Kun Zhan
Papers
2
Total Citations
11
H-Index
2
About
Kun Zhan is a researcher at the forefront of agricultural robotics, with a specialized focus on machine vision systems for fruit harvesting. His work primarily addresses the critical challenge of enabling robots to accurately perceive and interact with crops in unstructured orchard environments. Zhan’s major contributions lie in developing recognition and localization methods for pomelo fruit, a task complicated by the fruit’s size, color, and occlusion by foliage. His most cited work, “Recognition of cutting region for pomelo picking robot based on machine vision” (2019, 6 citations), proposes a novel method using the fruit’s centroid and natural pedicle growth characteristics to precisely identify the optimal cutting point for a robotic gripper. Complementing this, his research on “Recognition Methods of Pomelo Fruit Hanging on Trees” (2019, 5 citations) systematically compares classical computer vision techniques—chromatic aberration and K-means clustering—with the modern deep learning approach YOLOv3, providing a valuable benchmark for the field. These studies, though early in their citation life, establish a foundational pipeline for vision-guided pomelo harvesting, directly addressing a key bottleneck in agricultural automation. Zhan’s work is essential reading for students and engineers developing perception systems for specialty crop robotics.
Research Focus
Key Achievements
Top Papers
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- 2