Feng Kang
Papers
5
Total Citations
159
H-Index
5
About
Feng Kang is a leading researcher in agricultural robotics and precision agriculture, specializing in the application of deep learning and computer vision for automated orchard management. His work focuses on developing intelligent systems for fruit detection, branch identification, and robotic pruning—critical tasks for modern, labor-efficient farming. Dr. Kang’s most impactful contribution is an improved apple detection method based on lightweight YOLOv4, which achieved 81 citations by effectively addressing challenges like leaf occlusion in complex orchard backgrounds. He has also pioneered branch identification and junction point localization for apple trees using Transformer-based deep learning (31 citations), and developed an image-based system for locating pruning points via instance segmentation and RGB-D data (29 citations). Beyond detection, Dr. Kang designed Monkeybot, a climbing and pruning robot for standing trees in fast-growing forests, and has extended his expertise to grapevine branch recognition for dormant pruning. With over 150 total citations, his work bridges the gap between cutting-edge AI and practical agricultural automation, offering scalable solutions that reduce labor risks and improve efficiency in fruit and forestry production.
Research Focus
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Top Papers
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