## 1 Introduction  
One major problem in the agriculture field is that harvest performance could be damaged by many different plant diseases. Tomato (_Solanum Lycopersicum_ L.) is one of the most important and popular vegetable crops in the world [1]. Thus, the prevention of tomato plant diseases has attracted scientists, aiming to increase the harvest performance [2]. There are more than a dozen different diseases of tomato plants in practice, so it is essential to detect them as accurately and as early as possible to prevent and treat the disease [3, 4]. There are different methods of detecting plant diseases for different treatment, including those that use artificial intelligence (AI) algorithms of SVM based on image futures or neural networks (NNs) [5, 6, 7]. It is obvious that AI techniques have been applied in many fields of agriculture to identify plant diseases such as apple, tomato, and rice; others have used deep learning networks, which will be applied in this research for classifying tomato leaf diseases [8, 9, 10, 11]. In our research, one deep learning network will be employed for classifying tomato leaf diseases. In recent years, deep learning (DL) has been applied for the classification of plant conditions in order to find diseases for early treatment [12, 13, 14, 15, 16]. Significantly, the CNN was employed to improve the identification performance from 91% and 98% of 13 leaf diseases [17]. [PERSON] at el. proposed using the CNN with AlexNet for classifying four apple leaf types: mosaic, rust, brown spot, and Alternaria leaf spot [18]. With this CNN, the diseases on apple leaves were detected with high recognition performance, up to 97.62%. In another study, [PERSON] et al. utilized a deep CNN model to detect rice diseases using 500 images of 10 different common conditions [19]. This model achieved an accuracy of 95.48%, which is much higher than using a traditional machine learning network. Tomato leaf diseases have attracted many researchers and different algorithms have been proposed for recognizing and classifying tomato diseases [20; 21; 22]. The combination of three convolutional network families for recognizing tomato diseases was proposed [20]. In particular, these faster region-based convolutional neural network (Faster R-CNN), region-based fully convolutional network (R-FCN), and single shot multibox detector (SSD) were combined to create the meta-architectures with \"deep feature extractors\" and this increased the classification performance. One of the famous datasets is the Plant Village database, consisting of 16,010 images of 10 tomato leaf diseases, which is often used in many research articles [23]. In [24], the authors proposed a novel PCA-whale optimization-based deep neural network model for classifying tomato plant diseases. In particular, the PCA-whale optimization was applied to extract features of the leaf images before being fed into the deep learning network model for classification. This model represented not only an outstanding performance but also difficulties in some cases related to the lacking number of samples. CNN models and a transfer learning method have been applied for classifying tomato leaf diseases [25; 26; 27; 28] to create a pre-trained model for increased prediction. In particular, the authors proposed two models using transfer learning and feature extraction in classifying tomato plant diseases and the obtained result was an accuracy of 90% [29]. Moreover, [PERSON] et al. [30] developed a pre-trained deep-learning algorithm with two AlexNet and VGG-16 models, in which the transfer learning was employed for classifying six tomato crop diseases and one healthy class in image sets obtained from the Plant Village database. This research showed high accuracies of 97.29% using VGG-16 and 97.49% using AlexNet, respectively. In another study [31], a CNN with the transfer learning approach was employed to recognize nine leaf disease sets, in which the automatic extraction of features by directly processing the raw images was performed.