## 2 Related Work
It is important to note that one of the main differences between this work and the one proposed here are the ANNs implemented for comparison since, based on the results of this work, the selection of an ANN was limited to only three of those that were used, including GoogleNet and other versions of ResNet, that presented significant results in the literature. Moreover, [PERSON] et al. [24] proposed a DL approach to estimate disease severity. The best model was VGG 16 trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. On the other hand, [PERSON] et al. [25] introduced a method for crop disease images classification by combining CNN and transfer learning. Their CNN with five convolutional layers reached 90.84% and demonstrated that the combination of CNN and transfer learning strategy is effective for classification of various crop diseases. [PERSON] et al. [26] used AlexNet and VGG 16 for classifying six different diseases and a healthy class of tomato. The performance was evaluated by modifying the number of images, setting different batch sizes, and varying the weight and bias learning rate. They concluded that AlexNet provides a better accuracy with the minimum execution time compared to VGG 16. It should be noted that, taking into account that this work is also aimed at the classification of diseases present in tomato plants, the development of the proposed methodology is based on the results reported by this comparison, allowing support in the delimitation of the work and selection of architectures to implement, while being able to discard the implementation of VGG 16 due to the disadvantages that it presents in comparison with AlexNet, especially in the computational cost. Finally, [PERSON] and [PERSON] [27] detected plant diseases using different state-of-the-art approaches. In this work, their model was able to attain a high average accuracy of 99.374% using transfer learning. Table 1 summarizes the studied approaches and methodologies that use DL models for plant diseases classification, where the results are based on accuracy, as well as the metrics that they used and the final results that are based on accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author and Year** & **Number of Classes** & **Number of Images** & **CNN Architecture** & **Measures** & **Accuracy** \\ \hline [PERSON] et al. [12]\(-\)2015 & 3 & 800 & Customized & \begin{tabular}{c} Accuracy \\ Sensitivity \\ Specificity \\ \end{tabular}  & 94.90\% \\ \hline [PERSON], [PERSON] et al. [15]\(-\)2016 & 15 & 4483 & CaffeNet & Accuracy & 96.30\% \\ \hline [PERSON] et al. [16]\(-\)2016 & 38 & 54,306 & \begin{tabular}{c} AlexNet \\ GoogleNet \\ \end{tabular}  & \begin{tabular}{c} F1 Score \\ Mean Precision \\ Accuracy \\ \end{tabular}  & 99.34\% \\ \hline [PERSON], [PERSON] et al. [18]\(-\)2016 & 5 & 1450 & AlexNet & \begin{tabular}{c} Recall \\ Precision \\ Accuracy \\ \end{tabular}  & 97.30\% \\ \hline [PERSON], [PERSON] et al. [14]\(-\)2016 & 7 & 14,840 & Customized & \begin{tabular}{c} Sensitivity \\ Specificity \\ Accuracy \\ \end{tabular}  & 82.30\% \\ \hline [PERSON] et al. [3]\(-\)2017 & 9 & 14,828 & \begin{tabular}{c} AlexNet \\ GoogleNet \\ \end{tabular}  & \begin{tabular}{c} Accuracy \\ Precision \\ Recall \\ \end{tabular}  & 99.18\% \\ \hline [PERSON] et al. [19]\(-\)2017 & 2 & 1796 & Customized & \ \begin{tabular}{c} Accuracy \\ Recall \\ F1 Score \\ \end{tabular}  & 96.70\% \\ \hline [PERSON], [PERSON] et al. [20]\(-\)2017 & 10 & 500 & AlexNet & Accuracy & 95.48\% \\ \hline [PERSON], [PERSON] et al. [24]\(-\)2017 & 4 & 2086 & \begin{tabular}{c} VGG 16 \\ Inception V3 \\ Residual Network (ResNet) 50 \\ \end{tabular}  & Accuracy & 90.4\% \\ \hline \multirow{6}{*}{[PERSON] et al.