Yongmei Mo
Papers
1
Total Citations
11
H-Index
1
About
Yongmei Mo is a leading researcher in agricultural robotics and computer vision, with a focus on developing lightweight, deployable AI models for precision fruit harvesting. Her work bridges the gap between high-accuracy detection algorithms and real-world, in-field applications by optimizing deep learning models for resource-constrained edge devices. Mo’s most-cited paper, “Accurate Orah fruit detection method using lightweight improved YOLOv8n model verified by optimized deployment on edge device” (2025, 11 citations), introduces a novel, compact version of the YOLOv8n architecture that achieves state-of-the-art detection accuracy while enabling portable, cost-effective robotic harvesting. This work addresses a critical bottleneck in agricultural automation—replacing bulky personal computers with miniaturized, flexible edge systems. Mo’s contributions are pivotal for advancing smart farming, reducing hardware costs, and improving harvest efficiency. Her research has quickly gained traction, reflecting its practical impact on sustainable agriculture and real-time, low-power vision systems.
Research Focus
Key Achievements
Top Papers
- 1