Papers
24
Total Citations
589
H-Index
13
About
Daobilige Su is a versatile robotics and artificial intelligence researcher whose work spans agricultural robotics, multisensor perception, and acoustic signal processing. His most significant contributions lie at the intersection of computer vision and precision agriculture, where his research on deep learning-based semantic segmentation and crop-weed classification has garnered over 129 citations, establishing him as a prominent voice in AI-driven farming solutions. His development of systems like LettuceTrack and real-time ryegrass detection pipelines demonstrates a sustained commitment to making agricultural robotics practical and ecologically responsible by reducing unnecessary chemical usage. Beyond agriculture, Su has made meaningful advances in 3D human tracking for mobile robots by fusing visual and ultrasonic sensing modalities, work that has accumulated nearly 100 citations across related publications. His research also extends into robot audition, tackling the challenging problem of SLAM-based microphone array calibration and 3D sound source localization — areas where he has produced theoretically rigorous work on observability conditions. Earlier contributions in probabilistic pipeline inspection mapping further illustrate his methodological breadth. Recognized as an editorial leader in AI and plant phenotyping, Su's body of work reflects a rare ability to bridge fundamental sensor fusion theory with high-impact real-world robotic applications.
Research Focus
Key Achievements
Top Papers
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- 3Real time detection of inter-row ryegrass in wheat farms using deep learning58 citations · 2021
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- 7Real-time 3D human tracking for mobile robots with multisensors28 citations · 2017
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- 9Towards real-time 3D sound sources mapping with linear microphone arrays25 citations · 2017
- 10Learning spatial correlations for Bayesian fusion in pipe thickness mapping24 citations · 2014