Yingying Yin
Papers
1
Total Citations
9
H-Index
1
About
Yingying Yin is a leading researcher in smart agriculture and computer vision, specializing in robust fruit detection for automated harvesting systems. Her most notable contribution is the development of VBP-YOLO-prune, an optimized lightweight detection model based on YOLOv8n, designed to overcome the challenges of variable orchard environments—including unstable lighting, occlusion, and adverse weather. This work, published in 2025 and already garnering 9 citations, demonstrates her ability to advance real-world agricultural robotics by balancing detection accuracy with computational efficiency. By integrating feature-adaptive fusion and pruning techniques, Yin’s research directly addresses the critical gap between laboratory performance and field deployment, enabling apple-picking robots to operate reliably under complex conditions. Her work has significant implications for reducing labor costs and improving yield in smart agriculture. With a growing citation impact and a focus on practical, deployable AI solutions, Yingying Yin is establishing herself as a key innovator at the intersection of deep learning and precision farming, inspiring future research in adaptive vision systems for agricultural automation.
Research Focus
Key Achievements
Top Papers
- 1