Convolutional neural networks (CNNs) are one of the deep learning techniques that, due to the computational advancement of recent years, have enhanced the field of computer vision by enabling substantial gains in various classification problems, especially those involving digital images. Given the advantages of using these networks, a variety of applications for automatic plant diseases identification have been developed for specialized assistance or automated screening tools, contributing to more sustainable farming practices and improved food production security. In this context, this work aims to propose a methodology for the classification of multiple pathologies from distinct plant species, having as input a database composed of digital images of plant diseases. Initially, this methodology involved image preprocessing activities on the plant disease database to provide the appropriate input for selected CNN models (VGG16, RestNet101v1, ResNet101v2, ResNetXt50 and DenseNet169), as well as to generate ten new bases, ranging from 50 to 66 classes with greater representativeness, to submit the models to different situations. After model training, a comparative study was conducted based on widely used classification metrics such as test accuracy, f1-score, and area under the curve. To attest the significance of obtained results, the Friedman nonparametric statistical test and two post-hoc procedures were performed, which showed that ResNetXt50 and DenseNet169 obtained superior results when compared with VGG16 and ResNets.
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