Jichang Li
Papers
1
Total Citations
32
H-Index
1
About
Jichang Li is a researcher specializing in agricultural artificial intelligence, with a focus on deep learning for precision farming and weed management. His most-cited work, "GTCBS-YOLOv5s: A lightweight model for weed species identification in paddy fields" (2023, 32 citations), introduces a novel adaptation of the YOLOv5s architecture—enhanced with Ghost, Tiny, and CBAM modules—to achieve efficient, real-time weed detection in rice paddies. This contribution addresses a critical challenge in sustainable agriculture: balancing model accuracy with computational efficiency for deployment on resource-constrained devices. By significantly reducing parameter count and computational load while maintaining high detection precision, Li's work enables practical, low-cost solutions for site-specific weed control, reducing herbicide overuse and environmental impact. His research bridges computer vision and agronomy, offering scalable tools for smart farming. With growing recognition in the agricultural AI community, Li’s innovations are paving the way for more accessible, automated crop management systems that empower farmers with data-driven decision-making.
Research Focus
Key Achievements
Top Papers
- 1