Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1151204
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Campo DCValorIdioma
dc.contributor.authorSANTOS
dc.contributor.authorMARCATO JUNIOR, J.
dc.contributor.authorZAMBONI, P.
dc.contributor.authorSANTOS, M. F.
dc.contributor.authorJANK, L.
dc.contributor.authorCAMPOS, E.
dc.contributor.authorMATSUBARA, E. T.
dc.date.accessioned2023-01-25T13:01:26Z-
dc.date.available2023-01-25T13:01:26Z-
dc.date.created2023-01-25
dc.date.issued2022
dc.identifier.citationSensors, v. 22, article 4116, 2022.
dc.identifier.issn1424-8220
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1151204-
dc.descriptionWe assessed the performance of Convolutional Neural Network (CNN)-based approaches using mobile phone images to estimate regrowth density in tropical forages. We generated a dataset composed of 1124 labeled images with 2 mobile phones 7 days after the harvest of the forage plants. Six architectures were evaluated, including AlexNet, ResNet (18, 34, and 50 layers), ResNeXt101, and DarkNet. The best regression model showed a mean absolute error of 7.70 and a correlation of 0.89. Our findings suggest that our proposal using deep learning on mobile phone images can successfully be used to estimate regrowth density in forages.
dc.language.isoeng
dc.rightsopenAccess
dc.titleDeep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
dc.typeArtigo de periódico
dc.subject.thesagroBanco de Germoplasma
dc.subject.thesagroForragem
dc.subject.thesagroPanicum Maximum
dc.subject.thesagroTecnologia
dc.subject.nalthesaurusForage
dc.subject.nalthesaurusMechanical harvesting
dc.subject.nalthesaurusRegression analysis
dc.subject.nalthesaurusTillering
dc.description.notesNa publicação: Mateus Figueiredo Santos.
riaa.ainfo.id1151204
riaa.ainfo.lastupdate2023-01-25
dc.identifier.doihttps://doi.org/10.3390/s22114116
dc.contributor.institutionLUIZ SANTOS, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; JOSÉ MARCATO JUNIOR, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; PEDRO ZAMBONI, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; MATEUS FIGUEIREDO SANTOS, CNPGC; LIANA JANK, CNPGC; EDILENE CAMPOS, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; EDSON TAKASHI MATSUBARA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL.
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