Rundong Yetan
Papers
1
Total Citations
36
H-Index
1
About
Rundong Yetan has made pioneering contributions at the intersection of computer vision and agricultural robotics, with a primary focus on intelligent fruit detection in complex natural environments. His most impactful work, the YOLOv5-Litchi model (2022, 36 citations), addresses a critical challenge in precision agriculture: accurately detecting litchis under variable lighting, occlusion, and dense foliage conditions. By enhancing the standard YOLOv5 architecture with specialized convolutional layers, Yetan’s model significantly improves detection accuracy and speed, providing reliable visual guidance for autonomous litchi-picking robots. This innovation directly supports yield estimation and harvesting efficiency, reducing reliance on manual labor. Yetan’s research exemplifies how deep learning can be tailored to real-world agricultural constraints, bridging the gap between laboratory algorithms and field deployment. His work has been cited by researchers developing similar detection systems for other fruits, demonstrating its cross-commodity impact. As a rising scholar in agri-robotics, Yetan continues to advance the frontier of intelligent perception systems that enable sustainable, automated farming practices.
Research Focus
Key Achievements
Top Papers
- 1