Jun-Yan Bu
Papers
1
Total Citations
3
H-Index
1
About
Jun-Yan Bu is a rising researcher at the intersection of computer vision and precision agriculture, with a primary focus on developing deep learning algorithms for automated crop monitoring. His most cited work, "AFBF-YOLO: An Improved YOLO11n Algorithm for Detecting Bunch and Maturity of Cherry Tomatoes in Greenhouse Environments" (2025), addresses a critical bottleneck in intelligent harvesting systems: the accurate detection of occluded, overlapping fruit clusters and subtle ripeness variations under complex greenhouse lighting. By enhancing the YOLO11n architecture with attention mechanisms and feature fusion, Bu's algorithm achieves robust real-time performance, directly enabling more reliable robotic harvesting. Although early in his career, this work has already garnered 3 citations, signaling its immediate relevance to the agricultural AI community. Bu’s contributions are notable for tackling the practical challenges of occlusion and environmental variability that plague field deployment, bridging the gap between laboratory models and real-world farming. His research promises to accelerate the adoption of autonomous harvesting, reduce labor dependency, and improve yield estimation in controlled-environment agriculture.
Research Focus
Key Achievements
Top Papers
- 1