## 1 Introduction  
With the outbreak of COVID-19 in 2019, many major food-producing countries have taken measures to restrict grain exports. As tomatoes are the most widely planted and consumed vegetable crop globally, and China is one of the world's largest producers and consumers of tomatoes, tomato production is an important means for farmers to increase income and generate export revenue. However, various diseases severely affect tomato yields, especially considering the increasingly serious issue of food safety. Therefore, accurate identification of tomato diseases and timely treatment have become an urgent issue that needs to be addressed [1; 2; 3; 4]. Many deep learning-based methods have been proposed for crop disease identification [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. For example, [PERSON] et al. [5] proposed the OplusVNet, a 13-layer convolutional neural network that achieved 99% prediction accuracy on a dataset collected from the field using VGG16 network modules for transfer learning. [PERSON] et al. [6] proposed a neural network model that combines image segmentation with transfer learning, segments the image, uses HSV to extract the original leaf area and black background, and feeds it into a VGG-19 model for transfer learning, achieving an accuracy of 99.72%, with a training time of 275,000 s. These networks have effectively improved the recognition of crop diseases and pests. However, due to their complex structure and large model size, they are difficult to deploy on current mainstream devices for real-time disease and pest identification. Many researchers have realized the inconvenience brought by complex models and began designing models with simple structures but powerful functions. For example, [PERSON] et al. [19] proposed a model similar to the VGG inference time backbone, consisting of a series of 3 \(\times\) 3 convolutions and ReLU, and proposed a multi-branch topology efficient model to reduce training time. [PERSON] et al. [20] proposed a self-attention convolutional neural network (SACNN) to address the confusion caused by small disease areas, low contrast between disease areas and backgrounds, and background complexity in crop disease images. The recognition accuracy on AES-CD9214 and MK-D2 was 95.33% and 98%, respectively. These studies focus on the structure and performance of the model to ensure that the model has efficient performance under a simple structure, but they do not fully consider the impact of data on the model. [PERSON] et al. [21] explored this issue from the perspective of data and found that the difficulty in obtaining data samples is the main challenge to improving disease recognition performance. Therefore, they proposed a new data augmentation method based on generative adversarial networks (GAN), called RAHC_GAN, for tomato leaf data augmentation and disease recognition. The results showed that RAHC_GAN can generate leaves with clear disease features, and the generated extended dataset can significantly improve the classifier's recognition performance. Data augmentation is a commonly used method in deep learning to prevent model overfitting. To address the problem of noise samples that may be introduced by data augmentation, which may damage the performance of unorganized data during the inference process, [PERSON] et al. [22] proposed KeepAugment, which uses saliency maps to detect important areas in the original image and then preserves these information areas during the augmentation process to generate more realistic training images. The results showed that this method can improve the training effect on different datasets. Many studies have shown that the combination of attention mechanisms and deep learning models can effectively improve model performance [23; 24; 25; 26; 27]. However, most studies did not investigate the impact of different attention mechanisms on the model. Different attention mechanisms have different characteristics, so their impact on the model must be different.