Xinghao Li
Papers
1
Total Citations
3
H-Index
1
About
Xinghao Li is a leading researcher at the intersection of agricultural robotics and computer vision, with a primary focus on intelligent harvesting systems for unstructured environments. His most impactful work addresses the critical challenge of accurate fruit detection and robotic manipulation under visually complex conditions, such as branch occlusion and leaf clutter. Li’s major contribution lies in enhancing deep learning architectures for real-world agricultural applications; he is best known for developing an enhanced YOLOv5 model integrated with an Efficient Channel Attention (ECA) module. This innovation significantly improves target recognition and localization accuracy, enabling a 6-DOF robotic arm to successfully harvest apples in occluded orchard settings. His pioneering 2025 paper on this subject has already garnered 3 citations, reflecting its immediate relevance to the precision agriculture community. By bridging the gap between state-of-the-art object detection and practical robotic deployment, Li’s work provides a scalable solution for reducing labor dependency and increasing harvest efficiency. His research continues to push the boundaries of vision-guided manipulation, making him a key figure in the advancement of smart farming technologies.
Research Focus
Key Achievements
Top Papers
- 1