Yongbing Feng
Papers
5
Total Citations
44
H-Index
3
About
Yongbing Feng’s research sits at the intersection of agricultural robotics, precision automation, and systems engineering, with a focus on solving real-world challenges in complex environments. His most cited work, “Recognition and Detection of Greenhouse Tomatoes in Complex Environment” (2022, 24 citations), advances computer vision for harvesting robots by enhancing YOLO v5 with data augmentation to improve detection accuracy under variable lighting and occlusion. This contribution directly supports the development of reliable picking robots for greenhouse agriculture. Feng also explores model-based systems engineering (MBSE) for stakeholder value networks, proposing methods to capture multi-stakeholder needs in vertical farming and automated milking systems—work that bridges technical design with user requirements. His designs for a dexterous milking manipulator (2024, 5 citations) and an outside needle pipe climbing robot (2022, 3 citations) demonstrate his versatility in mechanical structure and analysis, tackling applications from dairy automation to industrial inspection. With a growing citation record and a portfolio spanning vision, robotics, and requirements engineering, Feng’s work is shaping the next generation of intelligent agricultural and industrial systems.
Research Focus
Key Achievements
Top Papers
- 1Recognition and Detection of Greenhouse Tomatoes in Complex Environment24 citations · 2022
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