Caliper Software for Milled-Rice – Counts the number of grains, headrice and broken kernels September 14, 2008
Posted by whaldsz in : projects, software, technology , 1 comment so far
This is an application for counting milled rice grains, determining the count of headrice, broken, and brewers. Using these grade factors, the caliper software (as I call it) is able to estimate the total weight in terms of percentage. Weight estimation of rice grain is performed using linear regression and support vector machines (SVM). It uses AForge library from AForge.NET for various image processing task.
I did this software for Bureau of Post-harvest Research Extension (BPRE) and indirectly, to National Food Authority (NFA) for the purpose of quickly determining the grain size of milled rice.
The demo and initial version of the caliper software is available for download here:
To use the software, simply install the application, then open the sample image included in the installation (“bigas.bmp”), then that’s it! You can try it for other similar problems, like corn, barley, etc. Please give me feedback if problems arises. For any questions, you may email me at {vlad_crasher at yahoo point com}.
UPDATE: Yesterday (September 15, 2008), they told me that the test was successful! The classification result did matched with the manual methods performed by human inspector.
Rice quality evaluation August 21, 2008
Posted by whaldsz in : research , add a commentPrior 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.
Weight estimation of milled rice August 20, 2008
Posted by whaldsz in : research , add a commentThis 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.