Zuoliang Tang
Papers
5
Total Citations
132
H-Index
4
About
Zuoliang Tang is a prominent researcher specializing in agricultural robotics, computer vision, and intelligent automation, with a particular focus on revolutionizing citrus harvesting through cutting-edge technology. His work sits at the intersection of deep learning-based detection and robotic motion planning, addressing real-world challenges in complex, unstructured orchard environments. Tang's most influential contribution, "Real-time and accurate detection of citrus in complex scenes based on HPL-YOLOv4" (2022, 67 citations), demonstrates his expertise in adapting advanced neural network architectures for precision fruit detection. Complementing this, his development of the time-optimal RRT (TO-RRT) algorithm for manipulator motion planning (31 citations) and artificial potential field-based collision-free planning for six-link manipulators (18 citations) showcases his systems-level approach to robotic harvesting efficiency and safety. His more recent work, YOLOC-tiny (2024), reflects his commitment to developing lightweight, generalizable models capable of detecting multi-ripeness citrus varieties under challenging field conditions. Earlier research incorporating binocular vision and immune algorithms further highlights his interdisciplinary methodology. Collectively, Tang's publications have accumulated over 130 citations, establishing him as a meaningful contributor to the emerging field of smart agricultural robotics and automated fruit harvesting systems.
Research Focus
Key Achievements
Top Papers
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