Grading rice grains using a multi-structure neural network approach.

A multi-structure neural network(MSNN) was proposed and applied to classify five classes of rice grains.The MSNN model consisted of five parallel multi-layer feed-forward neural networks(MLNN).With two hidden layers MLNN was trained using morphological and color features of the rice grains extracted from their images as input.The average classification accuracy of MSNN was 92.66%,with an increase of over 5.04 percent points than that of MLNN;moreover the network training time for MSNN was shorter than that for MLNN.