Guantao Xuan

Papers

Total Citations

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About

Guantao Xuan is a researcher advancing precision agriculture through deep learning and intelligent robotics. His primary research areas include computer vision for agricultural automation, lightweight neural network design, and robotic trajectory planning. Xuan’s most impactful contribution is the development of GTCBS-YOLOv5s, a lightweight model for weed species identification in paddy fields, which has garnered 32 citations since 2023. This work addresses the critical need for efficient, real-time weed detection in complex field environments, balancing accuracy with computational efficiency for deployment on resource-constrained devices. Earlier, Xuan tackled challenges in joint robot control by proposing a reverse-driving trajectory planning method using ADAMS, offering a novel solution to inverse kinematics problems and demonstrating his versatility across robotics and agricultural engineering. His research has direct implications for sustainable farming, enabling targeted herbicide application and reducing labor costs. By integrating state-of-the-art object detection with practical agricultural needs, Xuan’s work is paving the way for smarter, more autonomous farming systems.