Rice quality evaluation August 21, 2008
Posted by whaldsz in : research , trackbackPrior to the previous post, I also finished a paper entitled “Automatic milled rice quality evaluation” that’s about to appear in one international conference proceedings. The idea is to build a low-cost, efficient, and reliable alternative for the milled rice quality evaluation. As you know, some rice quality standards require the grading factors or defectives in terms weight (grams) , making this problem a very challenging and interesting one! Read on, some results are discussed in the succeeding paragraphs.
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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