USING ARTIFICIAL NEURAL NETWORKS MODELS FOR PREDICTING WHEAT YIELD PRODUCTIVITY
Genaidy, M. (2020). USING ARTIFICIAL NEURAL NETWORKS MODELS FOR PREDICTING WHEAT YIELD PRODUCTIVITY. EKB Journal Management System, 28(3), 767-775. doi: 10.21608/ajs.2020.153538
Mohamed Genaidy. "USING ARTIFICIAL NEURAL NETWORKS MODELS FOR PREDICTING WHEAT YIELD PRODUCTIVITY". EKB Journal Management System, 28, 3, 2020, 767-775. doi: 10.21608/ajs.2020.153538
Genaidy, M. (2020). 'USING ARTIFICIAL NEURAL NETWORKS MODELS FOR PREDICTING WHEAT YIELD PRODUCTIVITY', EKB Journal Management System, 28(3), pp. 767-775. doi: 10.21608/ajs.2020.153538
Genaidy, M. USING ARTIFICIAL NEURAL NETWORKS MODELS FOR PREDICTING WHEAT YIELD PRODUCTIVITY. EKB Journal Management System, 2020; 28(3): 767-775. doi: 10.21608/ajs.2020.153538

USING ARTIFICIAL NEURAL NETWORKS MODELS FOR PREDICTING WHEAT YIELD PRODUCTIVITY

Arab Universities Journal of Agricultural Sciences
Article 5, Volume 28, Issue 3, September 2020, Page 767-775  XML PDF (903.02 K)
Document Type: Original Article
DOI: 10.21608/ajs.2020.153538
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Author
Mohamed Genaidy email orcid
Agric. Engineering Dept., Fac. of Agric., Ain Shams Univ., P.O. Box 68, Hadayek Shoubra 11241, Cairo, Egypt
Abstract
Artificial intelligent provides diverse solutions for the complex problems in agriculture research. The study aimed to use three models of artificial neural networks (Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN) and Radial-Basis Neural Network (RBNN)) in the field of wheat yield prediction. 27-year data for the period (1986-2012) were utilized to improve the models and four-year data (2013 and 2016) were used to estimate the models, to compare their outputs with the measured data. Prediction data was not entered in the process of building neural network models. The results showed that the optimal configuration of the FFNN model consists of 40 neurons in the hidden layer (8-40-1). The Tan Sigmoid activation function was used in both the hidden layer and the output layer using all of these models (anterior neural feeding network and the regression neural network and radial base neural network) in the 4-year wheat yield forecast field for production (2013-2016) by applying 8 input parameters that were result of NMMS (8.6%, 7.6% and 15.7% resp.), To find that FFNN and GRNN provide the best result from BRNN because while the information set was large or in a wide range, then the range data ranges from -1 to +1 (normalization data) , GRNN gives better outcomes after the information or sample data were in large range.
Keywords
Wheat yield; Forecast; Artificial neural networks; Feed-forward Back propagation
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