文献类型： 外文期刊
作者： Xing, Xue 1 ; Liu, Chengzhong 1 ; Han, Junying 1 ; Feng, Quan 2 ; Qi, Enfang 3 ; Qu, Yaying 3 ; Ma, Baixiong 1 ;
作者机构： 1.Gansu Agr Univ, Coll Informat Sci & Technol, Lanzhou 730070, Peoples R China
2.Gansu Agr Univ, Coll Mech & Elect Engn, Lanzhou 730070, Peoples R China
3.Gansu Acad Agr Sci, Potato Res Inst, Lanzhou 730070, Peoples R China
关键词： deep learning; potato; Swin Transformer; convolutional neural network; variety identification
期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）
ISSN：
年卷期： 2025 年 15 卷 1 期
页码：
收录情况： SCI
摘要： Potato is one of the most important food crops in the world and occupies a crucial position in China's agricultural development. Due to the large number of potato varieties and the phenomenon of variety mixing, the development of the potato industry is seriously affected. Therefore, accurate identification of potato varieties is a key link to promote the development of the potato industry. Deep learning technology is used to identify potato varieties with good accuracy, but there are relatively few related studies. Thus, this paper introduces an enhanced Swin Transformer classification model named MSR-SwinT (Multi-scale residual Swin Transformer). The model employs a multi-scale feature fusion module in place of patch partitioning and linear embedding. This approach effectively extracts features of various scales and enhances the model's feature extraction capability. Additionally, the residual learning strategy is integrated into the Swin Transformer block, effectively addressing the issue of gradient disappearance and enabling the model to capture complex features more effectively. The model can better capture complex features. The enhanced MSR-SwinT model is validated using the potato plant dataset, demonstrating strong performance in potato plant image recognition with an accuracy of 94.64%. This represents an improvement of 3.02 percentage points compared to the original Swin Transformer model. Experimental evidence shows that the improved model performs better and generalizes better, providing a more effective solution for potato variety identification.
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