Papers
3
Total Citations
81
H-Index
3
About
Shang Chen is a leading researcher in agricultural robotics and computer vision, with a primary focus on the automated harvesting of Camellia oleifera fruit. His major contributions lie in developing advanced deep learning and image processing algorithms to overcome the significant challenge of fruit occlusion in natural orchard environments. Chen pioneered the use of fusion clustering techniques combined with an improved YOLOv5 algorithm, achieving robust detection even when fruits are heavily obscured by leaves or branches. His work also integrates binocular stereo vision with LBP texture matching for precise 3D localization, enabling accurate robotic picking. His most influential paper, "Study on fusion clustering and improved YOLOv5 algorithm based on multiple occlusion of Camellia oleifera fruit," has garnered 69 citations, underscoring its impact on the field. Chen’s research directly addresses a critical bottleneck in agricultural automation, providing practical solutions that enhance the efficiency and reliability of fruit harvesting systems. His innovative approach to combining clustering methods with state-of-the-art object detection models represents a notable achievement, positioning him as a key contributor to the advancement of smart agriculture and precision horticulture.
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