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Submit a ProposalAgricultural Supply Chain Optimization Using Federated Learning
One Line Description
Master the next evolution of agricultural intelligence with this definitive guide to federated learning, providing the decentralized, privacy-preserving strategies needed to optimize global supply chains without compromising data sovereignty.
Audience
Researchers, academics, engineers, scientists, and technologists in the fields of renewable energy, materials science, computer science, and environmental science.
Researchers, academics, engineers, scientists, and technologists in the fields of renewable energy, materials science, computer science, and environmental science.
Description
As global agriculture faces challenges such as climate variability, resource inefficiency, and privacy concerns, traditional centralized AI systems struggle to handle the scale and sensitivity of data involved. Federated learning addresses these issues by enabling decentralized, privacy-preserving model training across distributed datasets, ensuring secure and collaborative optimization. Advances in federated learning, such as adaptive algorithms, blockchain integration, and scalable architectures, have enhanced its applicability in agriculture. Federated learning supports precision farming, logistics optimization, and sustainable resource management by enabling real-time decision-making while respecting local variations and data privacy regulations. This book explores the transformative role of federated learning in the agricultural supply chain. Unlike traditional machine learning, federated learning enables collaborative model training across decentralized data sources, ensuring security and data sovereignty—a critical requirement for stakeholders in agriculture. This subject bridges the gap between cutting-edge AI technologies and practical supply chain management practices.
The content of the book is structured to provide a comprehensive understanding of both theoretical and applied aspects of federated learning in agriculture. The book will cover foundational concepts of federated learning, its application to optimize the different phases of the supply chain, and how it enhances operational security. It delves into practical case studies and real-world implementations, giving readers insights into how these concepts are being applied to improve productivity, reduce waste, and ensure sustainability in agriculture. By providing a balanced focus on the technical and managerial aspects of federated learning, this book ensures accessibility to a broad audience while maintaining depth and rigor for academic and professional experts.
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As global agriculture faces challenges such as climate variability, resource inefficiency, and privacy concerns, traditional centralized AI systems struggle to handle the scale and sensitivity of data involved. Federated learning addresses these issues by enabling decentralized, privacy-preserving model training across distributed datasets, ensuring secure and collaborative optimization. Advances in federated learning, such as adaptive algorithms, blockchain integration, and scalable architectures, have enhanced its applicability in agriculture. Federated learning supports precision farming, logistics optimization, and sustainable resource management by enabling real-time decision-making while respecting local variations and data privacy regulations. This book explores the transformative role of federated learning in the agricultural supply chain. Unlike traditional machine learning, federated learning enables collaborative model training across decentralized data sources, ensuring security and data sovereignty—a critical requirement for stakeholders in agriculture. This subject bridges the gap between cutting-edge AI technologies and practical supply chain management practices.
The content of the book is structured to provide a comprehensive understanding of both theoretical and applied aspects of federated learning in agriculture. The book will cover foundational concepts of federated learning, its application to optimize the different phases of the supply chain, and how it enhances operational security. It delves into practical case studies and real-world implementations, giving readers insights into how these concepts are being applied to improve productivity, reduce waste, and ensure sustainability in agriculture. By providing a balanced focus on the technical and managerial aspects of federated learning, this book ensures accessibility to a broad audience while maintaining depth and rigor for academic and professional experts.
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Author / Editor Details
Abhishek Kumar, PhD is an Assistant Director and Professor in the Computer Science and Engineering Department at Chandigarh University with more than 13 years of teaching experience. He has authored seven books, edited 51 books, and published more than 170 peer-reviewed articles. His research spans AI, renewable energy, image processing, and data mining.
Abhishek Kumar, PhD is an Assistant Director and Professor in the Computer Science and Engineering Department at Chandigarh University with more than 13 years of teaching experience. He has authored seven books, edited 51 books, and published more than 170 peer-reviewed articles. His research spans AI, renewable energy, image processing, and data mining.
Pooja Dixit is an Assistant Professor in the Department of Computer Science at Shri Ratanlal Kanwarlal Patni Girls' College, Kishangarh, India. With more than seven years of academic teaching and two years of research experience, she has published more than 25 research papers in reputed journals, books, and conferences. Her research interests include artificial intelligence, machine learning, and data mining.
J.P. Ananth, PhD is a Professor and Dean in the Internal Quality Assurance Cell at the Sri Krishna College of Engineering and Technology with more than 23 years of experience. He serves as a reviewer for several international conferences and journals. His research interests include computer vision, pattern recognition, artificial intelligence, and data analytics.
S. Oswalt Manoj, PhD is an Associate Professor in the Department of Computer Science and Engineering at the Sri Krishna College of Engineering and Technology with more than 14 years of experience. He has published more than 100 publications in reputed, peer-reviewed national and international journals and conferences, authored one book, and edited two books. His research areas include big data analytics, artificial intelligence, computer vision, machine learning, deep learning, and cloud computing.
S. Panneerselvam, PhD is a Professor in the Department of Agricultural Engineering at the Hindustan College of Engineering and Technology. He has published 65 research articles, more than 12 books, and 20 book chapters.
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Table of Contents
Preface
1. A Review of Federated Learning and Its Importance in Advancing Agricultural Practices
Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraz, Lakshmanan M., Vegi Fernando A. and Mithaguru
