Papers
16
Total Citations
486
H-Index
10
About
Zenghong Ma is a leading researcher in agricultural robotics and computer vision, whose work sits at the intersection of precision agriculture and intelligent automation. With a career spanning over a decade, Ma has made significant contributions to the development of machine learning and deep learning methodologies tailored specifically for crop detection, robotic harvesting, and field automation. Ma is perhaps best known for pioneering YOLO-based detection frameworks adapted for agricultural applications, including DSW-YOLO for strawberry fruit detection under varying occlusion conditions (107 citations) and STRAW-YOLO for simultaneous fruit and keypoint detection. His 2023 comprehensive review of core agricultural robot technologies (92 citations) has become an essential reference for researchers entering the field. Notable work on tomato 3D pose estimation and real-time strawberry stalk detection further demonstrates his commitment to solving practical harvesting challenges. Earlier foundational contributions include machine-vision-based crop positioning for intra-row weeding robots (2015) and paddy field root row detection, highlighting the longevity and consistency of his research focus. Collectively accumulating over 460 citations, Ma's body of work reflects substantial influence on advancing autonomous agricultural systems and positions him as a key figure shaping the future of smart farming technologies.
Research Focus
Key Achievements
Top Papers
- 1
- 2A review of core agricultural robot technologies for crop productions92 citations · 2023
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- 6STRAW-YOLO: A detection method for strawberry fruits targets and key points38 citations · 2025
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- 9Crop positioning for robotic intra-row weeding based on machine vision20 citations · 2015
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