Technological Progress on Variable Rate Fertilization

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.

List of contents

Foreword by Prof. Zhao Zhang.- Preface.- Progress and development on biological information of crop phenotype research applied to real-time variable-rate fertilization.- Precision Variable-Rate Fertilization for Rice-Wheat Cropping Using Outer-Grooved Wheel Mechanism Based on Multi-Layer Perceptron Model.- Design and Modeling of the Centrifugal Variable-Rate Fertilizer Spreader.- Mechanical properties of fertilizer particles and crop straw under discrete element modeling.- Performance Analysis and Optimization of Centrifugal Variable-Rate Fertilizer Spreaders.- Performance Evaluation and Lag Compensation of Actuators in Variable-Rate Fertilizer Spreaders.- Research on Fertilizer Discharge Flow Rate Detection System.- Field Evaluation and Efficiency Analysis of Centrifugal Variable-Rate Fertilization in Precision Agriculture.

About the author

Dr. Yinyan Shi obtained master's and doctoral degrees in Agricultural Biomechanics and Energy Engineering from Nanjing Agricultural University in 2015 and 2018 respectively. From July 2018 to April 2019, he served as an assistant researcher at the Nanjing Agricultural Mechanization Research Institute. From April 2019 to July 2020, he was a postdoctoral researcher at North Dakota State University. Since July 2020, Dr. Shi has been an associate professor at the College of Engineering of Nanjing Agricultural University. Dr. Shi focuses on research in agricultural engineering, particularly in the fields related to agricultural machinery and equipment. This includes the design and application of agricultural machinery, the development of intelligent detection systems for agricultural machinery, and the exploration of agricultural production optimization through advanced agricultural engineering technologies.

Prof. Xiaochan Wang received his B.E., M.E., and Ph.D. degrees in Agricultural Mechanization from Nanjing Agricultural University in 1991, 1998, and 2003, respectively. He is currently a Zhongshan Distinguished Professor and Ph.D. supervisor at Nanjing Agricultural University, and an Academic Leader of the “Qinglan Project” in Jiangsu Province. His research focuses on electrification, simplification, and intelligent equipment for protected agriculture, as well as agricultural robotics and autonomous systems. His research has been recognized with numerous awards, including the Second Prize of the Higher Education Outstanding Scientific Research Achievement Award (Technological Invention), and top prizes in Jiangsu provincial science and technology awards.

Dr. Man Chen is currently an Associate Research Fellow and Master’s Supervisor at the Nanjing Research Institute for Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, where he plays a key role in the Innovation Team for Intelligent Agricultural Machinery and Equipment. Dr. Chen's research focuses on intelligent agricultural machinery and control technologies, including harvest operation information sensing, machine vision, and deep learning model development and applications.

Dr. Lei Wang received his B.E., M.E., and Ph.D. degree in College of Engineering from Huazhong Agricultural University, Wuhan, China, in 2012, 2015, and 2021, respectively. Since Jul. 2021, Dr. Wang has been in College of Engineering, Nanjing Agricultural University as Lecturer. Dr. Wang focuses on the research of cultivation and sowing techniques and equipment for rapeseed, wheat, and rice. His recent research interests include: precision planter, air-assisted seeding technique, air-assisted fertilizing technique, Variable sowing control system, soil cultivating components, etc.

Dr. Xuekai Huang received his Bachelor's and Master's degrees in Vehicle Engineering from Nanjing Agricultural University in 2019 and 2022, respectively. Since September 2022, he has been pursuing a Ph.D. degree in Agricultural Mechanization Engineering at the same university. His research focuses on the coordinated control of electric tillage equipment and chassis, as well as path-tracking control in protected horticultural environments. His recent work includes intelligent control of agricultural equipment, agricultural robotics, autonomous navigation systems, and vehicle electronic control technologies.

Dr. Zhao Zhang received his B.E. and M.E. degrees in Industrial Engineering and Agricultural Mechanization from Northwest A&F University in 2009 and 2012, respectively, and the Ph.D. degree in Agricultural Engineering from The Pennsylvania State University, USA in 2015. Since Nov. 2021, Dr. Zhang has been in College of Information and Electrical Engineering, China Agricultural University as a Professor.

Summary

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.

Product details

• Assisted by: Man Chen (Editor), Man Chen et al (Editor), Xuekai Huang (Editor), Yinyan Shi (Editor), Lei Wang (Editor), Xiaochan Wang (Editor), Zhao Zhang (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Release: 16.01.2026
• EAN: 9789819543298
• ISBN: 978-981-9543-29-8
• No. of pages: 223
• Illustrations: V, 223 p. 133 illus., 104 illus. in color.
• Series: Smart Agriculture
• Subjects: Natural sciences, medicine, IT, technology > Biology > Agriculture, horticulture; forestry, fishing, food; Regelungstechnik, machine learning, Maschinelles Lernen, Datenbanken, Deep Learning, Automation, Materialwissenschaft, Agriculture, Data Engineering, Sensors and biosensors, Intelligent equipment, hybrid neural network models, Crop phenotypic biological information, Actuator optimization, Variable-rate fertilization, Inversion modeling, Phenotypic sensing systems