Xiao Xu
Papers
1
Total Citations
3
H-Index
1
About
Xiao Xu is a researcher focused on advancing agricultural automation through computer vision and deep learning. Their key research area lies in developing robust object detection models tailored for complex, unstructured orchard environments. Xu’s major contribution is the creation of AC-YOLO, a novel detection framework specifically designed to overcome challenges like fruit occlusion, irregular shapes, and variable lighting in natural settings. This work directly addresses the critical bottleneck in robotic fruit picking: accurate recognition under real-world conditions. The paper "AC-YOLO: citrus detection in the natural environment of orchards" (2024, 3 citations) demonstrates Xu’s ability to translate deep learning innovations into practical agricultural solutions. While early in its citation impact, this work represents a foundational step toward more reliable, automated harvesting systems. Xu’s research is particularly notable for tackling the "complex background" problem—where overlapping fruits, leaf shadows, and color variations confuse standard detectors—making their contributions highly relevant for precision agriculture and smart farming technologies.
Research Focus
Key Achievements
Top Papers
- 1AC-YOLO: citrus detection in the natural environment of orchards3 citations · 2024