Using Artificial Neural Networks Models for predicting wheat yield

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 ConclusionsThe research assessment in concerning MLP, GRNN and RBNN in the field of wheat yield forecast. The forecast was worked utilizing the climate variables namely; Rain (R), maximum temperature (Tmax), mean temperature (Taver) ,minimum temperature (Tmin), potential evapotranspiration (PET), dew point data (DP), wind speed (WS) and irrigation requirement (IR) for wheat. Data of historical 31 years (1986 to 2012) were collected from standard agricultural meteorological stations of the Agricultural Research Center in the tests station of Sakha Province, Kafer el Shikh Governorate, Egypt, Data of 27 years for the period (1986-2012) were retained to develop the models and the data of four years (2013 and 2016) were expended to evaluate the models, to compare their outputs with the data measured these data did not affect in the process of building neural networks models. Results discovered that the ideal conformation for the FFNN model involved of one layer (8-40-1). The hidden layers had 40 nodes in the hidden layer for the ANN model. Hyperbolic tangent transfer function was engaged in hidden and output layers of the ANN display. The learning rate and the momentum parameter were 0.005 and 0.9 resp. for the ANN model. Iterations were 1000 epochs during training process for the ANN model. The outcome represented that GRNN extant well forecast outcomes as competed to FFNN and RBNN.