Hongdu Zhang
Papers
4
Total Citations
76
H-Index
3
About
Hongdu Zhang is a leading researcher at the intersection of smart agriculture and robotic perception, with a primary focus on developing lightweight, real-time computer vision and path-planning algorithms for autonomous picking robots. His most impactful work, "GA-YOLO: A Lightweight YOLO Model for Dense and Occluded Grape Target Detection" (29 citations), introduces a novel detection framework that overcomes the critical challenges of missed detections and slow processing speeds in cluttered orchard environments—a key bottleneck for practical agricultural robotics. Zhang also made significant contributions to mobile robot navigation, notably through his "three-neighbor search A* algorithm combined with artificial potential field" (26 citations), which dramatically reduces computational time and search nodes in path planning. His 2022 study on "Image restoration and detection method for picking robot based on convolutional auto-encoder" (19 citations) further advances robust fruit detection under degraded visual conditions. Collectively, Zhang’s work has accumulated over 76 citations, establishing him as an influential voice in smart agriculture. His research directly enables the next generation of efficient, vision-guided harvesting robots, making him a vital contributor to the future of automated farming.
Research Focus
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Top Papers
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