## 1 Introduction  
In agriculture, the identification of tomato leaf diseases has traditionally relied on manual identification, which can be subject to fluctuations in accuracy due to the variability in the experience and expertise of the human identifiers [1]. Additionally, the process is laborious and time-consuming, making it challenging to identify a large number of experts with sufficient experience in the identification of tomato leaf diseases [2]. Recent advances in machine learning and deep learning techniques have revolutionized crop pest and disease identification by enabling the automated detection of leaf diseases. However, traditional machine learning methods face limitations, such as the need for manual screening of features and the inability to perform fully automated disease identification, including disease classification [3; 4; 5; 6]. Conventional convolutional networks are also not optimal for recognizing single diseases in crop disease identification and may not accurately identify diseased regions of the crop [7; 8; 9]. [PERSON] and [PERSON] addressed these limitations by employing the random forest (RF) algorithm to detect three different types of tomato leaf diseases, namely bacterial spots, tomato mosaic viruses, late blight, and healthy leaf disease. The algorithm achieved an impressive accuracy of 94.10%, surpassing the results obtained using the SVM and MDC algorithms, which recorded accuracies of 82.60% and 87.60%, respectively, when applied to the same dataset [10]. In a similar vein, [PERSON] et al. (2020) developed a system that could detect seven distinct types of tomato leaf diseases using SVM, logistic regression (LR), and RF. The Haralick algorithm was employed to extract the texture features of the leaves, and the classifiers were used to classify the extracted features. The results of the study revealed that SVM outperformed the other two algorithms, recording an accuracy of 87.60%, followed by RF and LR, which recorded accuracies of 70.05% and 67.30%, respectively [11]. Deep learning models have been widely applied in plant leaf disease detection and classification. [PERSON] et al., proposed a TL-DCNN model to identify tomato leaf diseases and compared the performance of Adam, RMSprop and SGD optimizers, finding that the modified-Xception model with Adam optimizer achieved the best results [12]. [PERSON] et al., used seven pre-trained EfficientNet-B0-B7 models to train and test on four datasets of cucumber powdery mildew, downy mildew, healthy leaves and a combination of powdery mildew and downy mildew, finding that efficient-B4 achieved the highest accuracy (97%) for cucumber disease classification in four tasks [13]. [PERSON] et al., proposed a lightweight convolutional neural network model to diagnose grape diseases, including black rot, black measles and leaf blight. The model incorporated the squeeze-and-excitation (SE) mechanism into ShuffleNet and achieved 99.14% accuracy on PlantVillage dataset [14]. [PERSON] proposed an SSCNN network for citrus leaf disease classification, which can be deployed on smartphones and has higher accuracy and less computation time than MobileNet [15]. [PERSON] et al., used four typical convolutional network models trained on Plant Village to find the optimal model and present a user interface on Raspberry Pi [16]. [PERSON] et al., generated synthetic images of tomato leaves by constructing a C-GAN network for the network training [17]. [PERSON] et al. (2017) used GoogLeNet and AlexNet models to detect tomato plant diseases and found that GoogLeNet had an accuracy of 99.18%, higher than AlexNet's 98.60% [18]. [PERSON] et al. (2020) proposed an improved ResNet50 model to classify three different types of tomato leaf diseases (spot disease, yellow leaf curling and late blight), which used leaky ReLU activation function and modified the filter size in each convolutional layer to 11 \(\times\) 11 to improve accuracy. The experimental results showed that ResNet50 achieved 98% accuracy on the test dataset [19]. [PERSON] et al.