Preface xxiii 1 A Review of Federated Learning and Its Importance in Advancing Agricultural Practices 1 Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraz, Lakshmanan M., Vegi Fernando A. and Mithaguru 1.1 Introduction 2 1.2 Advantages of Federated Learning in Agriculture 5 1.3 Literature Survey 7 1.4 Different Tools for Federated Learning Implementation 10 1.

5 Types of Federated Learning 13 1.6 Challenges of Federated Learning in Smart Agriculture 14 1.7 Conclusion and Future Scope 16 2 Blockchain-Integrated Federated Learning for Secure and Transparent Agricultural Supply Chains 23 Lakshmanan M., Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R. and Joshuva Arockia Dhanraj 2.1 Introduction 24 2.2 Literature Review 25 2.

3 Federated Learning in Agricultural Supply Chains 30 2.4 Blockchain for Agricultural Supply Chains 34 2.5 Blockchain-Federated Learning Integrated Framework 37 2.6 Security, Privacy, and Trust Mechanisms in Blockchain-Federated Learning 42 2.7 Applications and Case Studies: Blockchain-Federated Learning in Agriculture 47 2.8 Conclusion 51 3 Managing Climate Variability with Federated Artificial Intelligence Models 57 Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj and Lakshmanan M.

3.1 Introduction 58 3.2 Literature Survey 59 3.3 Case Studies 66 3.4 Climate Variability: Data and Computational Perspectives 68 3.5 Federated Artificial Intelligence Models and Technical Architecture for Federated Climate Artificial Intelligence 70 3.6 Conclusion 75 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 81 Meena Sharma 4.1 Introduction 82 4.

2 Scalability and System Design of Federated Learning 82 4.3 DevOps for Federated Systems 83 4.4 Infrastructure-as-Code, Computerization, and Orchestrator Tools in Federated Learning 85 4.5 The Use of Orchestration Tools in Federated Learning 90 4.6 Solving Client Churn and Intermittent Connectivity 90 4.7 Operation Budgets and Cost Modeling 91 4.8 Case Study: Federated Learning for Crop Yield Forecasting 93 4.9 Conclusion 95 5 Integrating Federated Learning with Satellite-Based Geospatial Analysis for Urban Lake Management: Case Study of Ana Sagar Lake, Rajasthan 99 Rohini Yadawar, Kh.

Moirangleima and Shailendra Patni 5.1 Introduction 100 5.2 Literature Review 103 5.3 Study Area 103 5.4 Data and Methodology 105 5.5 Results 107 5.6 Discussion 114 5.7 Recommendations 117 5.

8 Conclusion 119 6 Blockchain-Driven Loan Management System for Enhancing Agricultural Finance 123 M. Margarat, Chandrabalan C., Kishore Kumar S. and Nirmal Raj J. 6.1 Introduction 123 6.2 Related Works 125 6.3 Existing System 128 6.

4 Proposed Work 130 6.5 Result and Discussion 137 6.6 Conclusion 140 6.7 Future Scope 141 7 Federated Learning in Agriculture: Enabling Secure and Accurate Crop Yield Prediction for Supply Chain Management 145 Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mitha Guru and Sugandha Saxena 7.1 Introduction 146 7.

2 Proposed Methodology 148 7.3 Experimental Results and Discussion 154 7.4 Conclusion 159 8 A Case Study: A Real-Time Yellow Rust Infections Classification Using Various Advanced Approaches of Deep Learning Models 165 Shivani Sood, Harjeet Sing, Satinder Kaur and Suruchi Jindal 8.1 Introduction 166 8.2 Dataset Collection 169 8.3 Data Preparation 170 8.4 Training and Fine-Tuning the Model 175 8.5 Result and Discussion 181 8.

6 Conclusion and Future Work 186 9 Federated Learning with Edge Computing for Real-Time Decision-Making 191 Charles Mahimainathan A. 9.1 Introduction to Edge Computing in Agriculture 192 9.2 Overview of Federated Learning 192 9.3 Synergies between Federated Learning and Edge Computing 194 9.4 Architectural Considerations for Federated Learning-Edge Systems in Agriculture 195 9.5 Use Cases in Agricultural Supply Chains 197 9.6 Comparison of Centralized Cloud Computing, Edge, and Federated Learning with Edge Computing in Agriculture 199 9.

