Soil Data
10.5.3 Reinforcement Learning: Autonomous Machines in Farming
10.5.4 Neural Networks for Image and Pattern Recognition
10.6 Case Studies and Success Stories
10.6.1 Real-World Examples of AI Implementation in Agriculture
10.6.2 Startups and Innovations in Agri-Tech
10.7 Challenges and Limitations
10.7.1 High Costs and Accessibility Barriers
10.7.2 Ethical and Environmental Considerations
10.7.3 Resistance to Technology Adoption
10.8 Future Trends and Opportunities
10.8.1 AI in Sustainable Agriculture
10.8.2 Integration of Blockchain with AI in Farming
10.8.3 Role of Governments and Policymaking in AI Adoption
10.9 Conclusion
Bibliography
Part 2: Crop Disease Detection and Plant Health Monitoring – AI/ML/DL Models
11. Potato Leaf Disease Detection Using an Optimized Transfer
Learning Approach
Ambarish G.V.S., Ritik Goyal, Sweeti Sah and Shweta Sharma
11.1 Introduction
11.2 Literature Review
11.3 Methodology
11.4 Experimental Results
11.5 Conclusion
11.6 Future Scope
References
12. A Novel Approach for Sugarcane Disease Detection Using
Enhanced CNN with Gabor Filters
Ambarish G.V.S., Sweeti Sah, Shweta Sharma and Ritik Goyal
12.1 Introduction
12.2 Literature Review
12.3 Overview of Deep Learning Techniques in Sugarcane Disease Prediction
12.3.1 Traditional Image Processing Techniques
12.3.2 Deep Learning Models
12.3.2.1 Convolutional Neural Networks (CNNs)
12.3.2.2 VGG16 and VGG19
12.3.2.3 Residual Networks (ResNet)
12.3.2.4 You Only Look Once (YOLO)
12.3.2.5 Integrating Multiple Deep Learning Architectures
12.4 Proposed Approach
12.4.1 Data Source
12.4.2 Dataset Description
12.4.3 Data Preprocessing
12.4.4 Dataset Splitting
12.4.5 Model Architecture
12.5 Experimental Results
12.5.1 Performance Metrics of Model 1 (Using CNN without Integration with Gabor Filters)
12.5.2 Performance Metrics of Model 2 (Using Pre-Trained VGG16 Model)
12.5.3 Performance Metrics of Model 3 (CNN Integrated with Gabor Filters)
12.6 Comparison with Existing Literature
12.7 Conclusions and Future Work
Acknowledgments
References
13. Transfer Learning-Based Tomato Plant Disease Classification Using Advanced Deep Neural Networks
Thota Rishik Sai Santhosh, Aditya Nitin Patil and Tauseef Khan
13.1 Introduction
13.2 Literature Review
13.3 Proposed Methodology
13.3.1 Dataset Preparation and Class Distribution
13.3.2 Employed Deep Networks
13.3.2.1 DenseNet-201
13.3.2.2 Vision Transformer
13.3.2.3 Swin Transformer
13.4 Experiment Results and Analysis
13.4.1 Implementation Details
13.4.2 Experimental Findings
13.4.3 Comparative Analysis
13.4.3.1 Performance Comparison with Existing Works
13.4.3.2 Ablation Study
13.5 Analytical Discussion
13.6 Conclusion
References
14. Evaluating CNN Architectures for Optimal Deployment in Plant Disease Detection Systems
Nivethitha V., S. Rohith, Miruthula S. K. and Sanjay J.
