Yongguang Hu
Papers
1
Total Citations
2
H-Index
1
About
Yongguang Hu is a leading researcher in agricultural robotics and intelligent harvesting systems, with a primary focus on precision detection and selective picking of specialty crops. His most cited work centers on developing robust, real-time computer vision algorithms for identifying dense, small tea shoots under challenging field conditions, including occlusion and high visual similarity between buds and leaves. In a landmark 2025 study, Hu introduced YOLOv7-LEES, a lightweight deep-learning detector that integrates Efficient Channel Attention, Explicit Visual Center schemes, and SIoU loss within a RepNCSPELAN4 backbone. This innovation achieves 116.3 frames per second while reducing model parameters by 12.9% and computational cost by 8.3%, enabling practical deployment on field robots. His contributions directly address the bottleneck of selective harvesting in tea cultivation, where traditional methods fail under dense canopy and variable lighting. With growing citation impact, Hu’s work is shaping the next generation of intelligent agricultural machinery, bridging the gap between computer vision research and real-world farming automation.
Research Focus
Key Achievements
Top Papers
- 1