Papers
3
Total Citations
41
H-Index
3
About
Xinyuan Lu is a leading researcher in agricultural robotics and intelligent automation, with a focus on developing advanced computer vision systems for precision harvesting. Their work addresses critical challenges in non-structural environments, where fruit detection is complicated by near-color backgrounds, occlusion, and variable lighting. Lu’s major contributions include pioneering lightweight deep learning models for fruit recognition, such as YOLO-GEW for “Yuluxiang” pears and an improved YOLOv5 for cucumbers, which significantly enhance detection speed and accuracy while reducing computational demands. These innovations have direct implications for intelligent harvesting robots, improving their ability to operate in complex greenhouse settings. With over 40 citations across their most-cited works, Lu’s research demonstrates tangible impact in agricultural automation. Notably, their 2019 study on queue length optimization for compact robotic parking systems showcases versatility in applying performance improvement techniques to robotics beyond agriculture. Lu’s work represents a critical step toward scalable, efficient robotic solutions for real-world agricultural challenges, making them a key figure in the intersection of AI, robotics, and sustainable farming.
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