Chao Xue

Papers

Total Citations

211

H-Index

About

Chao Xue is a leading researcher in agricultural robotics and computer vision, specializing in intelligent fruit harvesting systems. His work focuses on overcoming the challenges of automated fruit recognition and branch detection in complex field environments, particularly for litchi—a fruit with small, easily damaged branches that make robotic picking difficult. Xue’s major contributions include developing deep learning-based semantic segmentation models, such as the DeepLabV3+ architecture for precise litchi branch detection (162 citations), and improving fruit detection accuracy under challenging conditions like variable lighting and occlusion using an enhanced YOLOv3 model (17 citations). He has also advanced multi-class fruit recognition by combining machine vision with Support Vector Machines (32 citations), aiming to reduce costs and increase adaptability for picking robots. Xue’s work has been instrumental in bridging the gap between computer vision algorithms and practical agricultural automation, directly addressing real-world constraints in field robotics. His research has garnered significant attention, with his most cited paper alone accumulating over 160 citations, reflecting its impact on the precision agriculture community. Xue’s achievements are paving the way for more efficient, damage-free robotic harvesting systems.