Design and Development of Artificial Neural Networking (ANN) System Using Sigmoid Activation Function to Predict Annual Rice Production in Tamilnadu

Abstract

Prediction of annual rice production in all the 31 districts of Tamilnadu is an important decision for the Government of Tamilnadu. Rice production is a complex process and non linear problem involving soil, crop, weather, pest, disease, capital, labour and management parameters. ANN software was designed and developed with Feed Forward Back Propagation (FFBP) network to predict rice production. The input layer has six independent variables like area of cultivation and rice production in three seasons like Kuruvai, Samba and Kodai. The popular sigmoid activation function was adopted to convert input data into sigmoid values. The hidden layer computes the summation of six sigmoid values with six sets of weightages. The final output was converted into sigmoid values using a sigmoid transfer function. ANN outputs are the predicted results. The error between original data and ANN output values were computed. A threshold value of 10-9 was used to test whether the error is greater than the threshold level. If the error is greater than threshold then updating of weights was done all summations were done by back propagation. This process was repeated until error equal to zero. The predicted results were printed and it was found to be exactly matching with the expected values. It shows that the ANN prediction was 100% accurate.