Yunong Yang
Papers
1
Total Citations
3
H-Index
1
About
Yunong Yang is a researcher whose work sits at the intersection of agricultural robotics and computer vision, with a particular focus on enhancing the perceptual capabilities of harvest robots in challenging environmental conditions. His most notable contribution is the development of a haze-removal method based on the dark channel prior, specifically designed for the visual system of apple harvest robots. This work, published in 2016, addresses a critical real-world problem: how to maintain accurate fruit detection and localization when visibility is degraded by atmospheric haze, fog, or dust in outdoor orchard settings. By adapting the dark channel prior—a well-known dehazing technique—to the unique constraints of agricultural robotics, Yang has helped improve the reliability of vision-guided harvesting systems. While his citation count currently stands at 3, the practical significance of his research lies in its direct application to precision agriculture, where robust visual perception is essential for autonomous fruit picking. His work represents a meaningful step toward making agricultural robots more resilient to the unpredictable outdoor environments in which they must operate.
Research Focus
Key Achievements
Top Papers
- 1