Advanced Sensing and Robotics Technologies in Smart Agriculture

Dr. Yuliang Yun received his B.E. and M.E. degrees in Measurement and Control Technology and Instruments from Shandong University of Science and Technology, Qingdao, Shandong, China, in 2004 and 2007, respectively. He obtained his Ph.D. degree in Agricultural Engineering from China Agricultural University, Beijing, China, in 2015. Since November 2018, Dr. Yun has been serving as an associate professor at the College of Mechanical and Electrical Engineering, Qingdao Agricultural University.Dr. Wenyi Sheng obtained his bachelor's degree in Electronics and Information Engineering and a doctoral degree in Agricultural Engineering, both from China Agricultural University in Beijing, China, in 2008 and 2013, respectively. Following the completion of his Ph.D., he held a position at the Institute of Intelligent Machines, China Academy of Sciences from 2013 to 2014. Subsequently, Dr. Sheng worked as a postdoctoral fellow at the Department of Plants, Soils, and Climate, Utah State University in Logan, Utah, USA, from 2014 to 2018. In 2019, he joined the College of Information and Electrical Engineering at China Agricultural University as an assistant professor.Dr. Zhao Zhang received his B.E. and M.E. degrees in Industrial Engineering and Agricultural Mechanization from Northwest A&F University, China 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 at China Agricultural University as a professor.

• Research Progress on Seed Appearance Recognition for Major Crops.- A review of corn growth status sensing methods.- Greenhouse Phenotyping Measurement Techniques and Systems: A Review.- On-site Intelligent Grading System for Fruits and Vegetables: Case Studies on Tomato and Apple.- Recent advances in intelligent harvesting robots.- Infield honeysuckle detection based on improved YOLOv5s under natural lighting.- Multiscale wheat lodging parameter detection based on MobilenetV3.