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dc.contributor.authorSAVIN IGORen_GB
dc.contributor.authorSTATHAKIS DIMITRIOSen_GB
dc.contributor.authorNEGRE THIERRYen_GB
dc.contributor.authorISAEV Viacheslaven_GB
dc.identifier.citationDoklady RASHN no. 6 p. 11-14en_GB
dc.description.abstractThe purpose of this paper is to explore the dynamics of neural networks in forecasting crop (wheat) yield using remote sensing and other data. We use the Adaptive Neuro-Fuzzy Inference System (ANFIS). The input to ANFIS are several parameters derived from the crop growth simulation model (CGMS) including soil moisture content, above ground biomass, and storage organs biomass. In addition we use remote sensing information in the form of the Normalized Difference Vegetation Index (NDVI). ANFIS has only one output node, the yield. In other words a single number is sought. An additional difficulty in predicting yield is that remote sensing data do not go long back in time. Hence any predicting effort is forced to use a very limited number of past years in order to construct a model to forecast future values. The system is trained by leaving one year out and using all the other data. We then evaluate the deviation of our estimate compared to the yield of the year that is left out. The procedure is applied to all the years and the average forecasting accuracy is given.en_GB
dc.titlePrognozirovanie Urozhainosti Selskohoziaistvennyh Kultur s Ispolzovaniem Neironnyh Setei (Forecasting Farm Crop Yield by the Use of Neural Networks)en_GB
dc.typeArticles in periodicals and booksen_GB
JRC Directorate:Space, Security and Migration

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