Predicting sugarcane yield through temporal analysis of satellite imagery during the growth phase.

Vista sencilla de ítem
dc.contributorJULIO 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.
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.date2025-03-24T12:31:21Z
dc.date2025-03-24T12:31:21Z
dc.date2025-03-24
dc.date2025
dc.date.accessioned2026-07-07T04:18:08Z
dc.descriptionThis 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.
dc.identifierAgronomy, v. 15, n. 4, 793, Apr. 2025.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1174128
dc.identifierhttps://doi.org/10.3390/agronomy15040793
dc.identifier.urihttp://hdl.handle.net/123456789/457051
dc.languageeng
dc.rightsopenAccess
dc.subjectAgricultura digital
dc.subjectRendimento de cultura
dc.subjectModelo estatístico
dc.subjectAprendizado de máquina
dc.subjectDigital agriculture
dc.subjectMachine learning
dc.subjectCana de Açúcar
dc.subjectAgricultura de Precisão
dc.subjectSaccharum Officinarum
dc.subjectSugarcane
dc.subjectCrop yield
dc.subjectStatistical models
dc.titlePredicting sugarcane yield through temporal analysis of satellite imagery during the growth phase.
dc.typeArtigo de periódico

Archivos

Colecciones

Embrapa Agricultura Digital (CNPTIA)