Convolutional neural networks in the qualitative improvement of sweet potato roots

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dc.creatorAnaclara Gonçalves Fernandes
dc.creatorNermy Ribeiro Valadares
dc.creatorClóvis Henrique Oliveira Rodrigues
dc.creatorRayane Aguiar Alves
dc.creatorLis Lorena Melucio Guedes
dc.creatorAndré Luiz Mendes Athayde
dc.creatorAlcinei Mistico Azevedo
dc.date.accessioned2024-09-17T13:10:48Z
dc.date.accessioned2025-09-08T23:35:22Z
dc.date.available2024-09-17T13:10:48Z
dc.date.issued2023
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.identifier.doihttps://doi.org/10.1038/s41598-023-34375-6
dc.identifier.issn20452322
dc.identifier.urihttps://hdl.handle.net/1843/76535
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofscientific reports
dc.rightsAcesso Aberto
dc.subjectBatata-doce
dc.subjectRedes neurais (Computação)
dc.subjectGenética vegetal
dc.subjectMelhoramento genético
dc.subject.otherBatata-doce
dc.subject.otherRedes neurais (Computação)
dc.subject.otherGenética vegetal
dc.subject.otherMelhoramento genético
dc.titleConvolutional neural networks in the qualitative improvement of sweet potato roots
dc.typeArtigo de periódico
local.citation.epage8
local.citation.spage1
local.citation.volume13
local.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.
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://www.nature.com/articles/s41598-023-34375-6

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