Prediction of crop yields with the use of neural networks
I. Yu. Savin, Demetris N. Stathakis, Thierry Nègre, В. А. Исаев · Russian Agricultural Sciences · 2007
The possibilities of the combined use of neural networks and fuzzy set theory in the form of constructing a so-called fuzzy neural network (FNN) or granular neural network (GNN) [1] for predicting crop yields in the Rostov oblast and Krasnodar and Stavropol krais are examined. The results of modeling plant growth on the basis of the CGMS simulation model as well as the values of the vegetation index NDVI, calculated from the SPOT VEGETATION satellite data, are the input parameters. As a result of training the neural network, the accuracy of predicting yields is on average about 75%.