Song Yue
Papers
1
Total Citations
17
H-Index
1
About
Song Yue is a leading researcher in agricultural robotics and precision sensing, with a focus on overcoming the challenges of unstructured, complex environments. Her work centers on integrating computer vision and semantic understanding to enable autonomous systems to perceive and interact with natural scenes. Her most-cited paper, "High-precision target ranging in complex orchard scenes by utilizing semantic segmentation results and binocular vision" (2023, 17 citations), exemplifies her core contribution: developing a novel method that fuses deep learning-based semantic segmentation with stereo vision to achieve robust, high-accuracy distance estimation for fruit and branch targets in dense, variable orchard canopies. This approach directly addresses the limitations of traditional ranging in cluttered agricultural settings, providing a critical foundation for automated harvesting, spraying, and navigation. Yue’s research is notable for its practical impact, bridging the gap between advanced computer vision algorithms and real-world agricultural deployment. Her work has been recognized for its potential to enhance the efficiency and autonomy of precision agriculture, marking her as an emerging voice in the field of agricultural robotics and environmental perception.
Research Focus
Key Achievements
Top Papers
- 1