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About

Yi Xun is a leading researcher in agricultural robotics, specializing in computer vision and machine learning for autonomous fruit and flower harvesting. His work addresses the critical challenge of enabling robots to accurately detect, track, and localize crops in complex, natural environments. Xun’s major contributions include developing a recognition and 3D localization system for robotic harvesting of Hangzhou White Chrysanthemums, a culturally and economically significant crop, as well as a segmentation algorithm using least squares support vector machines (LS-SVM) to improve visual positioning in natural light. He has also advanced the field with a tracking and recognition algorithm for oscillating apples, mitigating the effects of wind-induced fruit movement, and a superpixel-based segmentation method for mature citrus under variable illumination. With over 44 citations across his most-cited works, Xun’s research is foundational for the next generation of precision harvesting robots, reducing reliance on manual labor and increasing agricultural efficiency. His innovative fusion of affine transformations, superpixel techniques, and robust segmentation models marks him as a key figure in the transition toward fully autonomous crop harvesting.