Titre:	Insect pest image recognition : a few-shot machine learning approach including maturity stages classification
Auteur(s):	Gomes, Jacó Cirino Borges, Díbio Leandro
metadata.dc.identifier.orcid:	https://orcid.org/0000-0003-4810-5138 https://orcid.org/0000-0002-4868-0629
metadata.dc.contributor.affiliation:	University of Brasília, Department of Mechanical Engineering University of Brasília, Department of Computer Science
Assunto::	Aprendizado do computador Inseto - classificação Imagens digitais
Date de publication:	22-jui-2022
Editeur:	MDPI
Référence bibliographique:	GOMES, Jacó C.; BORGES, Díbio L. Insect pest image recognition: a few-shot machine learning approach including maturity stages classification. Agronomy, 12, 1733, 2023. DOI: https://doi.org/10.3390/agronomy12081733. Disponível em: https://www.mdpi.com/2073-4395/12/8/1733. Acesso em: 09 out. 2023.
Abstract:	Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at similar maturity stages. This research proposes a solution to this problem using a few-shot learning approach. First, a novel insect data set based on curated images from IP102 is presented. The IP-FSL data set is composed of 97 classes of adult insect images, and 45 classes of early stages, totalling 6817 images. Second, a few-shot prototypical network is proposed based on a comparison with other state-of-art models and further divergence analysis. Experiments were conducted separating the adult classes and the early stages into different groups. The best results achieved an accuracy of 86.33% for the adults, and 87.91% for early stages, both using a Kullback–Leibler divergence measure. These results are promising regarding a crop scenario where the more significant pests are few and it is important to detect them at earlier stages . Further research directions would be in evaluating a similar approach in particular crop ecosystems, and testing cross-domains.
metadata.dc.description.unidade:	Faculdade de Tecnologia (FT) Departamento de Engenharia Mecânica (FT ENM) Instituto de Ciências Exatas (IE) Departamento de Ciência da Computação (IE CIC)
metadata.dc.description.ppg:	Programa de Pós-Graduação em Sistemas Mecatrônicos
Licença::	Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
DOI:	https://doi.org/10.3390/agronomy12081733
Collection(s) :	Artigos publicados em periódicos e afins

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