Xinbo Yang
Papers
2
Total Citations
80
H-Index
2
About
Dr. Xinbo Yang is a leading researcher in agricultural robotics and computer vision, specializing in the automated detection and segmentation of fruit for precision harvesting systems. His work directly addresses the critical challenge of identifying green, obscured fruits in natural orchard environments—a key bottleneck for autonomous harvesting. Yang’s major contributions include developing highly accurate and efficient deep learning models for green fruit detection, notably the Foveabox-based approach for green apples, which achieves rapid localization while meeting the real-time demands of harvesting robot vision systems. His 2022 paper on an accurate detection and segmentation model for obscured green fruits has garnered 43 citations, while his fast and efficient green apple detection model has received 37 citations, reflecting the immediate impact and practical relevance of his research. By advancing object detection under occlusion and low-contrast conditions, Yang’s work is instrumental in enabling reliable yield measurement and automated fruit harvesting, pushing the frontier of smart agriculture toward greater autonomy and efficiency.
Research Focus
Key Achievements
Top Papers
- 1An accurate detection and segmentation model of obscured green fruits43 citations · 2022
- 2A fast and efficient green apple object detection model based on Foveabox37 citations · 2022