14.1 Introduction
14.2 Literature Survey
14.3 Methodology
14.4 System Architecture for Plant Disease Detection Using VGG19 and Edge Computing
14.5 Comparative Analysis
14.6 Conclusion
14.7 Future Work and Considerations
Bibliography
15. Deep Learning-Based Early Detection and Classification of Tomato Leaf Diseases Using Data Augmentation Techniques
Rasmita Kumari Mohanty, Satya Prakash Sahoo, Manas Ranjan Kabat, Sachi Nandan Mohanty and Basim Alhadidi
15.1 Introduction
15.1.1 Scope
15.1.2 Problem Statement
15.1.3 Objective
15.2 Literature Survey
15.2.1 Comparison with Existing System
15.3 Software Requirement Analysis
15.3.1 Functional Requirements
15.3.2 Non-Functional Requirements
15.4 Proposed System
15.4.1 CNN Architecture
15.4.2 VGG16 Architecture
15.4.3 Modules Implemented
15.5 Data Preprocessing
15.6 Splitting of the Data
15.7 Testing Levels
15.7.1 Non-Functional Testing
15.7.1.1 Performance Testing
15.7.1.2 Stress Testing
15.7.1.3 Security Testing
15.7.1.4 Portability Testing
15.7.1.5 Usability Testing
15.7.2 Functional Testing
15.7.2.1 Integration Testing
15.7.2.2 Regression Testing
15.7.2.3 Unit Testing
15.7.2.4 Alpha Testing
15.7.2.5 Beta Testing
15.8 Results
15.9 Conclusion and Further Work
References
16. Artificial Intelligence-Based Image Processing for Plant Disease Detection and Diagnosis
Nihala Asees C K
16.1 Introduction
16.2 Artificial Intelligence (AI): Revolutionizing Agriculture
16.2.1 Artificial Intelligence (AI) in Disease Detection and Diagnosis
16.2.2 Machine Learning (ML) and Deep Learning (DL) in Automated Disease Diagnosis
16.3 Automated Plant Disease Identification: Key Procedures
16.3.1 Image Acquisition
16.3.2 Image Pre-Processing
16.3.3 Image Segmentation
16.3.4 Feature Extraction
16.3.5 Classification and Disease Identification
16.4 Examples of Common Classification Algorithms
16.5 Challenges and Future Thrusts in AI-Based Image Processing for Disease Detection
16.6 Conclusion
References
17. Efficient Rice Leaf Disease Detection Based on Convolutional Neural Networks
Gouranga Mandal, Nageswara Sastry, Nihal Ahmed and Kashyap Jonnabathula
17.1 Introduction
17.2 Literature Review
17.3 Proposed Methodology
17.3.1 Dataset Description
17.3.2 Data Preprocessing
17.3.3 Deep Learning Methods for Classification
17.4 Experimental Results
17.4.1 Confusion Matrix
17.4.2 Accuracy Graph
17.5 Conclusion and Future Scope
References
18. Machine Learning Models for Crop Disease Detection
Devra S. J., Raj S. R. and Divya Agarwal
Introduction
18.1 Overview of Crop Diseases
18.1.1 Types of Crop Diseases
18.2 Machine Learning Basics
18.3 Key Machine Learning Models for Disease Detection
18.3.1 Convolutional Neural Networks (CNNs)
18.3.2 Support Vector Machines (SVM)
18.3.3 Random Forests
18.3.4 Transfer Learning
18.4 Data Sources and Challenges
18.4.1 Data Sources
18.4.2 Challenges
18.5 Future Directions
Conclusion
References
19. Deep Learning Model for Disease Diagnosis and Classification in Lemon Leaves
Battula Bhavya and Sampath A K
19.1 Introduction
19.2 Literature Survey
19.3 Proposed Work
19.3.1 Named Dataset
19.3.2 Segmentation Process
19.3.3 Feature Extraction
19.3.4 Classification
19.4 Conclusion
19.5 Future Work
References
20. LeafNet: A CNN and VGG16-Based Crop Disease Classification Framework with Web Integration
Shweta Sharma, Sweeti Sah, Rakesh Kumar, Kamaldeep and Manisha Malik
20.1 Introduction
20.2 Related Work
