Kun Ruan
Papers
1
Total Citations
12
H-Index
1
About
Kun Ruan is a researcher specializing in agricultural artificial intelligence and computer vision, with a focus on developing efficient, lightweight detection models for precision agriculture. His most notable contribution is the creation of YOLOC-tiny, a generalized real-time detection model designed to overcome the challenges of detecting large non-green-ripe citrus fruits in unstructured environments. This work addresses critical issues of low detection precision and poor generalization across varying ripeness levels and fruit varieties, building upon the YOLOv7 architecture to achieve high accuracy while maintaining computational efficiency. With 12 citations since its 2024 publication, this research has already demonstrated significant impact in the field of smart agriculture. Ruan’s work is particularly valuable for practical applications in automated fruit harvesting and yield estimation, where reliable detection in complex, real-world orchard conditions is essential. His contributions represent an important step toward bridging the gap between advanced deep learning models and the constraints of edge computing in agricultural robotics.
Research Focus
Key Achievements
Top Papers
- 1