Hongwen Yan
Papers
1
Total Citations
2
H-Index
1
About
Hongwen Yan is a researcher at the forefront of agricultural automation and computer vision, with a primary focus on developing lightweight, high-performance detection models for complex real-world environments. Their most notable contribution is the DSW-YOLO model, an innovative improvement on the YOLOv10n architecture designed specifically for green pepper detection under challenging field conditions. By systematically comparing mainstream lightweight models—including YOLOv5n, YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n—Yan identified the optimal baseline and enhanced it with the novel DWRR block, significantly boosting detection accuracy while maintaining computational efficiency. This work, published in 2025 and already garnering 2 citations, demonstrates Yan’s commitment to bridging the gap between cutting-edge AI and practical agricultural applications. Their research addresses critical challenges in precision farming, enabling real-time fruit detection that can revolutionize harvesting and yield estimation. Yan’s methodical approach to model optimization and their focus on deployable solutions mark them as an emerging leader in the intersection of deep learning and smart agriculture.
Research Focus
Key Achievements
Top Papers
- 1DSW-YOLO-Based Green Pepper Detection Method Under Complex Environments2 citations · 2025