20.3 Dataset Collection
20.4 Proposed LeafNet Architecture
20.4.1 Interactive Demonstrations with Web Interface
20.5 Experimental Setup and Results
20.6 Conclusion and Future Work
References
21. Crop Disease Detection Using AI
Preethi Nanjundan, Arushi Sharma and Lijo Thomas
21.1 Introduction
21.2 Current State of Agriculture and Crop Diseases in India
21.3 Overview of Artificial Intelligence in Agriculture
21.4 AI-Based Techniques for Crop Disease Detection
21.4.1 Computer Vision and Image Processing
21.4.2 Machine Learning and Predictive Analytics
21.4.3 Internet of Things (IoT) and Remote Sensing
21.5 Advantages of AI in Crop Disease Detection
21.5.1 Real-Time and Early Detection of Diseases
21.5.2 Reduction in Crop Losses and Improved Yields
21.5.3 Cost Efficiency for Farmers Through Targeted Interventions
21.5.4 Environmental Benefits: Minimization of Pesticide Overuse
21.6 Challenges in Implementing AI for Crop Disease Detection in India
21.6.1 Infrastructure Challenges: Limited Internet and Tech Access in Rural Areas
21.6.2 Data-Related Issues: Lack of Quality Datasets and Localized Information
21.6.3 Affordability and Accessibility of AI Tools for Smallholder Farmers
21.6.4 Need for Farmer Training and Awareness
21.6.5 Regulatory and Policy Considerations for AI Integration
21.7 Case Studies and Success Stories
21.7.1 Examples of AI Applications in Indian Agriculture for Disease Detection
21.7.1.1 eNAM Platform Integration with AI for Crop Health
21.7.1.2 Plantix Mobile App
21.7.1.3 Mahindra’s Krish-e Platform Krish-e
21.7.1.4 Next Technologies
21.7.2 Success Stories of Farmers Using AI-Powered Tools
21.7.2.1 Improving Rice Yields in Andhra Pradesh
21.7.2.2 Empowering Cotton Farmers in Maharashtra
21.7.2.3 Sustainable Farming in Punjab Using Drones
21.7.2.4 Success of Tomato Farmers in Karnataka
21.8 Future Prospects and Opportunities
21.8.1 Potential of Emerging AI Technologies
21.8.2 Role of Public-Private Partnerships
21.8.3 Policy Recommendations for Fostering AI Adoption in Rural India
21.8.4 Integration of AI with Other Technologies
21.9 Conclusion
Bibliography
22. Advancing Cucurbita pepo Leaf Disease Diagnosis with the YOLO Algorithm
S. Pandiarajan, Jaya Harish R., Mohankumar R. and Pradeesh H.
22.1 Introduction
YOLO Model Version 8
22.2 Literature Survey
22.3 Proposed System
22.4 Results and Discussion
22.5 Conclusion
22.6 Future Enhancement
References
23. A Deep Learning-Based Multi-Level Featuring Model Detecting Maize Leaf Diseases in the Early Stages
Ganesh B. Kote, Sweta Jha, Pawan R. Bhaladhare and Purushottam R. Patil
23.1 Introduction
23.2 Literature Review
23.3 Proposed System
23.3.1 Basic Steps in the System
23.3.2 Image Augmentation
23.3.3 Other Image Augmentation Tasks in the System
23.3.4 Image Reflection
23.3.5 Image Enlargement
23.3.6 Shearing of an Image
23.3.7 Application of Classifier
23.3.8 Convolutional Neural Network (CNN)
23.3.9 Steps in the Proposed System
23.4 Conclusion
23.5 Acknowledgment
References
24. Capsicum Leaf Disease Detection Through Swarm Optimization Methods
Prashant B. Vikhe and Baisa L. Gunjal
24.1 Introduction
24.2 Related Work
24.3 Dataset
24.4 Methodology
24.5 Comparison with Existing System
24.6 Conclusion
Bibliography
25. Types of Plant Leaf Disease Detection Techniques Using
Machine Learning Classification Algorithms: A Comprehensive Review
Shivakumar Nethani and Pramod Kumar P.