1.1 Introduction
1.2 Advantages of Federated Learning in Agriculture
1.3 Literature Survey
1.3.1 Agricultural Data Privacy and Federated Learning: A Review in Computers and Electronics in Agriculture
1.3.2 AGRIFOLD: Federated Leaf Disease Detection
1.3.3 Federated Learning for Crop Yield Prediction: A Review on Information Processing in Agriculture
1.3.3.1 Using Federated Learning to Diagnose Heterogeneous Multi-Site Crop Disease
1.3.3.2 Balancing Centralization and Decentralization in Federated Learning for Crop-Type Modeling
1.3.3.3 Multi-Edge Clustered and Edge Artificial Intelligence Heterogeneous Federated Learning
1.3.4 Federated Learning-Oriented Edge Computing Framework for the Industrial Internet of Things
1.3.5 Privacy-Preserving Data Linkage for Collaborative Agricultural Research
1.3.6 Real-Time Crop Disease Federated Learning Pilot by the Indian Institute of Information Technology, Allahabad
1.4 Different Tools for Federated Learning Implementation
1.4.1 MATLAB
1.4.2 TensorFlow Federated
1.4.3 PySyft
1.4.4 Federated Artificial Intelligence Technology Enabler
1.4.5 IBM Federated Learning Framework
1.4.6 Flower
1.4.7 NVIDIA Federated Learning Application Runtime Environment
1.4.8 OpenFL
1.4.9 FedML
1.4.10 FedLab
1.4.11 FedJAX
1.4.12 FedTree
1.5 Types of Federated Learning
1.5.1 Centralized Federated Learning Architecture
1.5.2 Decentralized Federated Learning
1.6 Challenges of Federated Learning in Smart Agriculture
1.6.1 Communication Overheads
1.6.2 Energy-Efficient Routing
1.6.3 Privacy Issues
1.6.4 Data Heterogeneity
1.6.5 System Heterogeneity
1.6.6 Data Quality Control
1.6.7 Debugging and Monitoring Limitations
1.7 Conclusion and Future Scope
References
2. Blockchain-Integrated Federated Learning for Secure and Transparent Agricultural Supply Chains
Lakshmanan M., Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R. and Joshuva Arockia Dhanraj
2.1 Introduction
2.2 Literature Review
2.2.1 Traditional Agricultural Supply Chain Optimization
2.2.2 Federated Learning in Agriculture and the Internet of Things
2.2.2.1 Applications of Federated Learning in Agriculture
2.2.2.2 Challenges in Federated Learning Adoption
2.2.3 Blockchain in Agriculture
2.2.3.1 Applications of Blockchain in Agriculture
2.2.3.2 Limitations of Blockchain
2.2.4 Blockchain–Federated Learning Synergy
2.2.5 Research Gaps
2.3 Federated Learning in Agricultural Supply Chains
2.3.1 Fundamentals of Federated Learning
2.3.2 Applications in Agricultural Supply Chains
2.3.3 Case Studies and Existing Research
2.3.4 Challenges in Federated Learning for Agriculture
2.3.5 Workflow of Federated Learning in Agriculture
2.4 Blockchain for Agricultural Supply Chains
2.4.1 Fundamentals of Blockchain Technology
2.4.2 Applications of Blockchain in Agriculture
2.4.3 Case Studies and Research Efforts
2.4.4 Limitations and Challenges
2.4.5 Blockchain-Enabled Agricultural Traceability Workflow
2.4.6 Summary
2.5 Blockchain–Federated Learning Integrated Framework
2.5.1 Rationale for Integration
2.5.2 Framework Architecture
2.5.3 Data Privacy and Security Mechanisms
2.5.4 Consensus and Incentive Mechanisms
2.5.5 Application Scenarios
2.5.6 Advantages over Standalone Approaches
2.5.7 Challenges and Future Directions
2.5.8 Summary
2.6 Security, Privacy, and Trust Mechanisms in Blockchain–Federated Learning
2.6.1 Threat Landscape in Blockchain–Federated Learning Systems
2.6.2 Homomorphic Encryption
2.6.3 Secure Multi-Party Computation
2.6.4 Trust Establishment with Blockchain
2.6.5 Integrated Security Framework
2.6.6 Challenges and Future Research Directions
2.6.7 Summary
2.7 Applications and Case Studies: Blockchain–Federated Learning in Agriculture
2.7.1 Counterfeit Detection and Input Verification
2.7.2 Food Safety and Quality Monitoring
2.7.3 Yield Prediction and Farm Productivity
2.7.4 Pest and Disease Detection
2.7.5 Logistics Optimization and Supply-Demand Alignment
2.7.6 Sustainability Certification and Carbon Tracking
2.7.7 Case Study Mapping of Blockchain–Federated Learning Applications
2.7.8 Summary
2.8 Conclusion
References
3. Managing Climate Variability with Federated Artificial Intelligence Models
Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj and Lakshmanan M.
3.1 Introduction
3.2 Literature Survey
3.2.1 From Centralized Machine Learning to Federated Learning
3.2.2 Centralized Machine Learning in Climate Variability: Strengths and Limitations
3.2.3 Federated Learning Foundations and Their Relevance to Climate
3.2.3.1 Core Algorithms and Systems
3.2.3.2 Privacy and Security Enhancements
3.2.3.3 Systems Features Important for Climate
3.2.4 Federated Learning in Scientific and Geospatial Domains
3.2.5 Federated Learning for Climate Variability: Design Patterns and Gaps
3.2.5.1 Data and Modeling Patterns
3.2.5.2 Evaluation Practices
3.2.5.3 Open Challenges in Climate Federated Learning
3.3 Case Studies
3.3.1 Case Study 1: Cyclone Prediction
3.3.1.1 Federated Learning Approach for Cyclone Prediction
3.3.2 Case Study 2: Drought Forecasting
3.3.2.1 Federated Learning Approach for Drought Forecasting
3.3.3 Case Study 3: Flood Early Warning Systems
3.3.3.1 Federated Learning Approach for Flood Early Warning Systems
3.4 Climate Variability: Data and Computational Perspectives
3.4.1 Satellite Remote Sensing Data
3.4.2 Reanalysis Datasets
3.4.3 Internet-of-Things-Based Weather Sensors
3.4.4 Ground-Based Meteorological Stations
3.4.5 Data Characteristics and Challenges in Federated Artificial Intelligence Context
3.5 Federated Artificial Intelligence Models and Technical Architecture for Federated Climate Artificial Intelligence
3.5.1 Data Acquisition Layer
3.5.2 Local Model Training Layer
3.5.3 Aggregation Layer
3.6 Conclusion
Bibliography
4. Engineering and Deployment of Federated Learning Systems in Agricultural Supply Chains: DevOps, Orchestration, and Cost Modeling Case Study: Federated Learning for Crop Yield Forecasting
Meena Sharma
4.1 Introduction
4.2 Scalability and System Design of Federated Learning
4.3 DevOps for Federated Systems
4.3.1 Continuous Integration
4.3.2 Continuous Deployment
4.3.3 Combined Continuous Integration and Continuous Deployment Workflow
4.4 Infrastructure-as-Code, Computerization, and Orchestrator Tools in Federated Learning
4.4.1 Tools of Orchestration
4.4.1.1 TensorFlow Federated
4.4.1.2 Flower
4.4.1.3 Open Federated Learning
4.4.1.4 Federated Machine Learning
4.4.1.5 PySyft
4.4.2 Containerization and Infrastructure-as-Code
4.4.3 Advantages of Containerization and Orchestration in Federated Learning
4.4.4 Federated Learning Orchestration Frameworks
4.5 The Use of Orchestration Tools in Federated Learning
4.6 Solving Client Churn and Intermittent Connectivity
4.6.1 Techniques of Client Dropout Management
4.6.2 Offline-First Designs
4.7 Operation Budgets and Cost Modeling
4.7.1 Federated Learning Cost Dimensions
4.7.2 Budgets Estimation Framework
4.7.3 Cost–Performance Trade-Offs
4.8 Case Study: Federated Learning for Crop Yield Forecasting
4.8.1 Scenario Description
4.8.2 System Deployment
4.8.3 Cost Modeling
4.8.4 Outcomes
4.9 Conclusion
References
5. Integrating Federated Learning with Satellite-Based Geospatial Analysis for Urban Lake Management: Case Study of Ana Sagar Lake, Rajasthan
