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About

Yinggang Shi is a prominent researcher in agricultural robotics and precision agriculture, whose work sits at the intersection of machine vision, autonomous navigation, and robotic manipulation for horticultural applications. His research focuses on developing intelligent robotic systems for greenhouse and field environments, with particular emphasis on fruit picking, crop pollination, and weed management. Shi's most celebrated contributions include the design of a lightweight robotic arm for kiwifruit pollination (67 citations) and an improved YOLO v4-based weed detection system for carrot fields (62 citations), demonstrating his ability to translate deep learning advances into practical agricultural tools. His development of force-sensing tomato picking arms and innovative "global-local" visual servo systems reflects a sophisticated understanding of the dexterity challenges inherent in automated harvesting. Notably, his laser SLAM-based autonomous navigation system for greenhouse tomato picking robots (29 citations) represents a significant step toward fully autonomous horticultural operations. Across his body of work, Shi consistently addresses real-world agricultural constraints — lightweight design, precision positioning, and robust navigation in unstructured environments — accumulating over 275 citations. His research offers students and engineers a compelling roadmap for advancing smart farming technologies in modern horticulture.