Climate Smart Agriculture, Gebunden

Klappentext

A machine learning method called reinforcement learning trains computers to make decisions based on what would produce the best outcomes. It emulates how humans learn by making mistakes and trying again until they reach their objectives. There are a number of application domains supported by reinforcement learning, including robotics, agriculture, autonomous vehicles, healthcare, finance, and advertising. This book provides a detailed analysis of the climate smart agriculture situation as it stands, the roles and difficulties of farmers, and current technologies and application systems that are technology-enabled. It discusses the art of the deep reinforcement learning framework, algorithms, and architecture with various insights, as well as the challenges faced in reinforcement learning methods by framing out data privacy, security, and scalability in various applications related to climate smart agriculture, like yield prediction, crop management, crop disease prediction, soil health surveillance, precision agriculture, and environmental monitoring.