Zezhen Li

Papers

Total Citations

H-Index

About

Zezhen Li is a rising researcher at the intersection of computer vision and precision agriculture, with a primary focus on developing efficient, real-time object detection algorithms for smart farming applications. Their most cited work, "YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5" (2023, 40 citations), addresses a critical bottleneck in agricultural automation: the challenge of detecting small, ripe fruits in complex field environments under hardware constraints. Li’s key contribution lies in enhancing the YOLOv5 architecture with an Adaptive Spatial Feature Fusion (ASFF) mechanism, significantly improving detection accuracy for occluded and small-scale targets without sacrificing inference speed. This work has direct implications for robotic harvesting and yield estimation, offering a practical solution for deploying intelligent monitoring on resource-limited edge devices. By tackling the trade-off between model performance and computational efficiency, Li’s research helps bridge the gap between deep learning theory and real-world agricultural deployment. Their work is particularly notable for demonstrating that state-of-the-art detection can be achieved on modest hardware, making smart farming more accessible. With 40 citations in a short period, Li’s algorithm is gaining traction among researchers developing vision systems for fruit detection and automated agriculture.