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

Year: 2025

Type: article

Abstract: 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.

Source: Revista Brasileira de Computação Aplicada

DOI: 10.5335/rbca.v17i2.16186

Authors: Maria Eloisa Mignoni, Aislan Honorato, Cesar Zagonel, Gabriel de Oliveira Ramos, Rafael Kunst Kunst

Institutions: Universidade do Estado de Mato Grosso, Centro Universitário de Várzea Grande, Universidade Cruzeiro do Sul, Universidade do Vale do Rio dos Sinos

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: diamond

OpenAlex ID: W4413879673

FWCI:

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