## 2 Related Work  
In recent years, the research of agricultural plant disease classification has been a relevant topic. Developing a reliable system that is applicable for a large number of classes is a very challenging task. Due to this, several studies have been using CNNs for classification and detection of diseases. In the case of [PERSON] et al. [12], it is proposed the use of CNNs to distinguish healthy cucumbers from unhealthy by using images of leaves. The authors introduced a CNN architecture, which uses the Caffe framework [13], including convolutional layers, pooling layers, and local contrast normalization layers. They achieved an accuracy of 94.9% under the 4-fold cross-validation strategy. Another approach for cucumber leaf diseases classification is that proposed by [PERSON] et al. [14], which presents a CNN composed by four convolutional layers, pooling, and local response normalization (LRN) operations. The parameters of the LRN were the same parameters from AlexNet architecture. At last, their classification system attained an average of 82.3% accuracy under the 4-fold cross validation strategy. [PERSON] et al. [15] developed a plant disease recognition model, based on leaf image classification, by the use of CNNs. Their developed model is able to recognize 13 different types of plant diseases. In addition, they use Caffe as the DL framework. The experimental results showed a precision between 91% and 98%, and the final overall accuracy of the trained model was 96.3%. On the other hand, [PERSON] et al. [16] evaluated well-known architectures of CNNs, AlexNet and GoogleNet, to identify 14 crops species and 26 diseases by using the PlantVillage dataset [17]. They used different training-test distributions to measure the CNN performance: 80-20, 60-40, 50-50, 40-60, and 20-80%. Finally, the performance of their models was based on the ability to predict the correct crop diseases pair. The best performing model achieves a mean F1 score of 0.9934 (overall accuracy of 99.35%). Moreover, [PERSON] et al. [18] made use of CNNs to detect and classify nutritional deficiencies and damage on apple trees. AlexNet was used as CNN architecture, so they made a comparison between Multilayer Perceptron (MLP) and the CNN, which was compared with seven volunteer experts. The results showed an accuracy of 97.3% obtained by CNN, while the human experts had 96%, and the MLP obtained the lowest accuracy at 77.3%. Furthermore, [PERSON] et al. [3] introduced CNN as a learning algorithm for classifying tomato diseases. They used the standard architectures, AlexNet and GoogleNet, and they concluded that DL outperforms other classification techniques (e.g., Random Forest, Support Vector Machine). For comparison purposes, they computed accuracy, macro precision, macro recall, and macro F-score. They obtained results reaching 99.18% accuracy. [PERSON] et al. [19] applied a CNN architecture to classify northern leaf blight lesions on images of maize plants. From the total number of images, 70% were used for training, 15% for validation, and 15% for testing. Their proposed system achieved 96.7% accuracy on test set images not used in training. Additionally, [PERSON] et al. [20] presented a novel rice diseases identification method based on CNNs techniques; the architecture used to identify 10 common rice diseases was AlexNet, and finally, the model achieved an accuracy of 95.48% under 10-fold cross validation. In addition, [PERSON] et al. [6] tested multiple state-of-the-art CNN (AlexNet, DenseNet 169 [21], Inception V3, ResNet 34 [11], SqueezeNet 1.1 [22], and Visual Geometry Group (VGG) 13 [23]) for plant diseases classification using three different strategies. Two of these are based on transfer learning, and the last one was based on shallow strategy. The dataset was divided into 80% for training and 20% for evaluation. The final model accuracy reached was 99.76%, and they concluded that the most successful learning strategy was transfer learning.