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

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.

• Publication year

  2014

• Venue

  Information Processing in Agriculture

• Publication date

  2014-08-01

• Fields of study

  Agricultural and Food Sciences, Computer Science, Engineering

• Identifiers
  DOI 10.1016/J.INPA.2014.04.001
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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