Methodology for Classifying Diseases in Plants Using Convolutional Neural Networks

Abstract

Convolutional neural networks (CNNs) are one of the deep learning techniques that, due to the computational advance of the last few years, have leveraged the area of computer vision, allowing substantial gains in the most varied classification problems, especially those involving digital images. In this context, this paper aims to propose a methodology for the classification of multiple pathologies related to different plant species. Initially, this methodology involved the image processing and the generation of ten new databases, varying between 50 and 66 classes with greater representation. After training the models (VGG16, RestNet101v1, ResNet101v2, ResNetXt50, and DenseNet169), 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 the results, Friedman’s nonparametric statistical test and two post-hoc procedures were performed, which demonstrated that ResNetXt50 and DenseNet169 obtained superior performances when compared with VGG16 and ResNets.