Please use this identifier to cite or link to this item:
|Title:||Neuro-Fuzzy Modeling for Crop Production Estimation|
|Authors:||STATHAKIS DIMITRIOS; SAVIN IGOR; NEGRE THIERRY|
|Citation:||The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences vol. 34 p. 358-361|
|Publisher:||International Society for Photogrammetry and Remote Sensing (ISPRS)|
|Type:||Contributions to Conferences|
|Abstract:||The 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.|
|JRC Institute:||Institute for the Protection and Security of the Citizen|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.