Classification of Osmancik and Cammeo Rice Varieties using Deep Neural Networks

Abstract

Rice is one of the most widely consumed grains in the world. It is globally known that countries in southern Asia are the ones that mostly produce and also consume this particular type of grain. About 800 million tons of rice in many varieties is produced in the world every year. Each variety has its unique characteristics. This study covers research on the classification of Osmancik and Cammeo rice varieties using Deep Neural Networks (DNNs). There are 3810 numerical data of which 2180 belong to Osmancik and 1630 to Cammeo in the University of California Irvine (UCI) Rice (Osmancik and Cammeo) Data Set that is used in this work. The data is subjected to a normalization process which improves the performance of the multilayer neural networks. The performance of this study is measured thru calculating accuracy, sensitivity, specificity, precision, F1-score, NPV, FPR, FDR and, FNR. The overall success rate of this study is found to be 93.04%.