Papers
9
Total Citations
254
H-Index
7
About
Driven by the urgent need to accelerate crop breeding for a growing global population, Zhengqiang Fan has pioneered the integration of robotics and high-throughput phenotyping (HTP) for precision agriculture. His research focuses on developing autonomous field robots and advanced sensing systems—particularly 3D LiDAR and RGB-D cameras—to non-destructively measure critical crop traits. Fan’s most impactful contribution is the “Phenomobile,” a field-based HTP system that generates 3D LiDAR point clouds for maize phenotyping, enabling rapid, accurate trait assessment at scale (118 citations). He also introduced the Extended Ackerman Steering Principle for coordinated four-wheel-drive agricultural robots (54 citations), advancing robust field navigation. His work on in situ stem diameter measurement using HTP robots (18 citations) and multi-beam LiDAR calibration for agricultural robots (13 citations) addresses key bottlenecks in field phenotyping, such as operating in tight spaces and variable lighting. More recently, Fan has explored TinyML-enabled IoT frameworks for weed classification, pushing toward sustainable, low-power edge intelligence. With over 200 total citations, his contributions are shaping the next generation of autonomous, data-driven crop breeding tools.
Research Focus
Key Achievements
Top Papers
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- 5Intrinsic Calibration of Multi-Beam LiDARs for Agricultural Robots13 citations · 2022
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