<![CDATA[The increased of consumer concern on the originality of rice variety and the quality of rice leads to originality certification of rice by existing institutions. Technology helps human to perform evaluations of food grains using images of objects. This study developed a system used as a tool to identify rice varieties. Identification process was performed by analyzing rice images using image processing. The analyzed features for identification consisted of six color features, four morphological features, and two texture features. Classifier used LVQ neural network algorithm. Identification results using a combination of all features gave average accuracy of 70,3% with the highest classification accuracy level of 96,6% for Mentik Wangi and the lowest classification accuracy of 30% for Cilosari.]]>
