Transform the future of sustainable farming with this guide to mastering deep reinforcement learning architectures and algorithms that turn complex environmental data into precise, high-yield decisions for climate-smart agriculture. It conveys the importance of deep reinforcement learning and its technological advancements across climate-smart agriculture applications, addresses challenges related to privacy, security, and scalability of climatic and agricultural data, and explains reinforcement learning from AI and optimal control perspectives. The book explores advanced solutions such as meta learning, hierarchical learning, multi-agent learning, and imitation learning, emphasizing modern frameworks, algorithms, tools, and decision-making systems that support farmers through intelligent, data-driven applications.
A machine learning method called reinforcement learning trains computers to make decisions that produce optimal outcomes by learning through trial and error. Applicable across robotics, autonomous vehicles, healthcare, finance, and agriculture, reinforcement learning plays a critical role in modern intelligent systems. This book provides a detailed analysis of climate-smart agriculture, examining farmers' challenges, current technology-enabled systems, and deep reinforcement learning frameworks, algorithms, and architectures. It also addresses data privacy, security, and scalability issues in applications such as yield prediction, crop management, disease prediction, soil health monitoring, precision agriculture, and environmental monitoring.
Anitha Velu, PhD, is an Assistant Professor in the Department of Electronics and Communication Engineering at Sri Sairam College of Engineering. She has published more than 15 journal papers and holds multiple patents. Her research interests include image processing, VLSI design, ontology, and the semantic web.
Prasanth Aruchamy, PhD, is an Associate Professor at Vel Tech Rangarajan Dr. Sagunthala Research and Design Institute of Science and Technology. He has published more than 45 research articles, holds ten patents, and has authored more than 15 books. His research interests include the Internet of Things, blockchain, wireless sensor networks, medical image processing, and machine learning.
Raghu Ramamoorthy, PhD, is an Assistant Professor in the Department of Computer Science and Engineering at the Oxford College of Engineering. His research focuses on wireless communications and vehicular ad hoc networks.
Rajesh Kumar Dhanaraj, PhD, is a Professor at Symbiosis International University. He has authored and edited more than 50 books, published 115 journal articles, and holds 22 patents. His research interests include machine learning, cyber-physical systems, and wireless sensor networks.
Seifedine Kadry, PhD, is a Professor at Noroff University College with more than 1100 international publications. His research focuses on data science, technology-enhanced education, system prognostics, stochastic systems, and applied mathematics.