25.1 Introduction
25.1.1 Background
25.1.2 Importance of Machine Learning
25.1.3 Objectives
25.1.4 Problem Statement
25.2 Literature Review
25.2.1 Common Plant Diseases
25.2.1.1 Blight
25.2.1.2 Rust
25.2.1.3 Mildew
25.2.1.4 Leaf Spot
25.2.1.5 Anthracnose
25.2.2 Symptoms and Impacts
25.2.2.1 Discoloration and Lesions
25.2.2.2 Wilting and Defoliation
25.2.2.3 Impact on Agricultural Productivity
25.3 Traditional Methods of Plant Leaf Disease Detection
25.3.1 Visual Inspection
25.3.2 Chemical Analysis
25.3.3 Early Computer-Based Image Processing Techniques
25.3.4 Limitations of Traditional Methods
25.4 Proposed Machine Learning Classification Algorithms in Plant Leaf Disease Detection
25.4.1 Feature Extraction Techniques
25.4.1.1 Color-Based Features
25.4.1.2 Texture-Based Features
25.4.1.3 Shape-Based Features
25.4.2 Machine Learning Classification Algorithms
25.4.2.1 Support Vector Machines (SVM)
25.4.2.2 Decision Trees and Random Forest
25.4.2.3 K-Nearest Neighbors (KNN)
25.4.2.4 Naive Bayes
25.4.2.5 Neural Networks
25.4.2.6 Deep Learning Models
25.4.2.7 Ensemble Methods
25.4.3 Comparative Analysis of Machine Learning Algorithms
25.4.4 Challenges and Future Directions
25.4.4.1 Data Availability and Quality
25.4.4.2 Interpretability and Explainability
25.4.4.3 Real-World Deployment
25.5 Comparative Analysis of Different Techniques
25.5.1 Accuracy and Performance
25.5.2 Computational Efficiency
25.5.3 Robustness and Generalization
25.5.4 Interpretability and Usability
25.5.5 Suitability for Real-World Applications
25.6 Comparative Analysis of Different Techniques
25.7 Emerging Trends and Future Directions
25.7.1 Integration of Deep Learning with IoT and Edge Computing
25.7.2 Transfer Learning and Domain Adaptation
25.7.3 Explainable Artificial Intelligence and Model Interpretability
25.7.4 Multimodal Data Fusion
25.7.5 Future Directions in Plant Leaf Disease Detection
25.8 Conclusion
References
26. Crop Recommendation System Using Deep Learning
Gokulnath B.V., Neha Chowdhary Chandra, Achyuth Kumar Debbe, Sai Mokshitha Thota and S.P. Siddique Ibrahim
26.1 Introduction
26.2 Literature Survey
26.3 Proposed Methodology
26.4 Conclusion
26.5 Future Work
References
Part 3: Smart Agriculture Systems – IoT, Sensors, Robotics, and Precision Farming
27. IoT-Based Soil Nutrition Water Management and Detection of Plant Diseases for Smart Agriculture
Gulabdeep Brar, Jayoti Bansal and Amandeep Singh
27.1 Introduction
27.2 Related Survey
27.2.1 Comparison of Survey
27.3 Algorithm Suitability for Plant Disease Detection
27.3.1 Random Forest Algorithm
27.3.2 Support Vector Machine and K-Nearest Neighbor Algorithm Approach
27.3.3 K-Nearest Neighbor (KNN)
27.3.4 CNN (Convolutional Neural Network)
27.3.5 Comparison of All Algorithms
27.3.6 Potential Applications
27.4 Challenges and Recommendations
27.5 Conclusion
Bibliography
28. Multihead Cross Attention–Bi-LSTM-Based Crop Forecast
Based on Soil Fertility and Weather Prediction
Harsharani Kote and S.P. Siddique Ibrahim
28.1 Introduction
28.1.1 Manuscript Organization
28.2 Literature Survey
28.2.1 Problem Statement
28.3 Proposed Methodology
28.3.1 Data Collection
28.3.2 Preprocessing
28.3.3 Feature Selection
