Shiyu Lv
Papers
1
Total Citations
8
H-Index
1
About
Shiyu Lv is a rising researcher in agricultural automation and computer vision, whose work addresses critical labor shortages in modern farming through intelligent machinery. Lv’s primary research focuses on developing lightweight, high-performance deep learning models for real-time obstacle detection in unmanned agricultural vehicles. Their most cited paper, “A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles” (2024, 8 citations), introduces a streamlined variant of the YOLOv8 architecture that balances accuracy with computational efficiency—a key requirement for deployment on resource-constrained agricultural robots. This contribution is notable for tackling the dual challenges of aging rural populations and rapid urbanization by enabling safer, more autonomous field operations. While early in their career, Lv’s work has already garnered attention for its practical impact on precision agriculture, demonstrating how optimized neural networks can enhance the reliability of obstacle detection systems. Their research sits at the intersection of computer vision, embedded systems, and agri-robotics, promising to accelerate the adoption of intelligent machinery in farming. As Lv continues to refine these models, their contributions are poised to play a pivotal role in shaping the next generation of autonomous agricultural technologies.
Research Focus
Key Achievements
Top Papers
- 1