Journal Article

Identification of leaves of wild Ussurian Pear (Pyrus ussuriensis) based on YOLOv10n-MCS

Dong, Xingguang, Liu, Chao, Qi, Dan, Wu, Yongqing, Tian, Luming, Li, Niman, Zhang, Ying, Huo, Hongliang, Xu, Jiayu, Chen, Zhiyan, Mou, Yulu

2025

Overview

Wild Ussurian Pear germplasm resource has rich genetic diversity, which is the basis for genetic improvement of pear varieties. Accurately and efficiently identifying wild Ussurian Pear accession is a prerequisite for germplasm conservation and utilization. We proposed YOLOv10n-MCS, an improved model featuring: (1) Mixed Local Channel Attention (MLCA) module for enhanced feature extraction, (2) Simplified Spatial Pyramid Pooling-Fast (SimSPPF) for multi-scale feature capture, and (3) C2f_SCConv backbone to reduce computational redundancy. The model was trained on a self-made dataset of 16,079 wild Ussurian Pear leaves images. Experiment results demonstrate that the precision, recall, mAP50, parameters, FLOPs, and model size of YOLOv10n-MCS reached 97.7(95% CI: 97.18 to 98.16)%, 93.5(95% CI: 92.57 to 94.36)%, 98.8(95% CI: 98.57 to 99.03)%, 2.52M, 8.2G, and 5.4MB, respectively. The precision, recall, and mAP50 are significant improved of 2.9%, 2.3%, and 1.5% respectively over the YOLOv10n model (p<0.05). Comparative experiments confirmed its advantages in precision, model complexity, model size, and other aspects. This lightweight model enables real-time wild Ussurian Pear identification in natural environments, providing technical support for germplasm conservation and crop variety identification.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Accuracy / Algorithms / Classification / Conservation / Crop identification / Datasets / Deep learning / Farms / Feature extraction / Flowers & plants / Fruits / Genetic diversity / Germplasm / Identification / Leaves / Machine learning / Morphology / Natural environment / Neural networks / Plant Science / Real time / Recall / target detection / Ussurian Pear / YOLOv10n