Insect pest image recognition : a few-shot machine learning approach including maturity stages classification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Brasília, Department of Mechanical Engineering-
Autor(es): dc.contributorUniversity of Brasília, Department of Computer Science-
Autor(es): dc.creatorGomes, Jacó Cirino-
Autor(es): dc.creatorBorges, Díbio Leandro-
Data de aceite: dc.date.accessioned2024-10-23T15:12:30Z-
Data de disponibilização: dc.date.available2024-10-23T15:12:30Z-
Data de envio: dc.date.issued2023-10-09-
Data de envio: dc.date.issued2023-10-09-
Data de envio: dc.date.issued2022-07-22-
Fonte completa do material: dc.identifierhttp://repositorio2.unb.br/jspui/handle/10482/46639-
Fonte completa do material: dc.identifierhttps://doi.org/10.3390/agronomy12081733-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0003-4810-5138-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-4868-0629-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/877230-
Descrição: dc.descriptionRecognizing 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.-
Descrição: dc.descriptionFaculdade de Tecnologia (FT)-
Descrição: dc.descriptionDepartamento de Engenharia Mecânica (FT ENM)-
Descrição: dc.descriptionInstituto de Ciências Exatas (IE)-
Descrição: dc.descriptionDepartamento de Ciência da Computação (IE CIC)-
Descrição: dc.descriptionPrograma de Pós-Graduação em Sistemas Mecatrônicos-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherMDPI-
Direitos: dc.rightsAcesso Aberto-
Direitos: dc.rightsCopyright: © 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/).-
Palavras-chave: dc.subjectAprendizado do computador-
Palavras-chave: dc.subjectInseto - classificação-
Palavras-chave: dc.subjectImagens digitais-
Título: dc.titleInsect pest image recognition : a few-shot machine learning approach including maturity stages classification-
Tipo de arquivo: dc.typelivro digital-
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