Rajneesh Kumar Patel – författare

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.

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.

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.

This book explores how modern technologies are changing the way we farm and produce food. It brings together a wide range of smart farming tools and digital solutions that are shaping the future of agriculture. The book covers both the opportunities and challenges that come with using technology in farming and offers practical solutions.The chapters begin with an introduction to the AgriTech revolution and the basic concepts of agricultural technology. It then explores precision agriculture and how computer vision, IoT, and artificial intelligence are helping farmers improve productivity. Readers will also learn about robotics, automation, data analytics, and data visualization in agriculture. Other chapters discuss the role of blockchain in the supply chain, remote sensing, and smart greenhouses. The final chapters focus on sustainability and future trends in AgriTech.This book is a valuable resource for students, researchers, academicians, and professionals who want to understand how advanced technologies are applied in modern farming. It will also benefit those interested in building solutions for digital agriculture and smart farming systems.