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About

Jianjun Yin is a leading researcher in agricultural robotics, with a focus on vision-based automation for fruit harvesting and unmanned agricultural vehicles. His work centers on developing intelligent systems that enable robots to perceive, navigate, and interact with crops in natural, unstructured environments. Yin’s most impactful contribution is a vision-based method for judging tomato maturity under growth conditions (25 citations), which provides critical data for harvesting robot control strategies. He has also advanced the understanding of collision-mechanical properties of tomatoes during robotic gripping (15 citations), informing safer and more efficient end-effector design. More recently, Yin introduced a novel lightweight YOLOv8-PSS model for obstacle detection on the paths of unmanned agricultural vehicles (8 citations), addressing the pressing need for highly intelligent and accurate agricultural equipment in the face of rural urbanization and labor shortages. His other notable work includes trajectory learning for tracked robots, fruit image segmentation, and obstacle-avoidance path planning for tomato-picking robot arms. With a career spanning over a decade, Yin’s research has laid foundational knowledge for the next generation of autonomous agricultural systems, directly impacting the efficiency and sustainability of modern farming.