Zhexing Sun

Kunming University of Science and Technology

Papers

Total Citations

H-Index

About

Zhexing Sun is a leading researcher in agricultural robotics and computer vision, specializing in real-time fruit detection for automated harvesting systems. His primary research focuses on developing lightweight, high-accuracy deep learning algorithms for detecting Xiaomila green peppers (Capsicum frutescens L.) under complex orchard conditions. Sun’s major contributions include pioneering improved YOLOv5s and YOLOv7 architectures that address critical challenges such as dense fruit distributions, occlusion, and variable lighting—enabling practical deployment on harvesting robots. His most-cited work, “Xiaomila Green Pepper Target Detection Method under Complex Environment Based on Improved YOLOv5s” (2022, 20 citations), demonstrates a novel approach to balancing detection speed and accuracy in real-world agricultural settings. A subsequent study, “Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm” (2023, 18 citations), further reduced computational costs while enhancing detection precision for occluded targets. With a combined 38 citations across his top papers, Sun’s work is gaining traction among researchers developing intelligent harvesting systems. His achievements highlight a commitment to bridging the gap between advanced computer vision and practical agricultural automation, making him a notable contributor to the field of precision agriculture.