Zhenhui Tang
Papers
1
Total Citations
7
H-Index
1
About
Zhenhui Tang is a researcher focused on agricultural robotics and computer vision, with particular expertise in the automated detection and harvesting of Camellia oleifera fruit. Their most cited work, "Detection and Positioning of Camellia oleifera Fruit Based on LBP Image Texture Matching and Binocular Stereo Vision" (2023, 7 citations), introduces a novel approach combining YOLOv7 deep learning with binocular stereo vision systems to achieve rapid fruit recognition and precise spatial positioning in natural environments. This contribution addresses a critical challenge in specialty crop automation—enabling accurate picking in complex, unstructured orchard settings. Tang’s research integrates texture analysis, neural network training, and 3D vision to improve both detection speed and localization accuracy, advancing the practical deployment of intelligent harvesting robots. Their work has garnered attention for its potential to reduce labor costs and increase efficiency in Camellia oleifera cultivation, a crop of significant economic importance in Asia. Tang’s ongoing contributions continue to bridge the gap between deep learning algorithms and real-world agricultural applications.
Research Focus
Key Achievements
Top Papers
- 1