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About

Leiyang Fu is a leading researcher in agricultural robotics, specializing in autonomous navigation, path planning, and visual perception for precision farming. His work addresses critical challenges in crop management, from weeding to harvesting. Fu’s most influential contribution is a novel semantic segmentation framework based on an ensemble of UNet networks, which enhances environmental awareness for agricultural robots, earning 17 citations. He also developed a quadratic traversal algorithm for shortest weeding path planning in cornfields (16 citations), significantly improving weeding efficiency and crop protection. In tea harvesting, Fu introduced the Adaptive Step RRT* algorithm for robotic arm path planning (10 citations) and a vision-based localization method using RGB-D fusion to accurately identify picking points in unstructured environments (5 citations). His recent work on a cost calculation method for manipulator trajectory planning (2 citations) further advances robotic efficiency. With a total of 50 citations across his top papers, Fu’s research is pivotal in enabling autonomous, safe, and efficient agricultural operations, directly impacting the future of smart farming and sustainable agriculture.