Papers
6
Total Citations
685
H-Index
5
About
Pan Fan is a leading researcher in agricultural robotics, specializing in computer vision and intelligent grasping for fruit harvesting. His core contributions lie in developing real-time, high-accuracy apple detection and robotic picking systems. Fan’s most influential work, a 2021 paper on an improved YOLOv5 for apple target detection, has garnered 564 citations, revolutionizing how picking robots distinguish occluded apples from branches and other fruit. He further advanced the field with a multi-feature patch-based segmentation technique (30 citations) that robustly handles halation and shadows, and a novel biomimetic mechanical hand (22 citations) that minimizes fruit damage through negative-pressure air suction. His research also includes three-finger grasp planning for robotic harvesting and, most recently, a lightweight CenterNet model for enhanced detection in complex orchard environments (2024). By integrating large kernel convolutions and residual connections into robotic grasping technology, Fan continues to push the boundaries of real-time, damage-free harvesting. His work directly addresses the critical challenges of speed, accuracy, and reliability in agricultural automation, making him a pivotal figure in the transition toward intelligent, sustainable farming.
Research Focus
Key Achievements
Top Papers
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