Papers
16
Total Citations
830
H-Index
9
About
Fuzeng Yang is a prominent researcher specializing in agricultural robotics, with particular expertise in fruit harvesting automation, computer vision, and multi-robot systems. Based at the intersection of precision agriculture and intelligent robotics, Yang's work has fundamentally advanced the field of apple-picking robotics through innovative solutions to longstanding technical challenges. Yang's most celebrated contribution is a real-time apple detection method based on an improved YOLOv5 architecture, which uniquely addresses occlusion challenges caused by branches and overlapping fruit — a problem that had long hindered practical deployment of harvesting robots. This landmark 2021 paper has accumulated an impressive 564 citations, underscoring its transformative impact on agricultural AI. Complementing this vision work, Yang has developed sophisticated grasping strategies, biomimetic mechanical hands inspired by apple physical properties, and integrated LiDAR/IMU/GNSS navigation systems for complex orchard environments. Beyond individual robot design, Yang has made significant contributions to multi-robot coordination frameworks, exploring how collaborative robot groups can improve efficiency in spraying, dosing, and harvesting operations. With research spanning over a decade — from early visual navigation systems to cutting-edge cooperative robot architectures — Yang's body of work represents a comprehensive roadmap for the intelligent automation of modern orchard agriculture.
Research Focus
Key Achievements
Top Papers
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- 3Research Progress on Synergistic Technologies of Agricultural Multi-Robots49 citations · 2021
- 4Development of a Combined Orchard Harvesting Robot Navigation System41 citations · 2022
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