Rohini Yadawar, Kh. Moirangleima and Shailendra Patni
5.1 Introduction
5.1.1 Background
5.1.1.1 Semiarid Areas Water Resources
5.1.2 Significance of the Study
5.1.2.1 Environmental Importance
5.1.2.2 Socio-Economic Relevance
5.1.2.3 Technological Innovation
5.1.3 Research Gap
5.1.3.1 Limited Temporal Studies
5.1.4 Research Objectives
5.1.4.1 Primary Objective
5.1.4.2 Secondary Objectives
5.2 Literature Review
5.3 Study Area
5.3.1 Geographic and Climatic Setting
5.3.2 Study Area Overview
5.3.3 Agricultural and Runoff Pathways in the Ana Sagar Catchment
5.4 Data and Methodology
5.4.1 Data Sources and Temporal Coverage
5.4.2 Hybrid Processing Workflow
5.4.2.1 Google Earth Engine Cloud Processing
5.4.2.2 Interoperability with Desktop Geographic Information System Platforms
5.4.2.3 Determination of Areas and Uncertainty
5.4.3 Federated Learning Framework Architecture
5.4.3.1 Multi-Stakeholder Node Design
5.4.3.2 Privacy-Preserving Collaborative Learning
5.4.4 Validation and Accuracy Assessment
5.5 Results
5.5.1 Temporal Expansion of Ana Sagar Lake
5.5.2 Spatial Expansion Patterns
5.5.3 Google Earth Engine Platform Performance
5.5.3.1 Analysis of Ana Sagar Lake (1991) Using a Google Earth Engine-Derived Map
5.5.3.2 Analytical Evaluation of Ana Sagar Lake (2001) Using a Google Earth Engine-Derived Map
5.5.3.3 Analysis of Ana Sagar Lake (2011) Using a Google Earth Engine-Derived Map
5.5.3.4 Ana Sagar Lake (2021) Using a Google Earth Engine-Derived Map Analysis
5.5.3.5 Analysis of Ana Sagar Lake (2023) Using a Google Earth Engine-Derived Map
5.5.3.6 Analysis of Multi-Temporal Boundary Overlay (1991–2023)
5.5.4 Federated Learning Framework Assessment
5.5.4.1 Multi-Stakeholder Data Integration
5.5.4.2 Privacy-Preserving Governance
5.5.4.3 Predictive Modeling
5.6 Discussion
5.6.1 Interpretation of Expansion
5.6.1.1 Paradoxical Growth
5.6.1.2 Wastewater as Primary Driver
5.6.1.3 Broader Implications
5.6.2 Methodological Innovations
5.6.2.1 Hybrid Google Earth Engine and Geographic Information System Approach
5.6.2.2 Federated Learning Framework
5.6.2.3 Reproducibility
5.6.3 Comparative Analysis
5.6.4 Policy Implications
5.6.4.1 Immediate Actions
5.6.4.2 Long-Term Planning
5.6.4.3 Governance Innovation
5.6.5 Socio-Economic Dimensions
5.7 Recommendations
5.7.1 Immediate Actions
5.7.1.1 Supreme Court Compliance
5.7.1.2 Pollution Mitigation
5.7.1.3 Encroachment Management
5.7.2 Long-Term Management
5.7.3 Governance Strengthening
5.7.4 Community Engagement
5.7.5 Research Priorities
5.7.6 Policy Recommendations
5.8 Conclusion
5.8.1 Key Findings
5.8.2 Theoretical Contributions
5.8.3 Practical Implications
5.8.4 Study Limitations
5.8.5 Conclusion
Acknowledgments
Bibliography
6. Blockchain-Driven Loan Management System for Enhancing Agricultural Finance
M. Margarat, Chandrabalan C., Kishore Kumar S. and Nirmal Raj J.
6.1 Introduction
6.2 Related Works
6.3 Existing System
6.3.1 Existing Agricultural Loan Management System: Challenges and Limitations
6.3.2 Traditional System of Disbursing Agricultural Loans
6.3.3 Complicated Loan Application Process
6.3.4 Bureaucratic Delays and Inefficiencies
6.3.5 Lack of Transparency and Corruption
6.3.6 Challenges in Government Intervention and Policy Implementation
6.3.7 The Need for a More Transparent and Efficient System
6.4 Proposed Work
6.4.1 System Enhancement Based on Review Feedback
6.4.1.1 Integration With Microfinance Institutions and Cooperatives
6.4.1.2 Machine Learning Based Credit Scoring
6.4.1.3 Scalability Through Layer-2 Blockchain
6.4.1.4 Multilingual and Inclusive User Interface
6.4.2 Decentralized Identity Management
6.4.2.1 Security and Identity Verification Enhancements
6.4.3 Farmer Dashboard
6.4.4 Government Dashboard
6.4.5 Blockchain Layer
6.4.6 Work Flow
6.4.7 Security and Integrity of Data
6.4.8 Automated Loan Repayment and Credit Scoring
6.5 Result and Discussion
6.6 Conclusion
6.7 Future Scope
Bibliography
7. Federated Learning in Agriculture: Enabling Secure and Accurate Crop Yield Prediction for Supply Chain Management
Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mitha Guru and Sugandha Saxena
7.1 Introduction
7.2 Proposed Methodology
7.2.1 Architecture of the Proposed Model
7.2.2 Dataset Description
7.2.3 Experimental Environment
7.2.4 Proposed Algorithms
7.2.4.1 Decision Tree
7.2.4.2 Random Forest
7.2.4.3 Gradient Boosting
7.2.4.4 Multi-Layer Perceptron
7.3 Experimental Results and Discussion
7.3.1 Model Performance Overview
7.3.2 Analysis of Error Metrics
7.3.3 Learning Curve Insights
7.3.4 Discussion in Agricultural Supply Chain Context
7.3.5 Implications and Future Enhancements
7.4 Conclusion
References
8. A Case Study: A Real-Time Yellow Rust Infections Classification Using Various Advanced Approaches of Deep Learning Models
Shivani Sood, Harjeet Sing, Satinder Kaur and Suruchi Jindal
8.1 Introduction
8.2 Dataset Collection
8.3 Data Preparation
8.4 Training and Fine-Tuning the Model
8.4.1 Experimental Setup
8.4.2 Model Optimization and Fine-Tuning the Parameters
8.4.3 Model Architecture
8.5 Result and Discussion
8.5.1 Accuracy Comparison
8.5.2 Loss Graph Comparison
8.5.3 Confusion Matrix Analysis
8.5.4 Comparison of the Proposed Model with State-of-the-Art Methods
8.6 Conclusion and Future Work
References
9. Federated Learning with Edge Computing for Real-Time Decision-Making
Charles Mahimainathan A.
9.1 Introduction to Edge Computing in Agriculture
9.2 Overview of Federated Learning
9.3 Synergies between Federated Learning and Edge Computing
9.3.1 Enhanced Real-Time Decision-Making
9.3.2 Enhanced Data Privacy and Security
9.3.3 Robustness to Network Disruptions
9.3.4 Scalability and Heterogeneity
9.4 Architectural Considerations for Federated Learning-Edge Systems in Agriculture
9.4.1 Edge Device Capabilities
9.4.2 Communication Infrastructure
9.4.3 Data Heterogeneity and Non-Independent and Identically Distributed
9.4.4 Security and Privacy Mechanisms
9.4.5 Model Management and Deployment
9.4.6 Energy Efficiency
9.5 Use Cases in Agricultural Supply Chains
9.5.1 Precision Agriculture
9.5.2 Livestock Management
9.5.3 Supply Chain Optimization
9.5.4 Agricultural Robotics and Autonomous Systems
9.6 Comparison of Centralized Cloud Computing, Edge, and Federated Learning with Edge Computing in Agriculture
9.7 Challenges and Future Directions
9.7.1 Resource Constraints in Edge Devices
9.7.2 Data Heterogeneity and Non-Independent and Identically Distributed
9.7.3 Overhead in Communication and Bandwidth
9.7.4 Security and Privacy Threats
9.7.5 Non-Standardization and Non-Interoperability
9.7.6 Reward and Trust Building
9.8 Conclusion
Bibliography
10. Enhancing Federated Learning Scalability for Global Agricultural Networks
Pramod Singh Rathore and Shweta Solanki
10.1 Introduction
10.1.1 Concept of Federated Learning
10.1.1.1 Communication Bottlenecks
10.1.1.2 Data Heterogeneity
10.1.1.3 Resource Constraints
10.1.1.4 Unreliable Internet Connectivity
10.1.2 Standardization Issues
10.1.3 Communication-Efficient Federated Learning Algorithms
10.1.3.1 Hierarchical Federated Learning Structures
10.1.3.2 Edge-Cloud Synergy
10.1.3.3 Model Personalization
10.1.3.4 Sustainability Aspects
10.1.3.5 Future Research Directions
10.2 Fundamentals of Federated Learning in Agriculture
10.2.1 Concept of Federated Learning
10.2.2 Importance of Data Privacy in Agriculture
10.2.2.1 Critical Aspects of Data Privacy in Agriculture
10.2.3 Unique Characteristics of Agricultural Data
10.3 Challenges in Scaling Federated Learning for Global Agricultural Networks (Hinglish)
10.3.1 Communication Overhead
10.3.2 Model Divergence Due to Data Heterogeneity
10.3.3 Resource Constraints At Farm Devices
10.3.4 Unreliable Network Connections
10.3.5 Data Labeling and Standardization Issues
10.4 Communication-Efficient Federated Learning Algorithms
10.4.1 Federated Averaging and Its Variants
10.4.2 Model Compression Techniques (Quantization, Sparsification)
10.4.3 Client Selection Strategies for Heterogeneous Networks
10.4.4 Asynchronous and Semi-Synchronous Federated Learning Approaches
10.5 Hierarchical Federated Learning for Agriculture
10.5.1 Concept of Multi-Level Federated Learning (Field-Level, Region-Level, Global-Level)
10.5.2 Benefits of Hierarchical Aggregation
10.5.3 Deployment Architecture for Agricultural Federated Learning
10.5.4 Case Studies in Regional Crop Disease Prediction
10.6 Edge-Cloud Synergy in Agricultural Federated Learning
10.6.1 Role of Edge Computing in Preprocessing and Local Aggregation
10.6.2 Collaborative Learning Between Edge Nodes and Cloud Servers
10.6.3 Sustainable Bandwidth Management in Rural Areas
10.7 Model Personalization in Agricultural Federated Learning
10.7.1 Personalized Federated Learning Approaches
10.7.2 Fine-Tuning Models for Local Farm Conditions
10.7.3 Transfer Learning and Meta-Learning Extensions
10.8 Future Research Directions
10.9 Conclusion
References
11. Advanced Crop Yield Prediction Models for Indian Agriculture
Geetha N. K., Vasudha S. N., Jamuna P. and Sudhakar B.
