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dc.contributor.authorGomes, Jacó Cirino-
dc.contributor.authorBorges, Díbio Leandro-
dc.date.accessioned2023-10-09T12:06:45Z-
dc.date.available2023-10-09T12:06:45Z-
dc.date.issued2022-07-22-
dc.identifier.citationGOMES, 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.pt_BR
dc.identifier.urihttp://repositorio2.unb.br/jspui/handle/10482/46639-
dc.language.isoengpt_BR
dc.publisherMDPIpt_BR
dc.rightsAcesso Abertopt_BR
dc.titleInsect pest image recognition : a few-shot machine learning approach including maturity stages classificationpt_BR
dc.typeArtigopt_BR
dc.subject.keywordAprendizado do computadorpt_BR
dc.subject.keywordInseto - classificaçãopt_BR
dc.subject.keywordImagens digitaispt_BR
dc.rights.licenseCopyright: © 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/).pt_BR
dc.identifier.doihttps://doi.org/10.3390/agronomy12081733pt_BR
dc.description.abstract1Recognizing 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.pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-4810-5138pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-4868-0629pt_BR
dc.contributor.affiliationUniversity of Brasília, Department of Mechanical Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasília, Department of Computer Sciencept_BR
dc.description.unidadeFaculdade de Tecnologia (FT)pt_BR
dc.description.unidadeDepartamento de Engenharia Mecânica (FT ENM)pt_BR
dc.description.unidadeInstituto de Ciências Exatas (IE)pt_BR
dc.description.unidadeDepartamento de Ciência da Computação (IE CIC)pt_BR
dc.description.ppgPrograma de Pós-Graduação em Sistemas Mecatrônicospt_BR
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