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Prediction of annual rice production in all the 31 districts of Tamilnadu isan important decision for the Government of Tamilnadu. Rice production is acomplex process and non linear problem involving soil, crop, weather, pest,disease, capital, labour and management parameters. ANN software was designedand developed with Feed Forward Back Propagation (FFBP) network to predict riceproduction. The input layer has six independent variables like area ofcultivation and rice production in three seasons like Kuruvai, Samba and Kodai.The popular sigmoid activation function was adopted to convert input data intosigmoid values. The hidden layer computes the summation of six sigmoid valueswith six sets of weightages. The final output was converted into sigmoid valuesusing a sigmoid transfer function. ANN outputs are the predicted results. Theerror between original data and ANN output values were computed. A thresholdvalue of 10-9 was used to test whether the error is greater than the thresholdlevel. If the error is greater than threshold then updating of weights was doneall summations were done by back propagation. This process was repeated untilerror equal to zero. The predicted results were printed and it was found to beexactly matching with the expected values. It shows that the ANN prediction was100% accurate.

Design and Development of Artificial Neural Networking (ANN) system using sigmoid activation function to predict annual rice production in Tamilnadu