Bøger i Smart Agriculture serien

This book presents a newly designed dynamic fluidic sprinkler (DFS) to improve hydraulic performance of the existing complete fluidic sprinkler (CFS) under low-pressure conditions. Sprinkler irrigation has high prospects for improving water management in crop production. In recent years, low-pressure water-saving has become an important research content in the field of sprinkler irrigation. It introduces the effect of riser height on rotation uniformity and application rate of the dynamic fluidic sprinkler. It also discusses the intelligent sprinkler irrigation technologies for autonomous and remote sensing system. This book will be a useful reference for researchers and professionals in the field of agriculture and irrigation.

This book focuses on state-of-the-art sensing and automation technologies for field crops and in-house product production and provides a lot of innovative knowledge on image processing, AI algorithms and applications in agriculture, and robotics. This book provides undergraduate or graduate students with take-away knowledge for unmanned agricultural production, including but not limited to corn disease detection, wheat head detection and counting, and soil nutrient condition monitoring. The first three chapters focus on reviewing plant phenotyping sensing technology and robotics and soil nutrient monitoring, followed by in-house crop sensing robotics. Then two case studies on corn and the other two case studies on wheat are presented.

The book provides the most recent technology for sensing and automation in apple production cycle in terms of bagging robotics, flower pollination robotics, pruning robotics, and harvest robotics. It does not only summarize the development of technology progress, but also discuss the future trend for unmanned apple production cycle. Though apple production still mainly relies on manual labor, a huge number of innovative technologies emerge during the past years, which pave the road for unmanned apple orchard management. A book summarizing all these new and innovative technologies is needed, to provide an easy approach to understand this field quickly and comprehensively, which drives the publication of this book. This book is a useful reference for students, researchers, and practitioners in the field of apple production.

This book, consisting of 8 chapters, describes the state-of-the-art technological progress and applications of unmanned aerial vehicles (UAVs) in precision agriculture. It focuses on the UAV application in agriculture, such as crop disease detection, mid-season yield estimation, crop nutrient status, and high-throughput phenotyping. Different from individual papers focusing on a specific application, this book provides a holistic view for readers with a wide range of subjects. In addition to researchers in the areas of plant science, plant pathology, breeding, engineering, it is also intended for undergraduates and graduates who are interested in imaging processing, artificial intelligence in agriculture, precision agriculture, agricultural automation, and robotics.

This book presents the progress, changes, and evolvement for apple mechanical harvest during the past decades, which include, but not limited to, bulky harvest method, harvest platform, apple infield sorting, and harvest robotics. Though there are significant progresses in apple harvest robotics, there still is a long way ahead before its practical applications, with existing and potential bottlenecks described in this book. Hence, other researchers would take advantage of this book to have a hint of the apple mechanical harvest history and state-of-the-art progress, so that they can find the room for their new research. This book targets senior undergraduates and more importantly graduate students in the field of agricultural engineering, sensing, automatic, and robotics.

This book presents the progress, changes, and evolvement for apple mechanical harvest during the past decades, which include, but not limited to, bulky harvest method, harvest platform, apple infield sorting, and harvest robotics.