Yield prediction of wheat in south-east region of Turkey by using artificial neural networks

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Abstract

In Turkey, similarly to other grain producing countries, the prediction of wheat yield is an important problem. The objective in this study is to build an artificial neural network model that could effectively predict wheat yield by using meteorological data such as temperature and rainfall records. Multi-Layer Perceptron neural network model was chosen and the performance of the built network was tested for different input and neurons number. For defining the model parameters back propagation training technique was used. During the training of the network, various learning rates were chosen and the optimal values for these parameters were defined. For the final assessment of the obtained results a multiple parameter linear regression model was developed and tested with the same data set used for the built artificial neural network.

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