Xinru Song
Papers
1
Total Citations
9
H-Index
1
About
Xinru Song is a leading researcher in agricultural robotics and computer vision, with a primary focus on intelligent fruit detection and automated harvesting systems. Her most influential work, "Watermelon Detection and Localization Technology Based on GTR-Net and Binocular Vision" (2024, 9 citations), tackles the critical challenge of developing reliable picking robots for complex field environments. Song’s major contribution lies in designing the GTR-Net architecture, which significantly improves fruit recognition accuracy under adverse conditions such as variable lighting, leaf occlusion, and overlapping fruits. By integrating this deep learning model with binocular vision, she has advanced the precision of 3D localization for watermelons, directly addressing the labor shortages and rising costs in fruit agriculture. This work demonstrates her ability to bridge cutting-edge AI with practical agricultural engineering. Song’s research has already garnered attention from peers working on robotic harvesting, and her methodologies are being adapted for other crops. Her achievements highlight a commitment to solving real-world food production challenges through innovative, technology-driven solutions.
Research Focus
Key Achievements
Top Papers
- 1