Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs

• In this study ANFIS models were developed to model wheat yield on the basis of energy inputs.
• The results of ANFIS model were compared with artificial neural networks.
• Results showed that ANFIS model can predict yield relatively better than ANN model.

Energy is regarded as one of the most important elements in agricultural sector. During the last decades energy consumption in agriculture has increased, so finding the relationship between energy consumption and crop yields in agricultural production can help to achieve sustainable agriculture. In this study several adaptive neuro-fuzzy inference system (ANFIS) models were evaluated to predict wheat grain yield on the basis of energy inputs. Moreover, artificial neural networks (ANNs) were developed and the obtained results were compared with ANFIS models. For the best ANFIS structure gained in this study, R, RMSE and MAPE were calculated as 0.976, 0.046 and 0.4, respectively. The developed ANN was a multilayer perceptron (MLP) with eleven neurons in the input layer, two hidden layers with 32 and 10 neurons and one neuron (wheat grain yield) in the output layer. For the best ANN model, R, RMSE and MAPE were computed as 0.92, 0.9 and 0.1, respectively. The results illustrated that ANFIS model can predict the yield more precisely than ANN.