Development and validation of a model based on vegetation indices for the prediction of sugarcane yield.

Detalhes bibliográficos
Autor(a) principal: VASCONCELOS, J. C. S.
Data de Publicação: 2023
Outros Autores: SPERANZA, E. A., ANTUNES, J. F. G., BARBOSA, L. A. F., CHRISTOFOLETTI, D., SEVERINO, F. J., CANÇADO, G. M. de A.
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/1153006
https://doi.org/10.3390/ agriengineering5020044
Resumo: This study aimed to develop a predictive model for sugarcane production based on data extracted from aerial imagery obtained from drones or satellites, allowing the precise tracking of plant development in the field.
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spelling Development and validation of a model based on vegetation indices for the prediction of sugarcane yield.Agricultura digitalModelo preditivoDistribuição gaussiana inversaRemotely piloted aircraft systemsRPASDigital agricultureInverse Gaussian distributionCana de AçúcarSaccharum OfficinarumSugarcaneVegetation indexModelsThis study aimed to develop a predictive model for sugarcane production based on data extracted from aerial imagery obtained from drones or satellites, allowing the precise tracking of plant development in the field.JULIO CEZAR SOUZA VASCONCELOS, FUNDAÇÃO DE APOIO A PESQUISA E AO DESENVOLVIMENTOEDUARDO ANTONIO SPERANZA, CNPTIAJOAO FRANCISCO GONCALVES ANTUNES, CNPTIALUIZ ANTONIO FALAGUASTA BARBOSA, CNPTIADANIEL CHRISTOFOLETTI, COOPERATIVA DOS PLANTADORES DE CANA DO ESTADO DE SÃO PAULOFRANCISCO JOSÉ SEVERINO, COOPERATIVA DOS PLANTADORES DE CANA DO ESTADO DE SÃO PAULOGERALDO MAGELA DE ALMEIDA CANCADO, CNPTIA.VASCONCELOS, J. C. S.SPERANZA, E. A.ANTUNES, J. F. G.BARBOSA, L. A. F.CHRISTOFOLETTI, D.SEVERINO, F. J.CANÇADO, G. M. de A.2023-04-05T11:50:27Z2023-04-05T11:50:27Z2023-04-052023info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleAgriEngineering, v. 5, n. 2, p. 698-719, June 2023.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1153006https://doi.org/10.3390/ agriengineering5020044enginfo: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:EMBRAPA2023-04-05T11:50:27Zoai:www.alice.cnptia.embrapa.br:doc/1153006Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542023-04-05T11:50:27falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542023-04-05T11:50:27Repositório 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 Development and validation of a model based on vegetation indices for the prediction of sugarcane yield.
title Development and validation of a model based on vegetation indices for the prediction of sugarcane yield.
spellingShingle Development and validation of a model based on vegetation indices for the prediction of sugarcane yield.
VASCONCELOS, J. C. S.
Agricultura digital
Modelo preditivo
Distribuição gaussiana inversa
Remotely piloted aircraft systems
RPAS
Digital agriculture
Inverse Gaussian distribution
Cana de Açúcar
Saccharum Officinarum
Sugarcane
Vegetation index
Models
title_short Development and validation of a model based on vegetation indices for the prediction of sugarcane yield.
title_full Development and validation of a model based on vegetation indices for the prediction of sugarcane yield.
title_fullStr Development and validation of a model based on vegetation indices for the prediction of sugarcane yield.
title_full_unstemmed Development and validation of a model based on vegetation indices for the prediction of sugarcane yield.
title_sort Development and validation of a model based on vegetation indices for the prediction of sugarcane yield.
author VASCONCELOS, J. C. S.
author_facet VASCONCELOS, J. C. S.
SPERANZA, E. A.
ANTUNES, J. F. G.
BARBOSA, L. A. F.
CHRISTOFOLETTI, D.
SEVERINO, F. J.
CANÇADO, G. M. de A.
author_role author
author2 SPERANZA, E. A.
ANTUNES, J. F. G.
BARBOSA, L. A. F.
CHRISTOFOLETTI, D.
SEVERINO, F. J.
CANÇADO, G. M. de A.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv JULIO CEZAR SOUZA VASCONCELOS, FUNDAÇÃO DE APOIO A PESQUISA E AO DESENVOLVIMENTO
EDUARDO ANTONIO SPERANZA, CNPTIA
JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA
LUIZ ANTONIO FALAGUASTA BARBOSA, CNPTIA
DANIEL CHRISTOFOLETTI, COOPERATIVA DOS PLANTADORES DE CANA DO ESTADO DE SÃO PAULO
FRANCISCO JOSÉ SEVERINO, COOPERATIVA DOS PLANTADORES DE CANA DO ESTADO DE SÃO PAULO
GERALDO MAGELA DE ALMEIDA CANCADO, CNPTIA.
dc.contributor.author.fl_str_mv VASCONCELOS, J. C. S.
SPERANZA, E. A.
ANTUNES, J. F. G.
BARBOSA, L. A. F.
CHRISTOFOLETTI, D.
SEVERINO, F. J.
CANÇADO, G. M. de A.
dc.subject.por.fl_str_mv Agricultura digital
Modelo preditivo
Distribuição gaussiana inversa
Remotely piloted aircraft systems
RPAS
Digital agriculture
Inverse Gaussian distribution
Cana de Açúcar
Saccharum Officinarum
Sugarcane
Vegetation index
Models
topic Agricultura digital
Modelo preditivo
Distribuição gaussiana inversa
Remotely piloted aircraft systems
RPAS
Digital agriculture
Inverse Gaussian distribution
Cana de Açúcar
Saccharum Officinarum
Sugarcane
Vegetation index
Models
description This study aimed to develop a predictive model for sugarcane production based on data extracted from aerial imagery obtained from drones or satellites, allowing the precise tracking of plant development in the field.
publishDate 2023
dc.date.none.fl_str_mv 2023-04-05T11:50:27Z
2023-04-05T11:50:27Z
2023-04-05
2023
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 AgriEngineering, v. 5, n. 2, p. 698-719, June 2023.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1153006
https://doi.org/10.3390/ agriengineering5020044
identifier_str_mv AgriEngineering, v. 5, n. 2, p. 698-719, June 2023.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1153006
https://doi.org/10.3390/ agriengineering5020044
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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