The book is a timely and forward-looking volume that explores the transformative role of Artificial Intelligence, Federated Learning, IoT, blockchain, and edge computing in building sustainable and climate-resilient agricultural systems. The book brings together interdisciplinary research and practical applications that address critical agricultural challenges such as crop disease detection, pest forecasting, livestock monitoring, resource optimization, climate-risk assessment, and smart farming automation.

Through diverse chapters authored by experts from academia and industry, the book highlights how privacy-preserving federated AI enables collaborative learning across farms, institutions, and regions without compromising data ownership. It also examines ethical, legal, and societal dimensions of deploying intelligent agricultural technologies. Combining innovation with sustainability, this book serves as a valuable resource for researchers, policymakers, students, agritech professionals, and practitioners working toward the future of precision agriculture and resilient global food systems.

Chapter 1. Federated Learning in Agriculture for the Next Generation of Data-Driven and Autonomous Farming Monalisha Pattnaik, Umrah Naushad, Sudev Kumar Padhi and Guddi Mohanty

Chapter 2. Integrating IoT, Edge Computing, and Federated Learning for Scalable Agricultural Intelligence S. Anand and Wan Mazlina Wan Mohamed

Chapter 3. Federated Learning Architectures for Climate-Resilient and Sustainable Agricultural Practices Desham Archana, B. Rama Sree, Bhoomeshwar Bala Raja Shekar Kadurka and Sabih Ahmad

Chapter 4. Advancing Early Crop Disease Detection Using Federated Deep Learning and Remote Sensing Ushaa Eswaran, Vishal Eswaran, Vivek Eswaran and Keerthna Murali

Chapter 5. Livestock Health Monitoring Using Federated Learning and Wearable IoT Sensors
Dr. V. Umadevi

Chapter 6. Federated Learning for Climate Resilience: Overcoming Data Silos and Enhancing Agricultural Sustainability Md. Shoeab Akhter, Sakibul Islam Ratul, Fahmida Tasnim and Prof. Dr. Mohammed Ataur Rahman

Chapter 7. Blockchain-Enabled Federated Learning for Secure and Transparent AgriTech Data Sharing Mohammed Abdul Bari, Md Fasihuddin, Dr. Mohd Ashraf, Dr. Shahanawaj Ahamad and Dr. Akhil Khare

Chapter 8. Federated Learning Case Studies for Plant Disease Forecasting Across Diverse Agro-Ecosystems Singamaneni Krishnapriya, Dr. Amaravarapu Pramodkumar and Rasmitha Kumari Mohanty

Chapter 9. The Future of Autonomous Farming through Intelligent and Distributed Federated AI Systems Lingala Thirupathi, Ananya Seeta, Kashif Mohammed and Ramya G

Chapter 10. Tools, Frameworks, and Real-World Deployment Strategies for Federated Learning in Agriculture Shaheda Begum, Patchipulusu Sneha, Monika Singh T and Dr. S Md Shakir Ali

Chapter 11. Federated Artificial Intelligence for Climate-Resilient Agricultural Marketing Systems Check Mohammed Abdul Bari, Dr. Md Zair Hussain, Dr. G F Ali Ahammed and Dr. Shahanawaj Ahamad

Chapter 12. Proactive Pest and Disease Management: AI-Powered Early Detection, Prediction, and Prevention Systems Satvika Pullisani, Nishitha Pujala and Harish Kannan

Chapter 13. Ethical, Legal, and Societal Challenges in Deploying Federated AI in Agricultural Domains Nishitha Pujala, Satvika Pullisani, Harish Kannan and Samanvitha Karri