Yongzong Lu
Papers
1
Total Citations
2
H-Index
1
About
Yongzong Lu is a leading researcher in agricultural robotics and computer vision, with a primary focus on intelligent selective harvesting systems. His most impactful work centers on the robust detection of dense, small tea shoots under challenging field conditions, including occlusion and bud-leaf similarity across multiple cultivars. Lu’s major contribution is the development of YOLOv7-LEES, a state-of-the-art deep learning model that integrates Efficient Channel Attention, Explicit Visual Center schemes, SIoU loss, and the lightweight RepNCSPELAN4 architecture. This innovation achieves real-time detection at 116.3 FPS while reducing model parameters by 12.9% and computational cost by 8.3%, making it highly suitable for field-robot deployment. His work addresses a critical bottleneck in automated tea harvesting, where traditional methods fail due to the small size, dense clustering, and shading of target shoots. With 2 citations already for his 2025 publication, Lu’s research is gaining rapid recognition for its practical impact on precision agriculture. His achievements demonstrate a unique ability to bridge advanced computer vision techniques with real-world agricultural challenges, positioning him as a key innovator in the development of intelligent, selective harvesting systems for specialty crops.
Research Focus
Key Achievements
Top Papers
- 1