11.1 Introduction
11.2 Literature Review
11.3 Methodologies
11.4 Performance Metrics and Evaluation Frameworks
11.5 Experiments
11.6 Conclusion
References
12. Crop Yield Prediction and Resource Allocation Optimization
N. Fathima Shrene Shifna, K. Baalaji and G. Nivethasri
12.1 Introduction
12.2 Crop Yield Prediction and Optimization: Output Analysis and Performance Enhancement
12.2.1 Input Data Integration
12.2.2 Data Processing and Feature Engineering
12.2.3 Model Architecture and Performance
12.2.4 Performance Metrics and Validation
12.2.5 Multi-Objective Optimization Framework
12.3 Evolutionary Optimization Algorithms
12.4 Optimization Results and Impact
12.4.1 Uncertainty Quantification and Risk Assessment
12.4.2 Adaptive Learning and Model Updating
12.4.3 Operational Implementation and Machine Learning Operations
12.4.4 Integration with Farm Management Systems
12.4.5 Future Enhancements and Research Directions
12.4.6 Enhanced Optimization Capabilities
12.5 Conclusion
References
13. Federated Learning for Smart Agricultural Supply Chains: Unified Approaches to Logistics, Crop Yield, and Threat Prediction
Mamta
13.1 Introduction
13.1.1 The Need for Smart Solutions to the Agricultural Supply Chain Challenges
13.1.2 Limitations of Centralized Artificial Intelligence Approaches
13.1.3 Role and Advantages of Federated Learning in Agriculture
13.2 Literature Review
13.3 Foundations of Federated Learning in Agriculture
13.3.1 Core Concepts and Architectures
13.3.1.1 Federated Learning Architectures in Agriculture
13.3.1.2 Agricultural Relevance
13.3.2 Data Privacy, Security, and Collaboration without Data Sharing
13.3.2.1 Privacy and Security Mechanisms in Federated Learning
13.3.2.2 Collaboration without Data Sharing
13.4 Federated Learning for Logistics Optimization
13.4.1 Route Planning and Transportation Efficiency
13.4.1.1 How Federated Learning Improves Route Planning
13.4.1.2 Impact on Transportation Efficiency
13.4.2 Reducing Post-Harvest Losses through FL-Enabled Supply Chain Insights
13.4.2.1 How Federated Learning Minimizes Post-Harvest Losses
13.4.2.2 Benefits for Stakeholders
13.5 Federated Learning for Crop Yield Prediction
13.5.1 Data Sources and Federated Learning Modeling Approach
13.5.1.1 Primary Product Information for Yield Estimation
13.5.1.2 Federated Learning Modeling Approach
13.5.2 Benefits for Resource Planning and Risk Management
13.5.2.1 Smarter Resource Planning
13.5.2.2 Risk Management
13.5.2.3 Socio-Economic Benefits
13.6 Federated Learning for Weather and Threat Forecasting
13.6.1 Distributed Weather Prediction Models
13.6.1.1 How Federated Learning Enhances Weather Prediction
13.6.1.2 Benefits of Distributed Weather Forecasting
13.6.2 Early Detection of Pests and Diseases Using Federated Learning
13.6.2.1 Data Sources for Pest and Disease Detection
13.6.2.2 Federated Learning Workflow for Threat Detection
13.6.2.3 Benefits for Stakeholders
13.7 Integrated Approach and Synergies
13.7.1 How Logistics, Yield, and Threat Models Work Together
13.7.1.1 Integration Pathways
13.7.1.2 Synergies in Action
13.7.2 Unified Decision-Support Framework for Stakeholders
13.7.2.1 Core Elements of the Unified Framework
13.7.2.2 Benefits of the Unified Framework
13.8 Challenges and Future Directions
13.8.1 Technical, Adoption, and Policy Challenges
13.8.2 Future Prospects for Federated Learning in Sustainable Agriculture
13.9 Conclusion
13.9.1 Summary of Key Insights and Impact on Agricultural Supply Chains
13.9.1.1 Key Insights Recap
13.9.1.2 Impact on Agricultural Supply Chains
References
14. Farmer-Centric Artificial Intelligence through Explainable Federated Learning for Smart Agriculture
Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mithaguru, Sugandha Saxena and Mude Nagarjuna Naik
14.1 Introduction
14.2 Literature Review
14.3 Background
14.4 Proposed Framework
14.4.1 Data Layer
14.4.2 Federated Learning Layer
14.4.3 Explainability Layer
14.4.4 Farmer Interface Layer
14.4.5 Applications and Case Studies
14.4.6 Crop Yield Prediction
14.4.7 Smart Irrigation Control
14.4.8 Soil Health Monitoring
14.4.9 Transparency of an Agricultural Supply Chain
14.4.10 Multi-Farm Simulated Collaboration Case Study
14.5 Challenges and Future Directions
14.5.1 Technical Challenges
14.5.2 Ethical and Social Predicaments
14.5.3 Policy and Regulatory Issues
14.5.4 Future Directions
14.6 Limitations
14.7 Practical Implications
14.8 Contribution to the United Nations’ Sustainable Development Goals
14.9 Conclusion
Bibliography
15. Proposing a Federated Learning Policy Framework for Smart, Secure, and Sustainable Agricultural Supply Chains
Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik and Sriramkumar R.
