Sensing Technologies for Field and In-House Crop Production: Technology Review and Case Studies

ISBN-13: 9789819979264 / Angielski

Man Zhang; Han Li; Wenyi Sheng

Spis treści

A Review of Three-Dimensional Multispectral Imaging in Plant Phenotyping.- Recent Advances in Soil Nutrient Monitoring: A Review.- Plant phenotyping robot platform.- Autonomous crop image acquisition system based on ROS system.- SeedingsNet: Field wheat seedling density detection based on deep learning.- Wheat lodging detection using smart vision-based method.- Design, construction, and experiment-based key parameter de-2 termination of auto maize seed placement system.- Development and test of an auto seedling detection System.

O autorze

Dr. Man Zhang is the dean of College of Information and Electric Engineering, China Agricultural University. Her main research areas are plant phenotyping and agricultural machinery navigation.

Dr. Han Li is an associate professor at the College of Information and Electric Engineering, China Agricultural University. Her studies mainly focus on plant phenotyping and robotics.

Dr. Wenyi Sheng is an assistant professor at the College of Information and Electric Engineering, China Agricultural University. Her studies mainly focus on the monitoring of soil conditions.

Dr. Ruicheng Qiu is an associate professor at the College of Information and Electric Engineering, China Agricultural University. He mainly works on crop phenotyping.

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 November 2021, Dr. Zhang has been in College of Information and Electrical Engineering, China Agricultural University, as a professor.

Blurb

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.