Research on the identification of desert plants in Xinjiang based on CNN and Swin Transformer

The desert areas of Xinjiang are prone to drought disasters and agricultural and animal husbandry production under the dual influence of climate and environment, which is not conducive to the sustainable economy of Xinjiang, the identification of desert plants in Xinjiang is the basis for various plant researchers to understand the growth status of plants, as well as a prerequisite for ecological conservation research and implementation of management measures. At the same time, the study is difficult due to the similarity of Xinjiang desert plant images between classes, complex image background and unbalanced data samples. In order to improve recognition accuracy, accurately locate locally important features and comprehensively consider complex global information, a plant image recognition method that combines convolutional neural network (CNN) and Swin Transformer network is proposed. The method combines the advantages of CNN network which is good at extracting local features and Swin Transformer which is good at capturing global representation, and embeds an improved Convolutional Block Attention Module (CBAM) in the CNN branch to fully extract the local key features with differentiation, and the Focal Loss function is used to solve the problem of data sample imbalance. The experimental results show that the proposed fused method can extract the features of the images more adequately than the single-branch network on the Xinjiang desert plant dataset, and its recognition accuracy can reach 97.99%, and the precision, recall and F1 score are better than the existing methods. Finally, the effectiveness of the method is further corroborated by visualization analysis and confusion matrix.