Yinyan Shi – författare

This book features interdisciplinary of agricultural engineering and precision agriculture, focusing on real-time variable-rate fertilization (VRF) based on crop phenotypic biological information. It systematically presents cutting-edge research on intelligent sensing, inversion modeling, actuator optimization, and practical system validation. Key scientific methods include hybrid neural network models (such as LFA-PSO-MLP), discrete element modeling (DEM), and deep learning-based flow detection (YOLOv5s-seg). These approaches are illustrated with clear diagrams, data tables, and experimental results, linking theoretical insights with practical engineering applications. The book introduces innovative designs like centrifugal VRF spreaders and phenotypic sensing systems, validated through field trials to improve fertilization accuracy, reduce input waste, and enhance sustainability. It offers a comprehensive technology framework integrating “crop–sensor–decision–actuator” into a closed-loop control system. For researchers and practitioners in smart farming, crop sensing, and equipment development, this book serves as a valuable reference bridging theory and field applications. Target readers include graduate and undergraduate students in agricultural engineering, precision agriculture researchers, intelligent equipment developers, and agricultural extension professionals.

This book features interdisciplinary of agricultural engineering and precision agriculture, focusing on real-time variable-rate fertilization (VRF) based on crop phenotypic biological information. It systematically presents cutting-edge research on intelligent sensing, inversion modeling, actuator optimization, and practical system validation. Key scientific methods include hybrid neural network models (such as LFA-PSO-MLP), discrete element modeling (DEM), and deep learning-based flow detection (YOLOv5s-seg). These approaches are illustrated with clear diagrams, data tables, and experimental results, linking theoretical insights with practical engineering applications. The book introduces innovative designs like centrifugal VRF spreaders and phenotypic sensing systems, validated through field trials to improve fertilization accuracy, reduce input waste, and enhance sustainability. It offers a comprehensive technology framework integrating "e;crop-sensor-decision-actuator"e; into a closed-loop control system. For researchers and practitioners in smart farming, crop sensing, and equipment development, this book serves as a valuable reference bridging theory and field applications. Target readers include graduate and undergraduate students in agricultural engineering, precision agriculture researchers, intelligent equipment developers, and agricultural extension professionals.

This book focuses on state-of-the-art sensing and automation technologies for apple production, which provides innovative knowledge on imaging processing, AI algorithms, sensing techniques, non-destructive detection, and robotics. It falls into the area of applied science, more specifically engineering. This book, including a total of seven chapters, provides undergraduates or graduate take-away knowledge for autonomous apple production, which include but not limited to, apple harvest robotics, autonomous pollination, fruit quality detection, and postharvest infield sorting. A major innovative for this book is that it provides case studies for readers’ easy understanding. The incentive behind this book is to provide readers the latest, systematic, comprehensive new technologies for apple production.