  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.

Neural and Evolutionary Computing