| Campo DC | Valor | Idioma |
|---|
| dc.contributor.author | Gomes, Jacó Cirino | - |
| dc.contributor.author | Borges, Díbio Leandro | - |
| dc.date.accessioned | 2023-10-09T12:06:45Z | - |
| dc.date.available | 2023-10-09T12:06:45Z | - |
| dc.date.issued | 2022-07-22 | - |
| dc.identifier.citation | 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. | pt_BR |
| dc.identifier.uri | http://repositorio2.unb.br/jspui/handle/10482/46639 | - |
| dc.language.iso | eng | pt_BR |
| dc.publisher | MDPI | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | Insect pest image recognition : a few-shot machine learning approach including maturity stages classification | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.subject.keyword | Aprendizado do computador | pt_BR |
| dc.subject.keyword | Inseto - classificação | pt_BR |
| dc.subject.keyword | Imagens digitais | pt_BR |
| dc.rights.license | 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/). | pt_BR |
| dc.identifier.doi | https://doi.org/10.3390/agronomy12081733 | pt_BR |
| dc.description.abstract1 | 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. | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0003-4810-5138 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0002-4868-0629 | pt_BR |
| dc.contributor.affiliation | University of Brasília, Department of Mechanical Engineering | pt_BR |
| dc.contributor.affiliation | University of Brasília, Department of Computer Science | pt_BR |
| dc.description.unidade | Faculdade de Tecnologia (FT) | pt_BR |
| dc.description.unidade | Departamento de Engenharia Mecânica (FT ENM) | pt_BR |
| dc.description.unidade | Instituto de Ciências Exatas (IE) | pt_BR |
| dc.description.unidade | Departamento de Ciência da Computação (IE CIC) | pt_BR |
| dc.description.ppg | Programa de Pós-Graduação em Sistemas Mecatrônicos | pt_BR |
| Aparece nas coleções: | Artigos publicados em periódicos e afins
|
