Development of Artificial Neural Network Models for Estimation of Yield of Cotton

In this paper, Artificial Neural Network (ANN) models were developed to estimate the yield of cotton. The climatological parameters considered to predict the yield of cotton were maximum temperature, minimum temperature, wind velocity and relative humidity and sunshine hours. An attempt has been made to develop the ANN model with the best network architecture, to predict the yield of cotton, considering the above given climatological parameters, as input. The models performance were evaluated using performance indices such as coefficient of correlation (r), coefficient of determination (R2) and discrepancy ratio (D. R). From the results, it can be concluded that the best fit ANN model is generalized regression model, with r, R2 and D. R. as 0.9770, 0.9545 and 1.003 during training and 0.9287, 0.8624 and 0.9977 during validation respectively.