Xingpeng Lei
Papers
1
Total Citations
29
H-Index
1
About
Xingpeng Lei is a researcher at the forefront of precision agriculture and computer vision, specializing in deep learning for weed detection and crop management. His major contribution lies in developing lightweight, efficient models that bridge the gap between high-accuracy AI and real-world field deployment. His most-cited work, "YOLOv8-ECFS: A lightweight model for weed species detection in soybean fields" (2024), has already garnered 29 citations, reflecting its immediate impact on sustainable farming practices. By optimizing the YOLOv8 architecture with enhanced feature extraction and channel pruning, Lei’s model achieves robust weed identification while minimizing computational demands—a critical step toward affordable, real-time agricultural robotics. This work not only advances smart farming but also addresses the pressing need for eco-friendly weed management. Lei’s research exemplifies how tailored AI solutions can transform agriculture, making him a rising voice in the intersection of machine learning and environmental sustainability.
Research Focus
Key Achievements
Top Papers
- 1