15.1 Introduction
15.2 Current Agricultural and Digital Policy Landscape in Karnataka
15.3 Need for a Policy Framework in Federated Learning for Agriculture
15.3.1 Data Fragmentation and Privacy Concerns
15.3.2 Technological Infrastructure and Rural Connectivity Gaps
15.3.3 Farmer Participation and Incentivization
15.3.4 Institutional Collaboration and Governance
15.3.5 In Line with the Global Trends of Artificial Intelligence and Agriculture
15.4 Proposing Policy Framework for Karnataka through Federated Learning
15.4.1 Data Governance and Privacy
15.4.2 Technological Enabling Infrastructure
15.4.3 Farmer Incentives and Capacity Building
15.4.4 Models of Institutional Collaboration and Governance
15.4.5 Strategic Alliances with National and International Programs
15.4.6 Monitoring, Evaluation, and Policy Adaptability
15.5 Karnataka Locality-Based Case Study on Agriculture
15.5.1 Precision Irrigation, Sugarcane Belt (Mandya District)
15.5.2 Yield Prediction for Millets in the Semi-Arid Zones (Raichur, Koppal, and Chitradurga)
15.5.3 Market Price Forecasting with Rashtriya e-Market Services
15.5.4 Climate-Smart Agriculture in Coffee Plantations (Coorg and Chikmagalur)
15.5.5 Summarization from Case Studies
15.6 Future Directions and Research Implications
15.6.1 Integration with Blockchain for Traceability and Trust
15.6.2 Leveraging Remote Sensing and Internet of Things for Federated Intelligence
15.6.3 Climate-Smart Based on Federated Learning Models
15.6.4 Global Cooperation and Global Knowledge Sharing
15.6.5 Research Ecosystems Research Policy
15.7 Conclusion
References
16. Privacy-Aware Machine Learning for Sustainable Farming: Federated Learning in Disease Detection
Mithaguru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M. and Vegi Fernando A.
16.1 Introduction
16.2 Literature Survey
16.3 Role of Federated Learning in Agriculture for Disease Detection
16.3.1 Significance of Disease Detection in Agriculture
16.3.2 Collaborative Model Training Federated Learning
16.3.3 Dealing with Heterogeneity of Data
16.3.4 Implementation and Live Detection
16.3.5 Privacy, Trust, and Adoption
16.4 Federated Learning Challenges and Opportunities in Detecting Diseases in Agriculture
16.5 Federa]ted Learning Concept and Framework
16.5.1 Local Training and Edge Clients
16.5.2 Global Aggregation and Federated Server
16.5.3 Global Model Deployment
16.5.4 Control Actions, Dashboards and Alerts
16.5.5 Communication and Privacy
16.6 Methodology
16.6.1 Problem Formulation
16.6.2 Data Collection and Preprocessing
16.6.3 Local Model Training
16.6.4 Federated Aggregation
16.6.5 Privacy and Security Mechanisms
16.6.6 Embodiment and Inference
16.6.7 Evaluation Metrics
16.6.7.1 Classification Metrics
16.6.7.2 Segmentation Metrics
16.6.7.3 Federated Performance Metrics
16.7 Contribution to Sustainable Development Goals (SDGs) and Future Directions
16.7.1 Sustainable Development Goal (SDGs) Impact
16.7.2 Future Scope
16.8 Conclusion
References
About the Editors
Index
Back to Top
Preface
1. A Review of Federated Learning and Its Importance in Advancing Agricultural Practices
Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraz, Lakshmanan M., Vegi Fernando A. and Mithaguru
1.1 Introduction
1.2 Advantages of Federated Learning in Agriculture
1.3 Literature Survey
1.3.1 Agricultural Data Privacy and Federated Learning: A Review in Computers and Electronics in Agriculture
1.3.2 AGRIFOLD: Federated Leaf Disease Detection
1.3.3 Federated Learning for Crop Yield Prediction: A Review on Information Processing in Agriculture
1.3.3.1 Using Federated Learning to Diagnose Heterogeneous Multi-Site Crop Disease
1.3.3.2 Balancing Centralization and Decentralization in Federated Learning for Crop-Type Modeling
1.3.3.3 Multi-Edge Clustered and Edge Artificial Intelligence Heterogeneous Federated Learning
1.3.4 Federated Learning-Oriented Edge Computing Framework for the Industrial Internet of Things
1.3.5 Privacy-Preserving Data Linkage for Collaborative Agricultural Research
1.3.6 Real-Time Crop Disease Federated Learning Pilot by the Indian Institute of Information Technology, Allahabad
1.4 Different Tools for Federated Learning Implementation
1.4.1 MATLAB
1.4.2 TensorFlow Federated
1.4.3 PySyft
1.4.4 Federated Artificial Intelligence Technology Enabler
1.4.5 IBM Federated Learning Framework
1.4.6 Flower
1.4.7 NVIDIA Federated Learning Application Runtime Environment
1.4.8 OpenFL
1.4.9 FedML
1.4.10 FedLab
1.4.11 FedJAX
1.4.12 FedTree
1.5 Types of Federated Learning
1.5.1 Centralized Federated Learning Architecture
1.5.2 Decentralized Federated Learning
1.6 Challenges of Federated Learning in Smart Agriculture
1.6.1 Communication Overheads
1.6.2 Energy-Efficient Routing
1.6.3 Privacy Issues
1.6.4 Data Heterogeneity
1.6.5 System Heterogeneity
1.6.6 Data Quality Control
1.6.7 Debugging and Monitoring Limitations
1.7 Conclusion and Future Scope
References
2. Blockchain-Integrated Federated Learning for Secure and Transparent Agricultural Supply Chains
Lakshmanan M., Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R. and Joshuva Arockia Dhanraj
2.1 Introduction
2.2 Literature Review
2.2.1 Traditional Agricultural Supply Chain Optimization
2.2.2 Federated Learning in Agriculture and the Internet of Things
2.2.2.1 Applications of Federated Learning in Agriculture
2.2.2.2 Challenges in Federated Learning Adoption
2.2.3 Blockchain in Agriculture
2.2.3.1 Applications of Blockchain in Agriculture
2.2.3.2 Limitations of Blockchain
2.2.4 Blockchain–Federated Learning Synergy
2.2.5 Research Gaps
2.3 Federated Learning in Agricultural Supply Chains
2.3.1 Fundamentals of Federated Learning
2.3.2 Applications in Agricultural Supply Chains
2.3.3 Case Studies and Existing Research
2.3.4 Challenges in Federated Learning for Agriculture
2.3.5 Workflow of Federated Learning in Agriculture
2.4 Blockchain for Agricultural Supply Chains
2.4.1 Fundamentals of Blockchain Technology
2.4.2 Applications of Blockchain in Agriculture
2.4.3 Case Studies and Research Efforts
2.4.4 Limitations and Challenges
2.4.5 Blockchain-Enabled Agricultural Traceability Workflow
2.4.6 Summary
2.5 Blockchain–Federated Learning Integrated Framework
2.5.1 Rationale for Integration
2.5.2 Framework Architecture
2.5.3 Data Privacy and Security Mechanisms
2.5.4 Consensus and Incentive Mechanisms
2.5.5 Application Scenarios
2.5.6 Advantages over Standalone Approaches
2.5.7 Challenges and Future Directions
2.5.8 Summary
2.6 Security, Privacy, and Trust Mechanisms in Blockchain–Federated Learning
2.6.1 Threat Landscape in Blockchain–Federated Learning Systems
2.6.2 Homomorphic Encryption
2.6.3 Secure Multi-Party Computation
2.6.4 Trust Establishment with Blockchain
2.6.5 Integrated Security Framework
2.6.6 Challenges and Future Research Directions
2.6.7 Summary
2.7 Applications and Case Studies: Blockchain–Federated Learning in Agriculture
2.7.1 Counterfeit Detection and Input Verification
2.7.2 Food Safety and Quality Monitoring
2.7.3 Yield Prediction and Farm Productivity
2.7.4 Pest and Disease Detection
2.7.5 Logistics Optimization and Supply-Demand Alignment
2.7.6 Sustainability Certification and Carbon Tracking
2.7.7 Case Study Mapping of Blockchain–Federated Learning Applications
2.7.8 Summary
2.8 Conclusion
References
3. Managing Climate Variability with Federated Artificial Intelligence Models
Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj and Lakshmanan M.
