AMIPDI: architecture model to identify pests in soybean defoliation images using convolutional neural networks

Pests that attack crops are one of the leading causes of low production and economic losses. Identifying and diagnosingforecasts is one way to reduce losses and maintain quality in production. Defoliation of crops harms production andproduction quality. Using computational technologies such as Artificial Intelligence andMachine Learning has enabledthe identification of pests faster and earlier. In this paper, we propose a Deep Learning-based architecture aimed atidentifying and diagnosing insects by analyzing defoliation in crop images. Different Convolutional Neural Networkapproacheswere considered to evaluate the proposed architecture. The training and testing of themodelswere performedusing images collected by drone in a natural environment. The approach that presented the best performance in ourscenario was the VGG16. The average accuracy in the validation phase was 0.95, while in the test set, we obtained 0.86.