# Tomato Leaf Disease Classification by Combining EfficientNetv2 and a Swin Transformer
## 1 Introduction  
Tomatoes, as one of the three major vegetables traded globally, play a pivotal role in the global vegetable trade [1]. With the increasing demand for tomatoes, the global production and planting area of tomatoes continue to expand. Concurrently, the growing consumer demand for food quality, including cocriently and high-quality produce, is increasing. However, tomato production and quality are frequently compromised by various diseases, resulting not only in diminished yields and quality but also in substantial economic losses for farmers [2]. Therefore, ensuring tomato yield and quality is highly important for the global population and even global economic trade. Traditionally, the identification of tomato leaf diseases has heavily relied on visual observation by workers, a method fraught with subjectivity, low accuracy, and inefficiency. Therefore, it is particularly important to achieve efficient automatic identification of tomato leaf diseases and pests using modern technology. With the rise of computer network technology, research on pest identification using machine learning has made significant advancements. Machine learning categorizes and labels external image features of crops, revealing associative information among data through techniques such as data analysis and data mining. Consequently, it automatically detects the extent of damage, greatly improving the efficiency and accuracy of recognition. However, due to the reliance of machine learning on manually extracted features andthe varied performance of different disease characteristics in different scenarios, along with significant differences in symptoms of the same disease at different stages of onset, in complex environmental backgrounds, it is unable to autonomously capture advanced semantic features, resulting in low learning efficiency and weak generalization, making it difficult to achieve satisfactory recognition results. Compared to traditional machine learning methods, deep-learning-based disease recognition methods do not require complex image preprocessing or feature extraction operations. Instead, they automatically extract image features by introducing operations such as convolutional layers, pooling layers, and fully connected layers. Their deep network structures can accommodate rich semantic information, enabling them to quickly and accurately complete target localization and image classification tasks even in complex application scenarios, demonstrating excellent recognition performance in pest and disease image analysis. For example, [PERSON] et al. [3] proposed a recognition model for leaf diseases utilizing deep convolutional neural network technology and successfully identifying 13 types of plant diseases. [PERSON] et al. [4] developed a plant disease detection model based on AlexNet for recognizing apple leaf diseases. [PERSON] et al. [5] improved the ResNet model for pest and disease detection and increased the detection accuracy. [PERSON] et al. [6] proposed an enhanced model based on VGG16 for apple leaf disease identification, where they substituted the fully connected layer with a global average pooling layer to reduce the number of parameters and integrated batch normalization layers to improve the convergence speed. [PERSON] et al. [7] presented a group multiscale attention network (GMA-Net) for rubber tree leaf disease image recognition by devising group multiscale dilated convolution (GMDC) modules for multiscale feature extraction and cross-scale attention feature fusion (CAFF) modules for integrating multiscale attention features. [PERSON] et al. [8] proposed an automatic identification and detection method for crop leaf disease detection based on a fully convolutional-switchable normalized dual-path network (FC-SNDPN); this was used to reduce the influence of complex backgrounds on the image identification of crop diseases and pests. In the field of natural language processing (NLP), the Transformer [9] has achieved significant success in many tasks due to its self-attention mechanism.