Title:	Development and validation of a model based on vegetation indices for the prediction of sugarcane yield.
Authors:	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.
Affiliation:	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.
Date Issued:	2023
Citation:	AgriEngineering, v. 5, n. 2, p. 698-719, June 2023.
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.
Thesagro:	Cana de Açúcar Saccharum Officinarum
NAL Thesaurus:	Sugarcane Vegetation index Models
Keywords:	Agricultura digital Modelo preditivo Distribuição gaussiana inversa Remotely piloted aircraft systems RPAS Digital agriculture Inverse Gaussian distribution
DOI:	https://doi.org/10.3390/ agriengineering5020044
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPTIA)