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  3. Artigo de Periódico
Use este identificador para citar o ir al link de este elemento: http://hdl.handle.net/1843/76535
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Campo DCValorIdioma
dc.creatorAnaclara Gonçalves Fernandespt_BR
dc.creatorNermy Ribeiro Valadarespt_BR
dc.creatorClóvis Henrique Oliveira Rodriguespt_BR
dc.creatorRayane Aguiar Alvespt_BR
dc.creatorLis Lorena Melucio Guedespt_BR
dc.creatorAndré Luiz Mendes Athaydept_BR
dc.creatorAlcinei Mistico Azevedopt_BR
dc.date.accessioned2024-09-17T13:10:48Z-
dc.date.available2024-09-17T13:10:48Z-
dc.date.issued2023-
dc.citation.volume13pt_BR
dc.citation.spage1pt_BR
dc.citation.epage8pt_BR
dc.identifier.doihttps://doi.org/10.1038/s41598-023-34375-6pt_BR
dc.identifier.issn20452322pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/76535-
dc.description.resumoThe objective was to verify whether convolutional neural networks can help sweet potato phenotyping for qualitative traits. We evaluated 16 families of sweet potato half-sibs in a randomized block design with four replications. We obtained the images at the plant level and used the ExpImage package of the R software to reduce the resolution and individualize one root per image. We grouped them according to their classifications regarding shape, peel color, and damage caused by insects. 600 roots of each class were destined for training the networks, while the rest was used to verify the quality of the fit. We used the python language on the Google Colab platform and the Keras library, considering the VGG-16, Inception-v3, ResNet-50, InceptionResNetV2, and EfficientNetB3 architectures. The InceptionResNetV2 architecture stood out with high accuracy in classifying individuals according to shape, insect damage, and peel color. Image analysis associated with deep learning may help develop applications used by rural producers and improve sweet potatoes, reducing subjectivity, labor, time, and financial resources in phenotyping.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Geraispt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofscientific reports-
dc.rightsAcesso Abertopt_BR
dc.subjectBatata-docept_BR
dc.subjectRedes neurais (Computação)pt_BR
dc.subjectGenética vegetalpt_BR
dc.subjectMelhoramento genéticopt_BR
dc.subject.otherBatata-docept_BR
dc.subject.otherRedes neurais (Computação)pt_BR
dc.subject.otherGenética vegetalpt_BR
dc.subject.otherMelhoramento genéticopt_BR
dc.titleConvolutional neural networks in the qualitative improvement of sweet potato rootspt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.nature.com/articles/s41598-023-34375-6pt_BR
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