Predicting rainfed barley crop yield using Artificial neural network and fuzzy neural systems in Khorasan provinces-Iran

Article Type:
Case Study (بدون رتبه معتبر)

In this research, we try to predict the yield of rainfed barley in Khorasan provinces using climatic parameters and two methods of artificial nervous netwework (Ann) and fuzzy neural system (Anfis). Calculations were performed with MATLAB software and then the statistical indices of correlation coefficient (R2), root mean square error (RMSE) and full mean error (MAE) were used to evaluate the performance of the models. Last year's yield and rainfall had an effective role in reducing prediction error and increasing correlation coefficient in both Ann and Anfis methods. Last year's yield and evapotranspiration made the Anfis method more accurate than the Ann method. The results of both Anfis and Ann methods for model L inputs, which included rainfall, relative humidity and last year's yield, showed that this model achieved the highest accuracy among the input models. However, in the Anfis method for model E inputs, which included evapotranspiration, rainfall, relative humidity and minimum temperature, the results showed that it was more accurate than the Ann method. The greatest difference in accuracy in estimating yield between the two Anfis and Ann methods was observed with R inputs model, which includes moisture inputs, Dew point temperatures and maximum temperatures. The presence of radiation parameters at the inputs reduced the accuracy of yield estimation in both methods. Overall, the Anfis method was more accurate in estimating yield than Ann.

Journal of Plant Production Science, Volume:11 Issue: 2, 2022
75 to 92  
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