Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.

Detalhes bibliográficos
Autor(a) principal: SANTOS
Data de Publicação: 2022
Outros Autores: MARCATO JUNIOR, J., ZAMBONI, P., SANTOS, M. F., JANK, L., CAMPOS, E., MATSUBARA, E. T.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1151204
https://doi.org/10.3390/s22114116
Resumo: We 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.
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spelling Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.Banco de GermoplasmaForragemPanicum MaximumTecnologiaForageMechanical harvestingRegression analysisTilleringWe 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.Na publicação: Mateus Figueiredo Santos.LUIZ 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.SANTOSMARCATO JUNIOR, J.ZAMBONI, P.SANTOS, M. F.JANK, L.CAMPOS, E.MATSUBARA, E. T.2023-01-25T13:01:26Z2023-01-25T13:01:26Z2023-01-252022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleSensors, v. 22, article 4116, 2022.1424-8220http://www.alice.cnptia.embrapa.br/alice/handle/doc/1151204https://doi.org/10.3390/s22114116enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2023-01-25T13:01:26Zoai:www.alice.cnptia.embrapa.br:doc/1151204Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542023-01-25T13:01:26falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542023-01-25T13:01:26Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
title Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
spellingShingle Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
SANTOS
Banco de Germoplasma
Forragem
Panicum Maximum
Tecnologia
Forage
Mechanical harvesting
Regression analysis
Tillering
title_short Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
title_full Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
title_fullStr Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
title_full_unstemmed Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
title_sort Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images.
author SANTOS
author_facet SANTOS
MARCATO JUNIOR, J.
ZAMBONI, P.
SANTOS, M. F.
JANK, L.
CAMPOS, E.
MATSUBARA, E. T.
author_role author
author2 MARCATO JUNIOR, J.
ZAMBONI, P.
SANTOS, M. F.
JANK, L.
CAMPOS, E.
MATSUBARA, E. T.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv LUIZ 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.
dc.contributor.author.fl_str_mv SANTOS
MARCATO JUNIOR, J.
ZAMBONI, P.
SANTOS, M. F.
JANK, L.
CAMPOS, E.
MATSUBARA, E. T.
dc.subject.por.fl_str_mv Banco de Germoplasma
Forragem
Panicum Maximum
Tecnologia
Forage
Mechanical harvesting
Regression analysis
Tillering
topic Banco de Germoplasma
Forragem
Panicum Maximum
Tecnologia
Forage
Mechanical harvesting
Regression analysis
Tillering
description We 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023-01-25T13:01:26Z
2023-01-25T13:01:26Z
2023-01-25
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Sensors, v. 22, article 4116, 2022.
1424-8220
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1151204
https://doi.org/10.3390/s22114116
identifier_str_mv Sensors, v. 22, article 4116, 2022.
1424-8220
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1151204
https://doi.org/10.3390/s22114116
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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