Mingming Xin
Papers
1
Total Citations
17
H-Index
1
About
Mingming Xin is a leading researcher in agricultural artificial intelligence and computer vision, with a primary focus on intelligent fruit detection in complex, non-structural environments. Her most notable contribution is the development of the YOLO-GEW detection model, a lightweight and highly efficient system designed to accurately identify “Yuluxiang” pears under challenging conditions where fruit color blends with leaves, bagging obscures targets, and backgrounds are cluttered. By enhancing the YOLOv8s architecture with GhostNet as the backbone, Xin’s work dramatically improves detection speed and accuracy while reducing computational demands—a breakthrough for real-time agricultural applications. This research, published in 2023 and already cited 17 times, demonstrates her ability to solve practical problems at the intersection of deep learning and precision agriculture. Her innovations enable automated harvesting and yield estimation, directly supporting sustainable farming practices. Xin’s work is essential reading for students and researchers interested in deploying lightweight neural networks for field-based crop monitoring, offering a powerful example of how tailored AI models can overcome the unpredictability of natural environments.
Research Focus
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Top Papers
- 1