Agricultural Supply Chain Using Federated Learning

1. Auflage 2026
ISBN: 978-1-394-46126-4
Verlag: John Wiley & Sons Inc

Buch, Englisch, 416 Seiten

Master the next evolution of agricultural intelligence with this definitive guide to federated learning, providing decentralized, privacy-preserving strategies needed to optimize global supply chains without compromising data sovereignty.

As global agriculture faces challenges such as climate variability, resource inefficiency, and data privacy concerns, traditional centralized AI systems struggle to operate at scale. Federated learning addresses these limitations by enabling decentralized, privacy-preserving model training across distributed datasets, supporting secure and collaborative optimization. This book explores how federated learning enhances precision farming, logistics optimization, and sustainable resource management through real-time, data-driven decision-making while respecting local variations and regulatory constraints. It bridges the gap between advanced AI technologies and practical agricultural supply chain management, covering foundational concepts, system architectures, and real-world implementations. Through case studies and applied insights, the book demonstrates how federated learning can improve productivity, reduce waste, and strengthen sustainability while maintaining data sovereignty. It offers a balanced perspective on both technical and managerial aspects, making it accessible to a wide audience while retaining depth for academic and industry professionals.

Autoren/Hrsg.

Kumar, Abhishek

Dixit, Pooja

Ananth, J. P.

Manoj, S. Oswalt

Panneerselvam, S.

Fachgebiete

• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften Agrartechnik, Landmaschinen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung

Inhaltsverzeichnis

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

Über Autor(innen)

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 101 books, and published more than 220 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 ten years of academic teaching and two years of research experience, she has published more than 30 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 Director of the Internal Quality Assurance Cell at Dayananda Sagar University 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 a Professor in the Department of Computer Science and Engineering at Alliance University 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.