Yuanhui Hu
Papers
1
Total Citations
22
H-Index
1
About
Yuanhui Hu is a researcher focused on advancing computer vision and deep learning techniques for agricultural applications, particularly in precision agriculture and fruit detection. Their major contribution lies in developing lightweight, high-accuracy detection models tailored for complex orchard environments. Hu’s most-cited work, "Improved YOLOv7-Tiny Complex Environment Citrus Detection Based on Lightweighting" (2023, 22 citations), addresses critical challenges such as variable lighting, branch occlusion, and fruit overlap that hinder traditional detection methods. By proposing the YOLO-DCA model—an enhancement of YOLOv7-tiny that incorporates depth-separable convolution (DWConv) for reduced computational load—Hu achieved a balance between efficiency and robustness, enabling real-time citrus detection in challenging field conditions. This work demonstrates Hu’s commitment to making AI-driven agricultural tools more accessible and practical for real-world deployment. With a growing citation record, Hu’s research holds promise for improving automated harvesting, yield estimation, and crop management, contributing to the broader goal of sustainable and technology-driven agriculture.
Research Focus
Key Achievements
Top Papers
- 1