Synopsis
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.
"synopsis" may belong to another edition of this title.
About the Author
Dr. Siddharth Singh Chouhan is currently working as a Senior Assistant Professor Grade 2 in the School of Computing Science and Engineering, VIT Bhopal University, Sehore, India. He has a B.Tech. (with Honours) 2010, an MTech (with Honours), 2013 in Computer Science and Engineering from RGPV University, Bhopal, India, and a PhD in Computer Science and Engineering from Shri Mata Vaishno Devi University Katra, Jammu and Kashmir, India. He is a Post Doctorate from the University of Malta. His area of interest includes Artificial Intelligence, Computer Vision, Drone Technology, and Precision Agriculture. He had authored several research papers published in reputed journals and conferences.
Dr. Rajneesh Kumar Patel is an Assistant Professor in the School of Computing Science and Engineering, VIT Bhopal University, Sehore, India. He holds a B.E. and an M.Tech. in Electronics and Communication Engineering from RGPV University, Bhopal, India, and a PhD in Electronics and Communication Engineering from Maulana Azad National Institute of Technology, Bhopal, India. His areas of interest include developing image processing, Computer vision, Machine learning/Deep Learning, and Precision Agriculture. He has authored several research papers in many reputed journals and conferences.
Dr. Anju Shukla is an Assistant Professor in the School of Computing Science and Engineering at VIT Bhopal University, Sehore, India. She holds a Doctorate from Jaypee University of Engineering and Technology, Guna, and an MTech in Computer Engineering from Shobhit University, Meerut. Dr. Shukla has successfully guided many undergraduate (UG) students in Computer Science for their academic projects. She has presented and published research papers in international journals (including SCI- and Scopus-indexed journals) as well as in international conferences. She has also attended conferences, workshops, Faculty Development Programs (FDPs), and seminars. Dr. Shukla is an active researcher and has published articles in SCI-indexed journals. Her areas of interest include Cloud Computing, Distributed Computing, and Artificial Intelligence.
Dr. Uday Pratap Singh is a Professor in the Department of Mathematics, Central University of Jammu, Jammu, India. He completed his BSc and MSc (Mathematics and Statistics) from Dr. R.M.L. (Avadh) University, Ayodhya, India, and another MSc (Mathematics and Computing) from Indian Institute of Technology, Guwahati, India, and a PhD in Computer Science from Barkatullah University, Bhopal. His areas of interest include Soft Computing, Nonlinear Systems, and Image Processing. He had authored several research papers in the many reputed journals and conferences. He is a life member of Soft Computing Research Society (SCRC), Barata Ganita Parishad, Computer Society of India and Member of IEEE and AMS.
"About this title" may belong to another edition of this title.
Search results for Farming 5.0: Deep Learning for Future-Generation Agriculture
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Farming 5.0 (Hardcover)
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Taylor & Francis Ltd, London, 2026
ISBN 10: 1041144121
ISBN 13: 9781041144120
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Hardcover. Condition: new. Hardcover. 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 deep learning and advanced AI are revolutionizing agriculture, from autonomous systems and UAVs to computer vision, genomics, and generative AI for synthetic data and digital twins. It emphasizes climate-smart, sustainable farming amid global challenges, while addressing ethics and policy for inclusive AI. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781041144120
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Farming 5.0
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Condition: New. Dr. Siddharth Singh Chouhan is currently working as a Senior Assistant Professor Grade 2 in the School of Computing Science and Engineering, VIT Bhopal University, Sehore, India. He has a B.Tech. (with Honours) 2010, an MTech (with Honours), 2013 . Seller Inventory # 3072810835
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Farming 5.0 (Hardcover)
Published by
Taylor & Francis Ltd, London, 2026
ISBN 10: 1041144121
ISBN 13: 9781041144120
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Hardcover. Condition: new. Hardcover. 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 deep learning and advanced AI are revolutionizing agriculture, from autonomous systems and UAVs to computer vision, genomics, and generative AI for synthetic data and digital twins. It emphasizes climate-smart, sustainable farming amid global challenges, while addressing ethics and policy for inclusive AI. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781041144120
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Farming 5.0 : Deep Learning for Future-Generation Agriculture
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ISBN 10: 1041144121
ISBN 13: 9781041144120
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Buch. Condition: Neu. Neuware - 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. Seller Inventory # 9781041144120