The Visioneers » Weight estimation http://www.oliveragustin.com Tech, IT, Security, Research, Trends and Tips Thu, 26 Jan 2012 10:45:23 +0000 en hourly 1 http://wordpress.org/?v=3.2.1 Rice quality evaluation http://www.oliveragustin.com/automatic-milled-rice-quality-evaluation/ http://www.oliveragustin.com/automatic-milled-rice-quality-evaluation/#comments Thu, 21 Aug 2008 14:33:33 +0000 whaldsz http://www.oliveragustin.com/?p=16 Prior 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.

automatic milled rice evaluation framework

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.

Acquired image and background-segmented image

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

Different milled rice kernel segmented images

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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]]> http://www.oliveragustin.com/automatic-milled-rice-quality-evaluation/feed/ 0 Weight estimation of milled rice http://www.oliveragustin.com/weight-estimation-and-classification-of-milled-rice/ http://www.oliveragustin.com/weight-estimation-and-classification-of-milled-rice/#comments Wed, 20 Aug 2008 17:10:03 +0000 whaldsz http://www.oliveragustin.com/?p=15 This article presents a method for weight estimation and classification of milled rice kernels using supervised learning algorithm. Shape descriptors are used as geometric features for determining the grade factors such as headrice, broken kernel, and brewer. Color histogram is extracted from milled rice image to obtain 24 color features in RGB and Cielab color spaces. A support vector machines (SVM) is adopted for addressing the classification and regression problem in milled rice quality evaluation. We built a support vector regression (SVR) model for estimating rice kernel weight and support vector classifier (SVC) for rice defectives.

Results showed that in real data, the performance of SVR is better than linear regression (LR) with a mean square error (MSE), mean absolute error (MAE) and correlation coefficient of 0.078, 0.21 and 99.4%, respectively. For classification of rice defectives using SVC, the accuracy is 98.9% outperforming the general regression neural network (GRNN) model.

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