Papers
1
Total Citations
9
H-Index
1
About
Xingyi Li is a researcher at the forefront of agricultural robotics and intelligent vision systems, with a focused expertise in deep learning-based navigation and object detection for precision farming. Li’s most notable contribution is the development of a navigation line detection algorithm for a corn spraying robot, detailed in the 2025 paper "Navigation line detection algorithm for corn spraying robot based on improved LT-YOLOv10s." This work introduces a novel enhancement to the YOLOv10s architecture, integrating lightweight transformer modules to achieve robust, real-time path recognition in complex field environments. The algorithm significantly improves the accuracy and efficiency of autonomous navigation in row crops, addressing critical challenges in agricultural automation. With 9 citations in its first year, the paper has quickly gained attention for its practical impact on reducing pesticide waste and improving crop management. Li’s research bridges the gap between advanced computer vision and agronomy, offering scalable solutions for smart farming. This achievement underscores Li’s role in advancing the next generation of autonomous agricultural machinery, making their work essential reading for students and researchers in robotics, precision agriculture, and applied deep learning.
Research Focus
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Top Papers
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