Kangkang Qi
Papers
1
Total Citations
11
H-Index
1
About
Kangkang Qi is a researcher at the forefront of agricultural automation and computer vision, with a primary focus on intelligent detection and classification systems for specialty crops. Their most notable contribution is the development of the Mamba-YOLO framework, a novel deep learning architecture designed to address critical challenges in shiitake mushroom production. By integrating advanced object detection with grade-specific classification, Qi’s work directly tackles the high labor intensity and low harvesting efficiency that have long plagued mushroom cultivation. The flagship paper, "Detection and classification of Shiitake mushroom fruiting bodies based on Mamba YOLO" (2025), has already garnered 11 citations, signaling its rapid impact on precision agriculture and robotics. This achievement demonstrates Qi’s ability to bridge cutting-edge computer vision techniques with practical agricultural needs, offering scalable solutions for automated harvesting and quality control. Their research not only advances the field of smart farming but also provides a template for applying state-of-the-art AI to other high-value crops. For students and researchers exploring the intersection of deep learning and agricultural engineering, Qi’s work represents a compelling case study in translating algorithmic innovation into real-world productivity gains.
Research Focus
Key Achievements
Top Papers
- 1