This is the block diagram of the milled rice quality evaluation system, comprised of 6 different stages.

Sample milled rice image is shown below, (a) acquired from scanner and (b) background-segmented image.  It is important that unnecessary objects are removed prior to further image processing.

These are the segmented kernel images prior to features extraction.  Different types of kernels are obviously visible, paddy, chalky, damaged, broken, good/sound kernels.

We then use the extracted features from the above kernel images to classify according to defective types.  The result is then processed to determine the estimated weight.  Finally, based on these estimates, we judge according to rice quality standards of course, whether the rice is premium grade, grade 1, grade 2, etc.  The result in terms of weight estimates were quite exceptional and the classification, too!  However, I’m still looking for a much better approach that gives better accuracy.  I built my classifier using probabilistic neural network (PNN).  And oh, I developed this system in C#.

Any better suggestion for improving my regression and classifier?  I welcome your comments and suggestions….

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The software is used for determining the grain size of a given milled rice sample.  Additional information about other milled rice grade factors are also provided as follows:

1. Total number of kernels and weight of the milled rice sample
2. Number of headrice and weight
3. Number of broken kernels including its total weight
4. Brewers

I will provide more information soon.  Keep posted!  And hey… feel free to comment.  Tell me what you think!

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