7 Challenges and Future Directions 201 9.8 Conclusion 202 10 Enhancing Federated Learning Scalability for Global Agricultural Networks 205 Pramod Singh Rathore and Shweta Solanki 10.1 Introduction 206 10.2 Fundamentals of Federated Learning in Agriculture 208 10.3 Challenges in Scaling Federated Learning for Global Agricultural Networks (Hinglish) 211 10.4 Communication-Efficient Federated Learning Algorithms 213 10.5 Hierarchical Federated Learning for Agriculture 216 10.6 Edge-Cloud Synergy in Agricultural Federated Learning 219 10.

7 Model Personalization in Agricultural Federated Learning 221 10.8 Future Research Directions 223 10.9 Conclusion 224 11 Advanced Crop Yield Prediction Models for Indian Agriculture 227 Geetha N. K., Vasudha S. N., Jamuna P. and Sudhakar B.

11.1 Introduction 228Contents xvii 11.2 Literature Review 229 11.3 Methodologies 231 11.4 Performance Metrics and Evaluation Frameworks 234 11.5 Experiments 234 11.6 Conclusion 239 12 Crop Yield Prediction and Resource Allocation Optimization 245 N. Fathima Shrene Shifna, K.

Baalaji and G. Nivethasri 12.1 Introduction 246 12.2 Crop Yield Prediction and Optimization: Output Analysis and Performance Enhancement 254 12.3 Evolutionary Optimization Algorithms 260 12.4 Optimization Results and Impact 260 12.5 Conclusion 263 13 Federated Learning for Smart Agricultural Supply Chains: Unified Approaches to Logistics, Crop Yield, and Threat Prediction 267 Mamta 13.1 Introduction 268 13.

2 Literature Review 272xviii Contents 13.3 Foundations of Federated Learning in Agriculture 274 13.4 Federated Learning for Logistics Optimization 278 13.5 Federated Learning for Crop Yield Prediction 280 13.6 Federated Learning for Weather and Threat Forecasting 284 13.7 Integrated Approach and Synergies 287 13.8 Challenges and Future Directions 290 13.9 Conclusion 293 14 Farmer-Centric Artificial Intelligence through Explainable Federated Learning for Smart Agriculture 299 Sriramkumar R.

, Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mithaguru, Sugandha Saxena and Mude Nagarjuna Naik 14.1 Introduction 300 14.2 Literature Review 301 14.3 Background 303 14.4 Proposed Framework 305 14.5 Challenges and Future Directions 312 14.

6 Limitations 316 14.7 Practical Implications 316 14.8 Contribution to the United Nations'' Sustainable Development Goals 317 14.9 Conclusion 318 15 Proposing a Federated Learning Policy Framework for Smart, Secure, and Sustainable Agricultural Supply Chains 323 Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik and Sriramkumar R. 15.1 Introduction 324 15.

2 Current Agricultural and Digital Policy Landscape in Karnataka 326 15.3 Need for a Policy Framework in Federated Learning for Agriculture 329 15.4 Proposing Policy Framework for Karnataka through Federated Learning 332 15.5 Karnataka Locality-Based Case Study on Agriculture 335 15.6 Future Directions and Research Implications 337 15.7 Conclusion 339 16 Privacy-Aware Machine Learning for Sustainable Farming: Federated Learning in Disease Detection 345 Mithaguru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M. and Vegi Fernando A.

16.1 Introduction 346 16.2 Literature Survey 347 16.3 Role of Federated Learning in Agriculture for Disease Detection 349 16.4 Federated Learning Challenges and Opportunities in Detecting Diseases in Agriculture 351 16.5 Federated Learning Concept and Framework 353 16.6 Methodology 355 16.7 Contribution to Sustainable Development Goals (SDGs) and Future Directions 360 16.

8 Conclusion 362 References 362 Index 365.