Sunan Chen

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About

Sunan Chen is an emerging researcher specializing in agricultural robotics, computer vision, and autonomous harvesting systems, with a focus on developing intelligent solutions for challenging real-world fruit detection and manipulation tasks. His work addresses critical bottlenecks in precision agriculture, particularly the deployment of robotic systems capable of operating under variable lighting conditions, complex occlusions, and resource-constrained edge computing environments. Chen's most notable contribution is YOLOv10n-CGD, a lightweight yet highly accurate dragon fruit detection framework tailored for harvesting robots, which has garnered 7 citations since its 2025 publication — a strong early indicator of its relevance to the field. Complementing this, his development of the XN-RRT* algorithm introduces a statistically informed path planning approach that significantly improves robotic arm efficiency in densely structured pitaya environments, earning 4 citations. His universal visual detection framework for *Camellia oleifera* further demonstrates his commitment to versatile, multi-crop robotic solutions. Collectively, Chen's research bridges deep learning, motion planning, and edge deployment, positioning him as a promising contributor to the next generation of intelligent agricultural automation systems. Students exploring smart farming robotics will find his work both practically grounded and methodologically innovative.