Papers
1
Total Citations
28
H-Index
1
About
Shilong Yu is a leading researcher in agricultural robotics and computer vision, with a focus on intelligent fruit harvesting systems. His work bridges deep learning and 3D perception to address critical bottlenecks in automated agriculture. Yu’s most cited study, “Tomato Recognition and Localization Method Based on Improved YOLOv5n-seg Model and Binocular Stereo Vision” (2023, 28 citations), tackles the dual challenges of high model complexity and low stereo-matching accuracy in fruit detection. By developing a lightweight segmentation model integrated with binocular vision, he demonstrated a practical, high-precision solution for real-time tomato recognition and localization—a key enabler for autonomous picking robots. This contribution stands out for its engineering pragmatism, reducing computational overhead while maintaining robust performance in cluttered greenhouse environments. Yu’s work is widely cited by researchers optimizing neural networks for agricultural applications and advancing stereo vision algorithms for unstructured settings. His research not only advances the field of precision agriculture but also provides a scalable framework for deploying AI-driven automation in crop harvesting, directly impacting food production efficiency and labor sustainability.
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Top Papers
- 1