3.1 Introduction
3.2 Literature Survey
3.2.1 From Centralized Machine Learning to Federated Learning
3.2.2 Centralized Machine Learning in Climate Variability: Strengths and Limitations
3.2.3 Federated Learning Foundations and Their Relevance to Climate
3.2.3.1 Core Algorithms and Systems
3.2.3.2 Privacy and Security Enhancements
3.2.3.3 Systems Features Important for Climate
3.2.4 Federated Learning in Scientific and Geospatial Domains
3.2.5 Federated Learning for Climate Variability: Design Patterns and Gaps
3.2.5.1 Data and Modeling Patterns
3.2.5.2 Evaluation Practices
3.2.5.3 Open Challenges in Climate Federated Learning
3.3 Case Studies
3.3.1 Case Study 1: Cyclone Prediction
3.3.1.1 Federated Learning Approach for Cyclone Prediction
3.3.2 Case Study 2: Drought Forecasting
3.3.2.1 Federated Learning Approach for Drought Forecasting
3.3.3 Case Study 3: Flood Early Warning Systems
3.3.3.1 Federated Learning Approach for Flood Early Warning Systems
3.4 Climate Variability: Data and Computational Perspectives
3.4.1 Satellite Remote Sensing Data
3.4.2 Reanalysis Datasets
3.4.3 Internet-of-Things-Based Weather Sensors
3.4.4 Ground-Based Meteorological Stations
3.4.5 Data Characteristics and Challenges in Federated Artificial Intelligence Context
3.5 Federated Artificial Intelligence Models and Technical Architecture for Federated Climate Artificial Intelligence
3.5.1 Data Acquisition Layer
3.5.2 Local Model Training Layer
3.5.3 Aggregation Layer
3.6 Conclusion
Bibliography
4. Engineering and Deployment of Federated Learning Systems in Agricultural Supply Chains: DevOps, Orchestration, and Cost Modeling Case Study: Federated Learning for Crop Yield Forecasting
Meena Sharma
4.1 Introduction
4.2 Scalability and System Design of Federated Learning
4.3 DevOps for Federated Systems
4.3.1 Continuous Integration
4.3.2 Continuous Deployment
4.3.3 Combined Continuous Integration and Continuous Deployment Workflow
4.4 Infrastructure-as-Code, Computerization, and Orchestrator Tools in Federated Learning
4.4.1 Tools of Orchestration
4.4.1.1 TensorFlow Federated
4.4.1.2 Flower
4.4.1.3 Open Federated Learning
4.4.1.4 Federated Machine Learning
4.4.1.5 PySyft
4.4.2 Containerization and Infrastructure-as-Code
4.4.3 Advantages of Containerization and Orchestration in Federated Learning
4.4.4 Federated Learning Orchestration Frameworks
4.5 The Use of Orchestration Tools in Federated Learning
4.6 Solving Client Churn and Intermittent Connectivity
4.6.1 Techniques of Client Dropout Management
4.6.2 Offline-First Designs
4.7 Operation Budgets and Cost Modeling
4.7.1 Federated Learning Cost Dimensions
4.7.2 Budgets Estimation Framework
4.7.3 Cost–Performance Trade-Offs
4.8 Case Study: Federated Learning for Crop Yield Forecasting
4.8.1 Scenario Description
4.8.2 System Deployment
4.8.3 Cost Modeling
4.8.4 Outcomes
4.9 Conclusion
References
5. Integrating Federated Learning with Satellite-Based Geospatial Analysis for Urban Lake Management: Case Study of Ana Sagar Lake, Rajasthan
