Potato Plant Variety Identification Study Based on Improved Swin Transformer

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.