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• W2030473669 startingPage "903" @default.
• W2030473669 abstract "In this study adaptive neuro-fuzzy inference system (ANFIS) was employed to forecast greenhouse strawberry yield on the basis of different combination of energy inputs. Also, an artificial neural networks (ANNs) model was developed and generalized to compare their results with proposed ANFIS model. Data for the present study was randomly collected from 33 greenhouses from Guilan province, Iran. Energy inputs used in strawberry cultivation included labor, chemical fertilizers, diesel fuel, machinery, biocides, electricity, natural gas and water for irrigation which were all selected as input parameters of the models and correspondingly strawberry yield was considered as output variable. Coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were calculated as 0.9121, 0.0667, 0.0388 and 0.0084 for ANN model and they were calculated as 0.963, 0.017, 0.014 and 0.003 for ANFIS model. Finally, a comparison between developed ANN and proposed ANFIS was made and the outcomes disclosed that ANFIS model can predict strawberry yield relatively better than does ANN model." @default.
• W2030473669 created "2016-06-24" @default.
• W2030473669 creator A5009537783 @default.
• W2030473669 creator A5015983534 @default.
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• W2030473669 date "2014-01-01" @default.
• W2030473669 modified "2023-10-16" @default.
• W2030473669 title "Application of multi-layer adaptive neuro-fuzzy inference system for estimation of greenhouse strawberry yield" @default.
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• W2030473669 doi "https://doi.org/10.1016/j.measurement.2013.10.018" @default.
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