Rohini Yadawar, Kh. Moirangleima and Shailendra Patni
5.1 Introduction
5.1.1 Background
5.1.1.1 Semiarid Areas Water Resources
5.1.2 Significance of the Study
5.1.2.1 Environmental Importance
5.1.2.2 Socio-Economic Relevance
5.1.2.3 Technological Innovation
5.1.3 Research Gap
5.1.3.1 Limited Temporal Studies
5.1.4 Research Objectives
5.1.4.1 Primary Objective
5.1.4.2 Secondary Objectives
5.2 Literature Review
5.3 Study Area
5.3.1 Geographic and Climatic Setting
5.3.2 Study Area Overview
5.3.3 Agricultural and Runoff Pathways in the Ana Sagar Catchment
5.4 Data and Methodology
5.4.1 Data Sources and Temporal Coverage
5.4.2 Hybrid Processing Workflow
5.4.2.1 Google Earth Engine Cloud Processing
5.4.2.2 Interoperability with Desktop Geographic Information System Platforms
5.4.2.3 Determination of Areas and Uncertainty
5.4.3 Federated Learning Framework Architecture
5.4.3.1 Multi-Stakeholder Node Design
5.4.3.2 Privacy-Preserving Collaborative Learning
5.4.4 Validation and Accuracy Assessment
5.5 Results
5.5.1 Temporal Expansion of Ana Sagar Lake
5.5.2 Spatial Expansion Patterns
5.5.3 Google Earth Engine Platform Performance
5.5.3.1 Analysis of Ana Sagar Lake (1991) Using a Google Earth Engine-Derived Map
5.5.3.2 Analytical Evaluation of Ana Sagar Lake (2001) Using a Google Earth Engine-Derived Map
5.5.3.3 Analysis of Ana Sagar Lake (2011) Using a Google Earth Engine-Derived Map
5.5.3.4 Ana Sagar Lake (2021) Using a Google Earth Engine-Derived Map Analysis
5.5.3.5 Analysis of Ana Sagar Lake (2023) Using a Google Earth Engine-Derived Map
5.5.3.6 Analysis of Multi-Temporal Boundary Overlay (1991–2023)
5.5.4 Federated Learning Framework Assessment
5.5.4.1 Multi-Stakeholder Data Integration
5.5.4.2 Privacy-Preserving Governance
5.5.4.3 Predictive Modeling
5.6 Discussion
5.6.1 Interpretation of Expansion
5.6.1.1 Paradoxical Growth
5.6.1.2 Wastewater as Primary Driver
5.6.1.3 Broader Implications
5.6.2 Methodological Innovations
5.6.2.1 Hybrid Google Earth Engine and Geographic Information System Approach
5.6.2.2 Federated Learning Framework
5.6.2.3 Reproducibility
5.6.3 Comparative Analysis
5.6.4 Policy Implications
5.6.4.1 Immediate Actions
5.6.4.2 Long-Term Planning
5.6.4.3 Governance Innovation
5.6.5 Socio-Economic Dimensions
5.7 Recommendations
5.7.1 Immediate Actions
5.7.1.1 Supreme Court Compliance
5.7.1.2 Pollution Mitigation
5.7.1.3 Encroachment Management
5.7.2 Long-Term Management
5.7.3 Governance Strengthening
5.7.4 Community Engagement
5.7.5 Research Priorities
5.7.6 Policy Recommendations
5.8 Conclusion
5.8.1 Key Findings
5.8.2 Theoretical Contributions
5.8.3 Practical Implications
5.8.4 Study Limitations
5.8.5 Conclusion
Acknowledgments
Bibliography
6. Blockchain-Driven Loan Management System for Enhancing Agricultural Finance
M. Margarat, Chandrabalan C., Kishore Kumar S. and Nirmal Raj J.
6.1 Introduction
6.2 Related Works
6.3 Existing System
6.3.1 Existing Agricultural Loan Management System: Challenges and Limitations
6.3.2 Traditional System of Disbursing Agricultural Loans
6.3.3 Complicated Loan Application Process
6.3.4 Bureaucratic Delays and Inefficiencies
6.3.5 Lack of Transparency and Corruption
6.3.6 Challenges in Government Intervention and Policy Implementation
6.3.7 The Need for a More Transparent and Efficient System
6.4 Proposed Work
6.4.1 System Enhancement Based on Review Feedback
6.4.1.1 Integration With Microfinance Institutions and Cooperatives
6.4.1.2 Machine Learning Based Credit Scoring
6.4.1.3 Scalability Through Layer-2 Blockchain
6.4.1.4 Multilingual and Inclusive User Interface
6.4.2 Decentralized Identity Management
6.4.2.1 Security and Identity Verification Enhancements
6.4.3 Farmer Dashboard
6.4.4 Government Dashboard
6.4.5 Blockchain Layer
6.4.6 Work Flow
6.4.7 Security and Integrity of Data
6.4.8 Automated Loan Repayment and Credit Scoring
6.5 Result and Discussion
6.6 Conclusion
6.7 Future Scope
Bibliography
7. Federated Learning in Agriculture: Enabling Secure and Accurate Crop Yield Prediction for Supply Chain Management
Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mitha Guru and Sugandha Saxena
7.1 Introduction
7.2 Proposed Methodology
7.2.1 Architecture of the Proposed Model
7.2.2 Dataset Description
7.2.3 Experimental Environment
7.2.4 Proposed Algorithms
7.2.4.1 Decision Tree
7.2.4.2 Random Forest
7.2.4.3 Gradient Boosting
7.2.4.4 Multi-Layer Perceptron
7.3 Experimental Results and Discussion
7.3.1 Model Performance Overview
7.3.2 Analysis of Error Metrics
7.3.3 Learning Curve Insights
7.3.4 Discussion in Agricultural Supply Chain Context
7.3.5 Implications and Future Enhancements
7.4 Conclusion
References
8. A Case Study: A Real-Time Yellow Rust Infections Classification Using Various Advanced Approaches of Deep Learning Models
Shivani Sood, Harjeet Sing, Satinder Kaur and Suruchi Jindal
8.1 Introduction
8.2 Dataset Collection
8.3 Data Preparation
8.4 Training and Fine-Tuning the Model
8.4.1 Experimental Setup
8.4.2 Model Optimization and Fine-Tuning the Parameters
8.4.3 Model Architecture
8.5 Result and Discussion
8.5.1 Accuracy Comparison
8.5.2 Loss Graph Comparison
8.5.3 Confusion Matrix Analysis
8.5.4 Comparison of the Proposed Model with State-of-the-Art Methods
8.6 Conclusion and Future Work
References
9. Federated Learning with Edge Computing for Real-Time Decision-Making
Charles Mahimainathan A.
9.1 Introduction to Edge Computing in Agriculture
9.2 Overview of Federated Learning
9.3 Synergies between Federated Learning and Edge Computing
9.3.1 Enhanced Real-Time Decision-Making
9.3.2 Enhanced Data Privacy and Security
9.3.3 Robustness to Network Disruptions
9.3.4 Scalability and Heterogeneity
9.4 Architectural Considerations for Federated Learning-Edge Systems in Agriculture
9.4.1 Edge Device Capabilities
9.4.2 Communication Infrastructure
9.4.3 Data Heterogeneity and Non-Independent and Identically Distributed
9.4.4 Security and Privacy Mechanisms
9.4.5 Model Management and Deployment
9.4.6 Energy Efficiency
9.5 Use Cases in Agricultural Supply Chains
9.5.1 Precision Agriculture
9.5.2 Livestock Management
9.5.3 Supply Chain Optimization
9.5.4 Agricultural Robotics and Autonomous Systems
9.6 Comparison of Centralized Cloud Computing, Edge, and Federated Learning with Edge Computing in Agriculture
9.7 Challenges and Future Directions
9.7.1 Resource Constraints in Edge Devices
9.7.2 Data Heterogeneity and Non-Independent and Identically Distributed
9.7.3 Overhead in Communication and Bandwidth
9.7.4 Security and Privacy Threats
9.7.5 Non-Standardization and Non-Interoperability
9.7.6 Reward and Trust Building
9.8 Conclusion
Bibliography
10. Enhancing Federated Learning Scalability for Global Agricultural Networks
Pramod Singh Rathore and Shweta Solanki
10.1 Introduction
10.1.1 Concept of Federated Learning
10.1.1.1 Communication Bottlenecks
10.1.1.2 Data Heterogeneity
10.1.1.3 Resource Constraints
10.1.1.4 Unreliable Internet Connectivity
10.1.2 Standardization Issues
10.1.3 Communication-Efficient Federated Learning Algorithms
10.1.3.1 Hierarchical Federated Learning Structures
10.1.3.2 Edge-Cloud Synergy
10.1.3.3 Model Personalization
10.1.3.4 Sustainability Aspects
10.1.3.5 Future Research Directions
10.2 Fundamentals of Federated Learning in Agriculture
10.2.1 Concept of Federated Learning
10.2.2 Importance of Data Privacy in Agriculture
10.2.2.1 Critical Aspects of Data Privacy in Agriculture
10.2.3 Unique Characteristics of Agricultural Data
10.3 Challenges in Scaling Federated Learning for Global Agricultural Networks (Hinglish)
10.3.1 Communication Overhead
10.3.2 Model Divergence Due to Data Heterogeneity
10.3.3 Resource Constraints At Farm Devices
10.3.4 Unreliable Network Connections
10.3.5 Data Labeling and Standardization Issues
10.4 Communication-Efficient Federated Learning Algorithms
10.4.1 Federated Averaging and Its Variants
10.4.2 Model Compression Techniques (Quantization, Sparsification)
10.4.3 Client Selection Strategies for Heterogeneous Networks
10.4.4 Asynchronous and Semi-Synchronous Federated Learning Approaches
10.5 Hierarchical Federated Learning for Agriculture
10.5.1 Concept of Multi-Level Federated Learning (Field-Level, Region-Level, Global-Level)
10.5.2 Benefits of Hierarchical Aggregation
10.5.3 Deployment Architecture for Agricultural Federated Learning
10.5.4 Case Studies in Regional Crop Disease Prediction
10.6 Edge-Cloud Synergy in Agricultural Federated Learning
10.6.1 Role of Edge Computing in Preprocessing and Local Aggregation
10.6.2 Collaborative Learning Between Edge Nodes and Cloud Servers
10.6.3 Sustainable Bandwidth Management in Rural Areas
10.7 Model Personalization in Agricultural Federated Learning
10.7.1 Personalized Federated Learning Approaches
10.7.2 Fine-Tuning Models for Local Farm Conditions
10.7.3 Transfer Learning and Meta-Learning Extensions
10.8 Future Research Directions
10.9 Conclusion
References
11. Advanced Crop Yield Prediction Models for Indian Agriculture
Geetha N. K., Vasudha S. N., Jamuna P. and Sudhakar B.