28.3.3.1 Hippopotamus Optimization Algorithm
28.3.3.2 Lotus Effect Optimization Algorithm
28.3.4 MCA–Bi-LSTM Network
28.3.4.1 Multi-Head Attention Layer
28.3.4.2 Snow Geese Algorithm (SGA)
28.4 Results and Discussion
28.4.1 Dataset Description
28.4.2 Experimental Outcomes
28.5 Conclusion
References
29. Real-Time Dataset Prediction with RNNs and FPGA Integration for Crops and Fruits Cultivation
Jagannatha K. B., Amogh C. Dixit, Sabina Rahaman and Usha Desai
29.1 Introduction
29.2 Proposed System
29.3 Results
29.4 Conclusion
Bibliography
30. Pest Control Using Predictive Analytics and Robotics
Preethi Nanjundan, Lijo Thomas, Indu P.V. and Sumit Arun Hirve
Introduction
30.1 Importance of Pest Control in Agriculture and Challenges
in Pest Control
30.1.1 Role of Predictive Analytics and Robotics
30.1.2 Limitations of Traditional Methods
30.1.3 Environmental and Economic Impacts
30.2 AI and Robotics in Pest Management
30.2.1 Predictive Analytics for Pest Forecasting
30.2.2 Drones and Robots for Detection and Control
30.2.3 Benefits and Applications
30.3 Future Directions and Case Studies
30.3.1 Real-World Examples
30.3.2 Emerging Trends
Conclusion
References
31. Smart Irrigation System of Recommendation for Efficient
Crop Yielding
Prajna Paramita Debata, Bidush Kumar Sahoo, Satyajit Pujapanda, Sriya Mishra and Smruti Rekha Sahoo
31.1 Introduction
31.2 Literature Review
31.3 Proposed Model
31.4 Discussion
31.5 Conclusion
References
32. AI in Soil and Water Management
Preethi Nanjundan, Lijo Thomas, Indu P.V. and Sumit Arun Hirve
Introduction
32.1 Overview of AI in Soil and Water Management
32.1.1 Importance of AI in Modern Agricultural Practices
32.1.2 Applications of AI in Soil and Water Monitoring
32.2 AI for Soil Health and Fertility
32.2.1 AI-Based Soil Quality Assessment
32.2.2 Predictive Models for Soil Fertility and Crop Yield
32.3 AI for Water Resource Management
32.3.1 AI for Efficient Irrigation Systems
32.3.2 Monitoring and Predicting Water Availability
32.4 Challenges and Future Directions in AI for Soil and Water
Management
32.5 Conclusion
Bibliography
33. IoT-Based Smart Agriculture System
Shriraj Nandkishor Pardeshi, Anushka Reddy, Linda Shalini Y., Riya Ramachandran, Nandini Sirohi and Virendra Kumar Shrivastava
33.1 Introduction
33.2 Literature Survey
33.3 Methodology
33.4 Results and Discussion
33.5 Conclusions
References
34. Quantum-Enhanced Automated Crop Health Monitoring Using Computer Vision with AWS SageMaker and AWS Braket for Precision Agriculture
Sandeep Chouhan, Ramandeep Sandhu, Om Prakash Yadav and Sardar M. N. Islam (Naz)
34.1 Introduction
34.2 Related Works
34.3 Proposed Methodology
34.3.1 Quantum-Assisted Image Segmentation
34.3.2 Quantum Feature Extraction with QSVM
34.3.3 Classification with AWS SageMaker
34.3.4 Farmer-Facing Dashboard
34.3.5 Hybrid Anomaly Detection
34.3.6 Automated Intervention Recommendations
34.3.7 Performance Benchmarking
34.3.8 Real-Time Data Integration and Visualization
34.3.9 Scalability and Adaptability
34.4 Conclusion
References
35. Evaluating Soil Fertility and Health: Technological
Innovations and Organic Fertilizer Applications
Vatsala G. A., Guruprasad B. K., Chetan, Brunda T. J. and Deekshitha P.
