02044naa a2200349 a 450000100080000000500110000800800410001902400380006010000150009824501410011326000090025450001250026352007810038865000130116965000160118265000290119865000090122765300160123665300220125265300270127465300430130165300240134465300230136865300280139165300170141965300390143665300210147570000160149670000170151270000260152977301390155510586662021-01-27       2019    bl uuuu     u00u1 u    #d7 a10.1016/j.compag.2018.04.0282DOI1 aBERGER, A.  aPredicting the Normalized Diference Vegetation Index (NDVI) by training a crop growth model with historical data.h[electronic resource]  c2019  aArticle history: Received 29 December 2017/ Revised 17 April 2018/ Accepted 29 April 2018/ Available online 10 May 2018.  aABSRACT: 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.  aCULTIVOS  aGLICINE MAX  aMEJORAMIENTO DE CULTIVOS  aSOJA  aCROP GROWTH  aCROP GROWTH MODEL  aÍNDICE DE VEGETACIÓN  aNORMALIZED DIFFERENCE VEGETATION INDEX  aPREDICTIVE ANALYSIS  aREMOTE MEASUREMENT  aROOT MEAN SQUARE ERRORS  aSOYBEAN CROP  aSPECTRAL PROJECTED GRADIENT METHOD  aTRAINING PROCESS1 aETTLIN , G.1 aQUINCKE, CH.1 aRODRÍGUEZ-BOCCAB, P.  tComputers and Electronics in Agriculture, Volume 161, June 2019, Pages 305-311, 2019.Doi: https://doi.org/10.1016/j.compag.2018.04.028