## 1 Introduction  
Tomato (_Lycoperscion esculentum_) occupies a prominent place in the Mexican agricultural economy. In fact, Mexico is the world's leading supplier of tomatoes, with an international market share of 25.11% of the value of world exports [1]. According to the _Sercicio de Informacion Agroalimentaria y Pesquera_ (SIAP), it is estimated that tomato exports will grow up to 3.84 million tons in 2024.  
Healthy plants need to be protected from diseases to guarantee the quality and quantity of crop since they are highly affected by diseases which cause dramatic losses in agricultural economy [2; 3].  
It is important to mention that providing early monitoring is essential for choosing the correct treatment and stopping the disease from spreading [4]. Commonly, the detection and identification of diseases is achieved by experts by simple observation [4]. However, this task needs continuous monitoring by experts [5] since it can be erroneously diagnosed by farmers because they usually judge the symptoms by their experience; thus, a precise recognition technology is essential for this task.  
Deep Learning (DL) is taking major advances in solving problems because the adaptability of computer vision techniques tan can be adapted, and its results outperform the state-of-the-art in several topics [6]. According to [PERSON] [7], DL refers to the use of artificial neural networks (ANNs) architectures that contain a quite large number of processing layers, as opposed to \"shallower\" architectures of more traditional ANN methodologies. The main advantage of DL is the ability to exploit directly raw data without using hand-crafted features [3].  
In DL, Convolutional Neural Networks (CNNs) are a class of deep ANN, and they have many applications, including complex tasks, such as image classification and object recognition, which lead to a significant improvement in image classification in several topics, such as agriculture. Motivated by the DL breakthrough and the rapid development of CNNs, many powerful architectures have emerged, such as AlexNet [8], GoogleNet [9], Inception V3 [10], Residual Network (ResNet) 50, and ResNet 18 [11].  
In this work, the architectures previously mentioned are used to classify tomato diseases, in order to develop a comparison between these CNN architectures performance, supporting in the selection of an automatic system that allows the extension of its the applications in the field of agriculture. The main contribution of this paper focuses on a detailed description of the operation of each ANN, in addition to its implementation to be applied in a database composed of a series of images related to diseased tomato plants and, in this way, to be able to objectively know through a direct comparison the behavior of the different ANNs for future applications. On the other hand, with the aim of determining which is the architecture that best models the problem and achieves a good classification of tomato plant diseases and validating it through different statistical metrics, the CNN models can be implemented in this way as an automated system designed to identify plant diseases, which could be of great help to technicians and experts in agriculture.  
The rest of this paper is organized as follows: Section 2 presents an overview of related work. Section 3 introduces the five state-of-the-art CNN architectures and the experiments. Section 4 shows the results. Section 5 holds the discussion and, finally, Section 6 reports the final conclusions.