Farming 5.0 : Deep Learning for Future-Generation Agriculture

Chouhan, Siddharth Singh (EDT)/ Patel, Rajneesh Kumar (EDT)/ Shukla, Anju (EDT)

CRC Press（2026/07発売）

• 製本 Hardcover:ハードカバー版／ページ数 436 p.
• 言語 ENG
• 商品コード 9781041144120

Full Description

Agriculture is going through a technology renaissance with deep learning leading the way. This volume addresses how advanced AI methods are transforming the future of farming, food systems, and environmental sustainability. It is forward-looking guide to integrating smart systems in agriculture across all scales - from soil to satellite. The volume comprises contributions from international leaders in AI, agronomy, genomics, remote sensing, and robotics. It addresses a broad array of emerging topics, such as autonomous farming systems, UAVs, and deep reinforcement learning for field operations; computer vision and hybrid models for crop, weed, and livestock detection; and advanced edge and federated learning approaches to real-time, privacy-conscious decision-making. It also discusses explainable AI for transparent agricultural models, deep learning in crop genomics and trait prediction, biodiversity monitoring, and time-series forecasting of pests and climate stress. Generative AI for generating synthetic agricultural data and simulating digital farms with AR/VR and digital twins are also covered. With a focus on climate-smart agriculture, the book addresses the role that deep learning can play in promoting resilient, adaptive, and sustainable food systems under climate change and food insecurity globally. It concludes with an examination of the ethical, regulatory, and policy issues, presenting a vision for inclusive, human-centric AI in agriculture.

Key Features:

Integrates autonomous agriculture, explainable artificial intelligence, edge/federated learning, genomics of crops, and biodiversity monitoring.
Highlights climate-resilient agriculture and future-proof simulations.
Incorporates real-world applications, case studies, and multidisciplinary viewpoints.
Connects AI research, policy, and ethics with agricultural adoption.

Contents

Preface. 1. Seeding the Future: Unveiling the AgriTech Revolution. 2. Introduction to Deep Learning in Agriculture. 3. Enhancing Soil Productivity and Health and Crop Yield with Artificial Intelligence. 4. Deep Learning for Satellite, UAV, and Hyperspectral Crop Monitoring. 5. Edge AI and On-Device Deep Learning Models for In-Field Diagnostics. 6. Interpretable Crop Yield Forecasting Under Climate Change Using Temporal Fusion Transformers. 7. AI4Farms: A Federated Deep Learning Framework for Climate-Resilient Agriculture in Coastal and Marginal Communities. 8. Deep Learning in Agriculture: Applications, Challenges, and Future Directions. 9. Computer Vision for Livestock and Agroforestry Monitoring. 10. Real-Time Recognition of Fruits, Pests, and Weeds Using Integrated Deep Learning Methods. 11. Deep Learning Applications for Agriculture: An Introduction. 12. A Comparative Analysis of 4G and 5G IoT Sensor Networks for Smart Agriculture: Performance, Efficiency, and Future Prospects. 13. Dynamic Detection of Fruit, Pest, and Weed Using Hybrid DL Models. 14. Integrating Deep Learning with loT, Blockchain, and Robotics in Agri-systems. 15. The Unseen Harvest: Ethical and Policy Perspectives in AI-Driven Agriculture. 16. Deep Learning for Satellite, UAV, and Hyperspectral Imaging-Based Crop Monitoring. 17. Pest and Disease Forecasting with Time-Series Deep Models. 18. Deep Reinforcement Learning for Farm Automation. 19. Tomato Plant Disease Classification using Deep Learning Techniques. Index.