11.1 Introduction
11.2 Literature Review
11.3 Methodologies
11.4 Performance Metrics and Evaluation Frameworks
11.5 Experiments
11.6 Conclusion
References
12. Crop Yield Prediction and Resource Allocation Optimization
N. Fathima Shrene Shifna, K. Baalaji and G. Nivethasri
12.1 Introduction
12.2 Crop Yield Prediction and Optimization: Output Analysis and Performance Enhancement
12.2.1 Input Data Integration
12.2.2 Data Processing and Feature Engineering
12.2.3 Model Architecture and Performance
12.2.4 Performance Metrics and Validation
12.2.5 Multi-Objective Optimization Framework
12.3 Evolutionary Optimization Algorithms
12.4 Optimization Results and Impact
12.4.1 Uncertainty Quantification and Risk Assessment
12.4.2 Adaptive Learning and Model Updating
12.4.3 Operational Implementation and Machine Learning Operations
12.4.4 Integration with Farm Management Systems
12.4.5 Future Enhancements and Research Directions
12.4.6 Enhanced Optimization Capabilities
12.5 Conclusion
References
13. Federated Learning for Smart Agricultural Supply Chains: Unified Approaches to Logistics, Crop Yield, and Threat Prediction
Mamta
13.1 Introduction
13.1.1 The Need for Smart Solutions to the Agricultural Supply Chain Challenges
13.1.2 Limitations of Centralized Artificial Intelligence Approaches
13.1.3 Role and Advantages of Federated Learning in Agriculture
13.2 Literature Review
13.3 Foundations of Federated Learning in Agriculture
13.3.1 Core Concepts and Architectures
13.3.1.1 Federated Learning Architectures in Agriculture
13.3.1.2 Agricultural Relevance
13.3.2 Data Privacy, Security, and Collaboration without Data Sharing
13.3.2.1 Privacy and Security Mechanisms in Federated Learning
13.3.2.2 Collaboration without Data Sharing
13.4 Federated Learning for Logistics Optimization
13.4.1 Route Planning and Transportation Efficiency
13.4.1.1 How Federated Learning Improves Route Planning
13.4.1.2 Impact on Transportation Efficiency
13.4.2 Reducing Post-Harvest Losses through FL-Enabled Supply Chain Insights
13.4.2.1 How Federated Learning Minimizes Post-Harvest Losses
13.4.2.2 Benefits for Stakeholders
13.5 Federated Learning for Crop Yield Prediction
13.5.1 Data Sources and Federated Learning Modeling Approach
13.5.1.1 Primary Product Information for Yield Estimation
13.5.1.2 Federated Learning Modeling Approach
13.5.2 Benefits for Resource Planning and Risk Management
13.5.2.1 Smarter Resource Planning
13.5.2.2 Risk Management
13.5.2.3 Socio-Economic Benefits
13.6 Federated Learning for Weather and Threat Forecasting
13.6.1 Distributed Weather Prediction Models
13.6.1.1 How Federated Learning Enhances Weather Prediction
13.6.1.2 Benefits of Distributed Weather Forecasting
13.6.2 Early Detection of Pests and Diseases Using Federated Learning
13.6.2.1 Data Sources for Pest and Disease Detection
13.6.2.2 Federated Learning Workflow for Threat Detection
13.6.2.3 Benefits for Stakeholders
13.7 Integrated Approach and Synergies
13.7.1 How Logistics, Yield, and Threat Models Work Together
13.7.1.1 Integration Pathways
13.7.1.2 Synergies in Action
13.7.2 Unified Decision-Support Framework for Stakeholders
13.7.2.1 Core Elements of the Unified Framework
13.7.2.2 Benefits of the Unified Framework
13.8 Challenges and Future Directions
13.8.1 Technical, Adoption, and Policy Challenges
13.8.2 Future Prospects for Federated Learning in Sustainable Agriculture
13.9 Conclusion
13.9.1 Summary of Key Insights and Impact on Agricultural Supply Chains
13.9.1.1 Key Insights Recap
13.9.1.2 Impact on Agricultural Supply Chains
References
14. Farmer-Centric Artificial Intelligence through Explainable Federated Learning for Smart Agriculture
Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mithaguru, Sugandha Saxena and Mude Nagarjuna Naik
14.1 Introduction
14.2 Literature Review
14.3 Background
14.4 Proposed Framework
14.4.1 Data Layer
14.4.2 Federated Learning Layer
14.4.3 Explainability Layer
14.4.4 Farmer Interface Layer
14.4.5 Applications and Case Studies
14.4.6 Crop Yield Prediction
14.4.7 Smart Irrigation Control
14.4.8 Soil Health Monitoring
14.4.9 Transparency of an Agricultural Supply Chain
14.4.10 Multi-Farm Simulated Collaboration Case Study
14.5 Challenges and Future Directions
14.5.1 Technical Challenges
14.5.2 Ethical and Social Predicaments
14.5.3 Policy and Regulatory Issues
14.5.4 Future Directions
14.6 Limitations
14.7 Practical Implications
14.8 Contribution to the United Nations’ Sustainable Development Goals
14.9 Conclusion
Bibliography
15. Proposing a Federated Learning Policy Framework for Smart, Secure, and Sustainable Agricultural Supply Chains
Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik and Sriramkumar R.
15.1 Introduction
15.2 Current Agricultural and Digital Policy Landscape in Karnataka
15.3 Need for a Policy Framework in Federated Learning for Agriculture
15.3.1 Data Fragmentation and Privacy Concerns
15.3.2 Technological Infrastructure and Rural Connectivity Gaps
15.3.3 Farmer Participation and Incentivization
15.3.4 Institutional Collaboration and Governance
15.3.5 In Line with the Global Trends of Artificial Intelligence and Agriculture
15.4 Proposing Policy Framework for Karnataka through Federated Learning
15.4.1 Data Governance and Privacy
15.4.2 Technological Enabling Infrastructure
15.4.3 Farmer Incentives and Capacity Building
15.4.4 Models of Institutional Collaboration and Governance
15.4.5 Strategic Alliances with National and International Programs
15.4.6 Monitoring, Evaluation, and Policy Adaptability
15.5 Karnataka Locality-Based Case Study on Agriculture
15.5.1 Precision Irrigation, Sugarcane Belt (Mandya District)
15.5.2 Yield Prediction for Millets in the Semi-Arid Zones (Raichur, Koppal, and Chitradurga)
15.5.3 Market Price Forecasting with Rashtriya e-Market Services
15.5.4 Climate-Smart Agriculture in Coffee Plantations (Coorg and Chikmagalur)
15.5.5 Summarization from Case Studies
15.6 Future Directions and Research Implications
15.6.1 Integration with Blockchain for Traceability and Trust
15.6.2 Leveraging Remote Sensing and Internet of Things for Federated Intelligence
15.6.3 Climate-Smart Based on Federated Learning Models
15.6.4 Global Cooperation and Global Knowledge Sharing
15.6.5 Research Ecosystems Research Policy
15.7 Conclusion
References
16. Privacy-Aware Machine Learning for Sustainable Farming: Federated Learning in Disease Detection
Mithaguru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M. and Vegi Fernando A.
16.1 Introduction
16.2 Literature Survey
16.3 Role of Federated Learning in Agriculture for Disease Detection
16.3.1 Significance of Disease Detection in Agriculture
16.3.2 Collaborative Model Training Federated Learning
16.3.3 Dealing with Heterogeneity of Data
16.3.4 Implementation and Live Detection
16.3.5 Privacy, Trust, and Adoption
16.4 Federated Learning Challenges and Opportunities in Detecting Diseases in Agriculture
16.5 Federa]ted Learning Concept and Framework
16.5.1 Local Training and Edge Clients
16.5.2 Global Aggregation and Federated Server
16.5.3 Global Model Deployment
16.5.4 Control Actions, Dashboards and Alerts
16.5.5 Communication and Privacy
16.6 Methodology
16.6.1 Problem Formulation
16.6.2 Data Collection and Preprocessing
16.6.3 Local Model Training
16.6.4 Federated Aggregation
16.6.5 Privacy and Security Mechanisms
16.6.6 Embodiment and Inference
16.6.7 Evaluation Metrics
16.6.7.1 Classification Metrics
16.6.7.2 Segmentation Metrics
16.6.7.3 Federated Performance Metrics
16.7 Contribution to Sustainable Development Goals (SDGs) and Future Directions
16.7.1 Sustainable Development Goal (SDGs) Impact
16.7.2 Future Scope
16.8 Conclusion
References
About the Editors
Index
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