Mingliang Wu
Papers
3
Total Citations
153
H-Index
3
About
Mingliang Wu is a leading researcher in agricultural robotics and computer vision, specializing in the automated harvesting of Camellia oleifera fruit. His work addresses the formidable challenge of detecting and positioning fruits in complex orchard environments, where occlusion by leaves, color similarity between foliage and fruit, and simultaneous flowering create significant ambiguities. Wu’s major contributions lie in developing fusion algorithms that combine classical image processing with state-of-the-art deep learning models. His most-cited paper, “Adaptive Active Positioning of Camellia oleifera Fruit Picking Points: Classical Image Processing and YOLOv7 Fusion Algorithm” (2022, 77 citations), introduces a novel approach to mitigate flower loss caused by picking shock. He further advanced the field with “Study on fusion clustering and improved YOLOv5 algorithm based on multiple occlusion” (2023, 69 citations), tackling severe occlusion scenarios. His work on binocular stereo vision and LBP texture matching (2023) demonstrates a comprehensive, multi-modal strategy for precise fruit localization. With over 150 combined citations, Wu’s research is critical for developing gentle, efficient harvesting robots, directly impacting the productivity of the Camellia oleifera oil industry.
Research Focus
Key Achievements
Top Papers
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