Estimating sugar yield in sugarcane using green normalized difference vegetation index derived from imagery obtained by remotely piloted aircrafts.

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dc.contributorJULIO CEZAR SOUZA VASCONCELOS, FUNDAÇÃO DE APOIO À PESQUISA E AO DESENVOLVIMENTO; CAIO SIMPLICIO ARANTES, FUNDAÇÃO DE APOIO À PESQUISA E AO DESENVOLVIMENTO; EDUARDO ANTONIO SPERANZA, CNPTIA; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; LUIZ ANTONIO FALAGUASTA BARBOSA, CNPTIA; GERALDO MAGELA DE ALMEIDA CANCADO, CNPTIA.
dc.creatorVASCONCELOS, J. C. S.
dc.creatorARANTES, C. S.
dc.creatorSPERANZA, E. A.
dc.creatorANTUNES, J. F. G.
dc.creatorBARBOSA, L. A. F.
dc.creatorCANÇADO, G. M. de A.
dc.date2026-03-06T17:49:07Z
dc.date2026-03-06T17:49:07Z
dc.date2026-03-06
dc.date2026
dc.date.accessioned2026-07-07T04:18:35Z
dc.descriptionSugarcane (Saccharum officinarum L.) is one of the largest crops in Brazil, and its productivity varies according to the environment and management practices adopted. In this study, tons of sugar per hectare (TSH) are estimated using a heteroscedastic gamma (GA) regression model, which considers several explanatory variables, one of which is the normalized difference green vegetation index (GNDVI), obtained from multispectral images in two locations over two consecutive growing seasons. The modeling considers regression structures in the parameters representing the mean and coefficient of variation, respectively. The results show that there is an influence of location, cultivar, cycle, accumulated precipitation, and GNDVI. To verify if the model is well-fitted to the data, the analysis of quantile residuals shows that the model is adequate. Therefore, the results indicate that heteroscedastic GA regression is an alternative model for predicting TSH and can assist in decision-making in sugarcane cultivation.
dc.identifierSugar Tech, 2026.
dc.identifier0974-0740
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1185146
dc.identifierhttps://doi.org/10.1007/s12355-026-01737-z
dc.identifier.urihttp://hdl.handle.net/123456789/457351
dc.languageeng
dc.rightsopenAccess
dc.subjectÍndice de vegetação
dc.subjectModelo de regressão
dc.subjectNormalized difference green vegetation index
dc.subjectHeteroscedastic GA regression model
dc.subjectSaccharum Officinarum
dc.subjectCana de Açúcar
dc.subjectSugarcane
dc.subjectNormalized difference vegetation index
dc.titleEstimating sugar yield in sugarcane using green normalized difference vegetation index derived from imagery obtained by remotely piloted aircrafts.
dc.typeArtigo de periódico

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