Simulation of Growth Stimulating Bacteria on Corn using Artificial Neural Network

Many studies have been done to identify factors affecting the growth and development of crops such as corn. Considering the special position of maize, determining the effect of these factors is very important. . Since the study of these studies in farms and laboratories is time consuming and costly, the use of artificial neural network is important as a quick and non-destructive way to simulate factors affecting crop production and predict experimental results. The purpose of this study was to assess the predictive power of growth stimulating bacteria on the yield of corn fodder using artificial neural network. After obtaining the required data from experimental studies, Each of the input and output parameters included independent variable(the effect of growth promoting bacteria and effective factors) and dependent variable(forage yield per hectare) data mining were data mining, Then a multilayered perceptron neural network(MLP) with a sigmoid transformation function was designed in the number of neurons 5, 10, 15, 20, 25 in the secret layer. The results showed that the network(MLP) with an error correlation training algorithm with a correlation coefficient(R= 0.999) and mean square error(MSE= 0/000113) With 10 neurons in the hidden layer as the optimum model was able to predict corn forage yield affected by growth-promoting bacteria, Also, in the simulation results with the new input parameters, there was very little difference between the actual output values and the predicted output, Therefore, the artificial neural network in this study has been able to estimate the effect of growth stimulating bacteria on corn, and the resulting neural network can be used to predict other influential factors.