Papers
1
Total Citations
76
H-Index
1
About
Zedong Wu is a leading researcher in agricultural robotics and computer vision, with a focus on intelligent monitoring systems for precision agriculture. His work centers on developing deep learning-based object detection and tracking algorithms to automate crop management, particularly for greenhouse and field-grown tomatoes. Wu’s most cited paper, “Tracking and Counting of Tomato at Different Growth Period Using an Improving YOLO-Deepsort Network for Inspection Robot” (2022, 76 citations), introduces a novel YOLO-Deepsort framework that integrates ShuffleNetV2 and CBAM attention mechanisms into YOLOv5s. This innovation enables real-time identification and counting of tomatoes across multiple growth stages, directly supporting yield prediction and robotic inspection. By optimizing lightweight architectures for embedded systems, Wu’s contributions address critical challenges in agricultural automation—balancing accuracy with computational efficiency for field deployment. His work has significant implications for smart farming, reducing labor costs while improving crop monitoring precision. With growing citation impact, Wu continues to advance the intersection of computer vision and agricultural robotics, making him a key figure in the development of autonomous inspection systems for sustainable food production.
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Top Papers
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