Qiyuan Xue

Papers

Total Citations

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About

Qiyuan Xue is a researcher at the forefront of agricultural robotics and autonomous navigation, with a focus on precision agriculture and environmental perception. Their work addresses critical challenges in complex outdoor environments, particularly in orchards and fields. Xue’s most notable contribution is the development of an improved YOLOv7 model for real-time weed detection, achieving 13 citations by enhancing feature extraction and fusion to identify weeds in complex field backgrounds. This work directly supports sustainable farming by enabling targeted herbicide application. In simultaneous localization and mapping (SLAM), Xue proposed LeGO-LOAM-FN, a novel method that fuses Faster_GICP and NDT algorithms to solve cumulative mapping errors in large, feature-similar orchard environments. This 2024 work, with 5 citations, significantly improves robot navigation stability under challenging conditions. Xue’s research integrates deep learning with advanced sensor fusion, demonstrating high impact in agricultural automation. Their achievements are particularly valuable for students and researchers in robotics, computer vision, and precision agriculture, offering practical solutions for autonomous systems operating in unstructured, real-world settings.