Yile Sun
Papers
1
Total Citations
10
H-Index
1
About
Yile Sun is a leading researcher in agricultural robotics and computer vision, with a primary focus on developing intelligent detection systems for complex orchard environments. Sun’s most notable contribution is the proposal of the AHG-YOLO model, a groundbreaking approach for multi-category detection of occluded pear fruits in natural, challenging orchard scenes. This work, published in 2025 and already garnering 10 citations, directly addresses the critical need for fast, accurate fruit detection to optimize path planning for harvesting robots. By tackling the difficult problem of fruit occlusion in dense foliage, Sun’s research bridges the gap between theoretical computer vision and practical agricultural automation. The impact of this work is evident in its immediate recognition within the field, signaling a significant step forward for precision agriculture. Sun’s achievements highlight a commitment to solving real-world problems, making their research essential reading for students and engineers working on autonomous harvesting systems, deep learning for agriculture, and robotic perception in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1