Title:	Estimating sugar yield in sugarcane using green normalized difference vegetation index derived from imagery obtained by remotely piloted aircrafts.
Authors:	VASCONCELOS, J. C. S. ARANTES, C. S. SPERANZA, E. A. ANTUNES, J. F. G. BARBOSA, L. A. F. CANÇADO, G. M. de A.
Affiliation:	JULIO 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.
Date Issued:	2026
Citation:	Sugar Tech, 2026.
Description:	Sugarcane (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.
Thesagro:	Saccharum Officinarum Cana de Açúcar
NAL Thesaurus:	Sugarcane Normalized difference vegetation index
Keywords:	Índice de vegetação Modelo de regressão Normalized difference green vegetation index Heteroscedastic GA regression model
ISSN:	0974-0740
DOI:	https://doi.org/10.1007/s12355-026-01737-z
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPTIA)