Título:	Predicting sugarcane yield through temporal analysis of satellite imagery during the growth phase.
Autoria:	VASCONCELOS, J. C. S. ARANTES, C. S. SPERANZA, E. A. ANTUNES, J. F. G. BARBOSA, L. A. F. CANÇADO, G. M. de A.
Afiliação:	JULIO CEZAR SOUZA VASCONCELOS, FUNDAÇÃO DE APOIO A PESQUISA E AO DESENVOLVIMENTO; CAIO SIMPLICIO ARANTES, FUNDAÇÃO DE APOIO A PESQUISA E AO DESENVOLVIMENTO; EDUARDO ANTONIO SPERANZA, CNPTIA; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; LUIZ ANTONIO FALAGUASTA BARBOSA, CNPTIA; GERALDO MAGELA DE ALMEIDA CANCADO, CNPTIA.
Ano de publicação:	2025
Referência:	Agronomy, v. 15, n. 4, 793, Apr. 2025.
Conteúdo:	This research investigates how to estimate sugarcane (Saccharum officinarum L.) yield at harvest by using an average satellite image time-series collected during the growth phase. This study aims to evaluate the effectiveness of various modeling approaches, including a heteroskedastic gamma regression model, Random Forest, and Artificial Neural Networks, in predicting sugarcane yield based on satellite-derived vegetation indices and environmental variables. Key covariates analyzed include sugarcane varieties, production cycles, accumulated precipitation during the growth phase, and the mean GNDVI vegetation index. The analysis was conducted in two locations over two consecutive growing seasons. The research emphasizes the integration of satellite data with advanced statistical and machine learning techniques to enhance yield prediction in agricultural systems, specifically focusing on sugarcane cultivation.
Thesagro:	Cana de Açúcar Agricultura de Precisão Saccharum Officinarum
NAL Thesaurus:	Sugarcane Crop yield Statistical models
Palavras-chave:	Agricultura digital Rendimento de cultura Modelo estatístico Aprendizado de máquina Digital agriculture Machine learning
Digital Object Identifier:	https://doi.org/10.3390/agronomy15040793
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPTIA)