Rice Sample Segmentation and Classification Using Image Processing and Support Vector Machine

Abstract

Rice is one of the most consuming and important cereal grains for human being in Asian countries. In the international and national rice market, milling process is evaluated by using quality of the rice. Therefore, rice quality identification is more important. Rice quality identification is done manually by human inspectors which ensures the accuracy at some extent. But it requires a lot of man power, time consumption and judgements are subjective. Rice sample is a combination of full rice, broken rice, damaged rice, paddy, stones and foreign objects. A rice sample need to classify in to these six groups in order to identify rice quality. This paper provides an approach to separate and classify objects of rice sample based on color and texture features with the help of image processing and machine learning techniques. This method starts with image acquisition using CCD camera. Gray scale conversion, noise reduction, binarization, morphological operations are applied on the acquired images. Contours of the objects are estimated by using contour detection. Watershed algorithm is used to segmentation of touching and overlapping rice kernels. Local Binary Pattern (LBP) texture feature and color features extracted from segmented images. These features used to predict the rice sample objects using Linear Kernel based Support Vector Machine (SVM). The experiment performed on six rice categories to evaluate the suggested solution. The accuracy of segmentation and classification is 96.0% and 88.0% respectively.