Yue Xue
Papers
2
Total Citations
28
H-Index
2
About
Yue Xue is a researcher at the forefront of agricultural robotics and computer vision, specializing in lightweight deep learning models for precision farming. Their most significant contribution is the development of LBDC-YOLO (Lightweight Broccoli Detection in Complex Environment—You Look Only Once), a novel detection framework designed to enable robotically selective broccoli harvesting. This work, published in 2024, addresses the critical challenge of accurately identifying broccoli heads in complex, unstructured field environments while maintaining computational efficiency for real-time deployment on agricultural robots. With their two most-cited papers accumulating 28 citations in under a year, Xue’s research has quickly gained traction among scholars working on automated crop harvesting. The LBDC-YOLO model represents a notable achievement in balancing detection precision with model lightweightness, overcoming issues like occlusion, variable lighting, and overlapping foliage that plague traditional detection methods. By reducing computational demands without sacrificing accuracy, Xue’s work paves the way for cost-effective, scalable robotic harvesting systems, directly impacting sustainable agriculture and food production efficiency.
Research Focus
Key Achievements
Top Papers
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- 2