This book provides a complete and comprehensive reference for agricultural mobile robots, covering all aspects of the design process, from sensing and perceiving to planning and acting for practical farming applications.
Mobile Robots for Digital Farming explores topics such as Robot Operating Systems (ROS), dynamic simulation, artificial intelligence, image processing, and machine learning. Additionally, it features multiple case studies from funded projects and real-field trials.
This book will be useful for professors and academics in various engineering disciplines (mechanical, robotics, control, electrical, computer, and agricultural), graduate and undergraduate students, farmers, commercial growers, startups, private companies, consultancy agencies, equipment suppliers, and agricultural policymakers.
This book examines the latest achievements in agricultural mobile robots, specifically those that are used for autonomous weed control, field scouting, mowing, and harvesting. The book examines object identification, task planning algorithms, digitalization and optimization of sensors and the challenges in the context of digital farming.
