Comparação de métodos baseados em redes convolucionais para classificação de fava

Abstract

The bean crop has received little attention from research and extension agencies, which has resulted in limitations in the knowledge of the agronomic characteristics of the crop. This has affected the accuracy in classifying them. Such classification is of great importance because the correct identification of plants allows a good response of the culture in terms of productivity and behavior in different environmental conditions. In this context of limited information about the characteristics, we present a solution that applies the power of computer vision to agronomy, which aims to improve productivity, reduce waste and assist in decision-making and in the selection of culture that best suits a particular region. Computer vision techniques are a set of methods used to interpret images, extracting patterns and features. Aiming to contribute to this scenario of technological development in the agronomic sector, this work compares some of the supervised classification approaches for the automatic identification of broad bean species. The scope of this work is to classify images of seedlings generated by rural producers into two categories of beans: grandma’s ear and cearense. From the comparisons made between methods of classifiers that use convolutional networks as feature extractors, we selected the best method that was the combination of a convolutional network with a support vector machine (SVM), to finally present this method to automate the classification of bean images.