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dc.contributor.authorSTATHAKIS DIMITRIOSen_GB
dc.contributor.authorSAVIN IGORen_GB
dc.contributor.authorNEGRE THIERRYen_GB
dc.date.accessioned2010-02-25T17:24:24Z-
dc.date.available2007-06-01en_GB
dc.date.available2010-02-25T17:24:24Z-
dc.date.issued2006en_GB
dc.date.submitted2007-05-22en_GB
dc.identifier.citationThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences vol. 34 p. 358-361en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC36837-
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.description.sponsorshipJRC.G.3-Agricultureen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.publisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)en_GB
dc.relation.ispartofseriesJRC36837en_GB
dc.titleNeuro-Fuzzy Modeling for Crop Production Estimationen_GB
dc.typeArticles in periodicals and booksen_GB
JRC Directorate:Space, Security and Migration

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