Xiaoyi Shi
Papers
1
Total Citations
19
H-Index
1
About
Xiaoyi Shi is a researcher at the forefront of agricultural robotics and computer vision, specializing in deep learning-based object detection for complex orchard environments. Her most impactful work centers on the development of MLG-YOLO, a real-time detection model that achieves remarkable accuracy in localizing winter jujubes within challenging, unstructured settings. With directional errors as low as 3.90 mm, her method provides critical technical support for automated harvesting robots, bridging the gap between algorithmic precision and practical agricultural deployment. This work, published in 2024, has already garnered 19 citations, reflecting its immediate relevance to the field of precision agriculture. Shi’s contributions address a key bottleneck in fruit harvesting automation: reliable detection under occlusion, variable lighting, and dense foliage. By integrating lightweight architectures with high localization fidelity, her research enables robots to operate efficiently in real-world orchards. Her achievements demonstrate a clear trajectory toward scalable, intelligent harvesting systems, positioning her as an emerging leader in the intersection of computer vision and agricultural engineering.
Research Focus
Key Achievements
Top Papers
- 1