Overview
- Introduction to high-tech agriculture
- Focus on Data utilization and decision-making for agriculture automation
- In-depth review on the concept, principle, characteristic, applications of machine vision technologies
Part of the book series: Agriculture Automation and Control (AGAUCO)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (12 chapters)
-
Front Matter
Keywords
About this book
Agricultural automation is the emerging technologies which heavily rely on computer-integrated management and advanced control systems. The tedious farming tasks had been taken over by agricultural machines in last century, in new millennium, computer-aided systems, automation, and robotics has been applied to precisely manage agricultural production system. With agricultural automation technologies, sustainable agriculture is being developed based on efficient use of land, increased conservation of water, fertilizer and energy resources. The agricultural automation technologies refer to related areas in sensing & perception, reasoning & learning, data communication, and task planning & execution. Since the literature on this diverse subject is widely scattered, it is necessary to review current status and capture the future challenges through a comprehensive monograph.
In this book we focus on agricultural automation and provide critical reviews of advanced control technologies, their merits and limitations, application areas and research opportunities for further development. This collection thus serves as an authoritative treatise that can help researchers, engineers, educators, and students in the field of sensing, control, and automation technologies for production agriculture.
Editors and Affiliations
About the editors
Dr. Shaochun Ma is a “Famous Ideological and Political Teacher” of China’s Ministry of Education. He obtained his Ph.D. degree in Agricultural and Biological Engineering from the University of Illinois at Urbana-Champaign. He joined the faculty of the College of Engineering by “Talents Program” of China Agricultural University. His teaching and research focuses on Hydraulic and Pneumatic Transmission, Engineering Fluid Dynamics, machine intelligence for production agriculture, and agricultural systems modelling, simulation, and control.
Dr. Tao Lin is a a ZJU-100 Professor in the College of Biosystems Engineering and Food Science at Zhejiang University. He obtained his Ph.D. and M.S. degrees in Agricultural and Biological Engineering from the University of Illinois at Urbana-Champaign. His research focuses on agricultural big data systems informatics and analytics, ranging from spatiotemporal analysis, remote sending, GIS, optimization modeling analysis, todecision support systems. He has published more than 30 peer-reviewed journal articles.
Prof. Enrong Mao teaches and conducts research on digital design and experimental method for tractor and harvester, automation, hydraulic control, human-factors engineering, and computerized simulation. He served as an Associate Dean of College of Engineering at China Agricultural University, P.R.China during 2002-2019.
Prof. Zhenghe Song teaches and conducts research on design and experimental method for tractor and harvester, automation, hydraulic control, and computerized simulation. He is currently the Dean of College of Engineering at China Agricultural University, P. R. China.
Prof K. C. Ting is Professor and Head Emeritus, Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, USA. His teaching and research emphasize systematic human supervised cyber-physical integration of engineering and agriculture for advancement of complex agricultural production and processing systems. He served as a Vice Dean of International Campus, Zhejiang University, China during 2017-2020.
Bibliographic Information
Book Title: Sensing, Data Managing, and Control Technologies for Agricultural Systems
Editors: Shaochun Ma, Tao Lin, Enrong Mao, Zhenghe Song, Kuan-Chong Ting
Series Title: Agriculture Automation and Control
DOI: https://doi.org/10.1007/978-3-031-03834-1
Publisher: Springer Cham
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-03833-4Published: 08 June 2022
Softcover ISBN: 978-3-031-03836-5Published: 09 June 2023
eBook ISBN: 978-3-031-03834-1Published: 06 June 2022
Series ISSN: 2731-3492
Series E-ISSN: 2731-3506
Edition Number: 1
Number of Pages: VIII, 332
Number of Illustrations: 33 b/w illustrations, 102 illustrations in colour
Topics: Agriculture, Control, Robotics, Mechatronics, Food Science, Statistics, general, Machine Learning