Weidi Xu
Papers
1
Total Citations
35
H-Index
1
About
Weidi Xu is a researcher whose work bridges computer vision and agricultural technology, with a primary focus on image segmentation and environmental perception. His most cited paper, "Shadow detection and removal in apple image segmentation under natural light conditions using an ultrametric contour map" (2019, 35 citations), addresses a critical challenge in precision agriculture: accurately segmenting fruit in variable outdoor lighting. By introducing an ultrametric contour map-based approach, Xu's method effectively mitigates shadow interference, enabling more reliable fruit detection and harvesting automation. This contribution has practical implications for improving yield estimation and robotic harvesting systems. Beyond this flagship work, Xu's research explores how visual data can be robustly interpreted under real-world constraints, such as changing illumination and complex backgrounds. His work is characterized by a blend of theoretical rigor in image processing and applied problem-solving for agricultural robotics. With a growing citation record, Xu is establishing himself as a key contributor to the intersection of computer vision and smart farming, where his methods offer scalable solutions for enhancing crop monitoring and automated decision-making in natural environments.
Research Focus
Key Achievements
Top Papers
- 1