35.1 Introduction
35.2 Literature Review
35.3 Gap Analysis
35.4 Proposed Method
35.5 Formulas
35.6 Flow Chart
35.7 Conclusion
References
36. Automated Crop Protection System Using IoT for Rain, Hail, and Fog
Tonnema Sarkar Chity, Faisal Imran, Md. Sazzad Hossen, Md. Yaqub Arafat and Farhana Tania
36.1 Introduction
36.2 Literature Review
36.3 Proposed Methodology
36.3.1 The Main Component Used in the System
36.3.2 System Control Algorithm
36.3.3 System Workflow
36.3.4 System Architecture
36.3.5 Working Procedure
36.4 Result
36.5 Conclusion
References
37. Enhancing Agricultural Pest Management by Leveraging
ResNet50 for High-Accuracy Insect Classification
Bhanu Prakash Pandey and Kumar Gaurav
37.1 Introduction
37.2 Literature Survey
37.3 Methodology
37.3.1 Dataset Preparation
37.3.2 Pretrained Neural Network and Transfer Learning
37.3.3 Model Architecture and Training
37.4 Implementation and Results
37.4.1 Loading the Pre-Trained ResNet50 Model
37.4.2 Adding Custom Layers
37.4.3 Model Development
37.4.4 Training the Model
37.4.5 Testing the Model
37.4.6 Fine-Tuning and Inference
37.5 Conclusion and Future Work
References
Part 4: Environmental & Resource Monitoring – Remote Sensing, Forecasting, and Ecology
38. EcoScan: A Deep Learning Based Framework for Identification of Deforestation from Satellite Imagery
Kancharla Narendranath Reddy, Maddela Preethi, Aditya Patil and Tauseef Khan
38.1 Introduction
38.2 Literature Work
38.3 Proposed Methodology
38.3.1 Preparation of Dataset
38.3.1.1 Data Augmentation
38.3.1.2 Employed Deep Learning Models
38.3.1.3 U-Net Model for Deforested Land Segmentation
38.3.1.4 Custom Sequential CNN Model
38.4 Experimental Findings and Analysis
38.4.1 Implementation Details
38.4.2 Experimental Details
38.4.3 Experimental Results
38.4.3.1 Results for Segmentation-Based U-Net Model
38.4.3.2 Results for Classification-Based Sequential CNN Model
38.4.3.3 Ablation Study
38.5 Conclusion
References
39. Precipitation Nowcasting for Crop Irrigation Using Multi-Source Data Fusion and a Dynamic-Static Spatiotemporal
Network
Jing Hu, Hui Deng, Peng Zheng, Honghu Zhang and Xi Wu
39.1 Introduction
39.2 Related Work
39.2.1 Nowcasting Precipitation Based on RNN
39.2.2 Nowcasting Precipitation Based on CNN
39.2.3 Nowcasting Precipitation Based on Transformer
39.3 Method
39.3.1 MDF-DSST
39.3.1.1 Data Preprocessing Module
39.3.1.2 Feature Fusion Module
39.3.1.3 Encoder
39.3.1.4 Dynamic-Static Spatio-Temporal Module
39.3.1.5 Decoder
39.3.2 Loss Function
39.4 Experiments
39.4.1 Dataset
39.4.2 Training Setting
39.4.3 Comparisons with the State-of-the-Art Methods
39.4.4 Ablation Experiments
39.5 Conclusion
Acknowledgement
References
40. Leveraging Machine Learning to Boost Banana Yields in Kerala: A Study of Influential Factors in Kozhikode
Naveena K., Ashish K. Chaturvedi, Fousiya Fasiludeen, Venu Prasad H.D., Santhosh Onte and Surendran U.
Introduction
Methodology
Study Area
Data
Statistical and Machine Learning Approaches for Identifying Key Factors Influencing Banana Cultivation
Conclusion
References
41. Automated Phenotyping of Rice Seedlings Under Saline
Conditions Using EfficientNetB5 Deep Learning Model
Chinmai Shetty, Malathi S.Y., Archana Kumar, Suchetha G. and K. Annapoorneshwari Shetty
41.1 Introduction
41.2 Related Works
41.3 Materials and Methods
41.3.1 Experimental Details
41.3.2 Data Description
41.3.3 Data Visualization
41.3.4 EfficientNetB5
41.3.4.1 Depthwise Separable Convolution
41.3.4.2 Linear Bottleneck
41.4 Results and Analysis
41.4.1 Performance Evaluation for EfficientNetB5
41.5 Conclusion
Acknowledgment
Bibliography
42. An Innovative Hybrid Deep Learning Model for Water Quality Prediction
Mithun Shivakoti, Srinivasa Reddy K. and Narsaiah Shivakoti
42.1 Introduction
42.2 Literature Review
42.3 Proposed Methodology
42.3.1 Dataset Description
42.3.2 Models
42.3.2.1 Random Forest
42.3.2.2 Light Gradient Boosting Method (LGBM)
42.3.2.3 CatBoost
42.3.2.4 Gradient Boosting
42.3.2.5 Extreme Gradient Boosting (XGBoost)
42.3.2.6 SVM
42.3.2.7 Decision Tree
42.3.2.8 Logistic Regression
42.3.2.9 KNN
42.3.2.10 ANN
42.3.2.11 Synthetic Minority Over-Sampling Technique (SMOTE)
42.3.2.12 Adaptive Synthetic Sampling (ADASYN)
42.4 Results
Conclusion
References
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