Hewei Zhang
Papers
1
Total Citations
12
H-Index
1
About
Hewei Zhang is making impactful strides at the intersection of computer vision and sustainable agriculture, with a primary focus on deep learning-based object detection for precision farming. His most cited work, "PD-YOLO: a novel weed detection method based on multi-scale feature fusion" (2025), addresses a critical challenge in automated weeding: accurately identifying weeds amid complex field environments. By developing a YOLO-based architecture enhanced with multi-scale feature fusion, Zhang improves detection robustness across varying weed sizes and occlusions—a key step toward deploying reliable robotic weeders that can reduce herbicide use and labor demands. This paper has already garnered 12 citations shortly after publication, signaling strong interest from both agricultural robotics and computer vision communities. Zhang’s research directly supports the vision of sustainable, automated agriculture, where vision-based systems enable precise, real-time weed management. His work exemplifies how cutting-edge AI techniques can be tailored to solve real-world agricultural bottlenecks, making him a promising voice in the growing field of deep learning for precision agriculture.
Research Focus
Key Achievements
Top Papers
- 1PD-YOLO: a novel weed detection method based on multi-scale feature fusion12 citations · 2025