MA Guo-xin
Papers
2
Total Citations
28
H-Index
2
About
MA Guo-xin is a leading researcher in agricultural robotics and computer vision, specializing in lightweight deep learning models for precision crop detection. His major contribution lies in developing efficient, real-time object detection systems tailored for complex field environments, addressing the critical challenge of robotic harvesting. His most influential work, "Lightweight Detection of Broccoli Heads in Complex Field Environments Based on LBDC-YOLO" (2024, 19 citations), introduces the LBDC-YOLO model—a lightweight, high-precision framework that enables robotically selective broccoli harvesting by accurately detecting heads amidst variable lighting, occlusion, and foliage. This innovation significantly reduces computational demands while maintaining detection accuracy, making it deployable on resource-constrained agricultural robots. With a combined 28 citations for his flagship papers, MA’s research bridges the gap between advanced computer vision and practical agri-robotics, offering scalable solutions for automated crop management. His work is pivotal for students and researchers exploring edge-AI in agriculture, demonstrating how tailored lightweight architectures can transform labor-intensive harvesting into efficient, autonomous processes.
Research Focus
Key Achievements
Top Papers
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- 2