Fecha/hora: 11/06/2024 21:23:54

Biblioteca(s): 	INIA La Estanzuela.	
Fecha corriente: 	05/06/2018	
Fecha de la última actualización: 	27/01/2021	
Autoria: 	BERGER, A.; ETTLIN , G.; QUINCKE, CH.; RODRÍGUEZ-BOCCAB, P.	
Afiliación: 	ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUILLERMO ETTLIN, Instituto de Computación, Facultad de Ingeniería, Universidad de la República.; CHRISTOPHER QUINCKE, Instituto de Computación, Facultad de Ingeniería, Universidad de la República.; PABLO RODRÍGUEZ-BOCCAB, Instituto de Computación, Facultad de Ingeniería, Universidad de la República.	
Título: 	Predicting the Normalized Diference Vegetation Index (NDVI) by training a crop growth model with historical data.	
Año de publicación: 	2019	
Fuente/Imprenta: 	Computers and Electronics in Agriculture, Volume 161, June 2019, Pages 305-311, 2019.Doi: https://doi.org/10.1016/j.compag.2018.04.028	
DOI: 	10.1016/j.compag.2018.04.028	
Idioma: 	Inglés	
Notas: 	Article history: Received 29 December 2017/ Revised 17 April 2018/ Accepted 29 April 2018/ Available online 10 May 2018.	
Contenido: 	ABSRACT:
Normalized Difference Vegetation Index (NDVI) is an important remote measurement in agriculture because it has a high correlation with crop growth and yield result. In this paper, we present a methodology to predict the NDVI by training a crop growth model with historical data. Although we use a very simple soybean growth model, the methodology could be extended to other crops and more complex models. The training process is an optimization problem, that is solved using the spectral projected gradient method. The quality of the prediction
is measured by computing the Root-Mean-Square Error (RMSE) between predicted and true values, obtaining an error lower than 9%, which improves the results obtained by simple forecast techniques used as baseline estimators.	
Palabras-Clave: 	CROP GROWTH; CROP GROWTH MODEL; ÍNDICE DE VEGETACIÓN; NORMALIZED DIFFERENCE VEGETATION INDEX; PREDICTIVE ANALYSIS; REMOTE MEASUREMENT; ROOT MEAN SQUARE ERRORS; SOYBEAN CROP; SPECTRAL PROJECTED GRADIENT METHOD; TRAINING PROCESS.	
Thesagro: 	CULTIVOS; GLICINE MAX; MEJORAMIENTO DE CULTIVOS; SOJA.	
Categoría del tema: 	F01 Cultivo	
Registro original: 	INIA La Estanzuela (LE)	
Ejemplares: 	LE - (102472-1) - PXI - AP - DD - PP/Comp. Elec. Agric./2019 -  -  - DIS