Kuifan Chen
Papers
1
Total Citations
8
H-Index
1
About
Kuifan Chen is a leading researcher in precision agriculture and computer vision, with a focus on deploying lightweight deep learning models for real-time, resource-constrained environments. His most cited work, "Optimized YOLOv5s-Im for real-time apple flower detection in drone-based pollination" (2025, 8 citations), introduces a novel, optimized YOLOv5s-Im architecture that dramatically improves detection accuracy and speed for apple flowers. Chen’s major contribution lies in enabling robust, real-time performance on drone-based pollination systems, achieving significantly more pollination attempts than prior methods. He validated this model through practical deployment across diverse, resource-limited platforms, bridging the gap between theoretical AI and field-ready agricultural robotics. This work is pivotal for advancing automated pollination, addressing critical labor shortages and food security challenges. Chen’s research has already garnered attention for its practical impact, and his innovative approach to model optimization continues to inspire new directions in edge-AI for agriculture.
Research Focus
Key Achievements
Top Papers
- 1