Tomato ripeness clustering using 6-means algorithm based on v-channel otsu segmentation

Abstract

Segmentation process in an essential part in image processing to obtain good preparation either for further process of data mining or object recognition. This paper proposes a new method of segmenting tomato image for clustering its ripeness. The tomato images are taken from three types of smartphone camera in various lighting condition with white background. When taking picture by using smartphone camera, the image is a bit darker or lighter in certain side, so the segmentation is involved to the following stage. Color transformation is needed at the first stage of preprocessing which converts RGB channel to YUV channel in order to apply histogram equalization. YUV is better to perceptual similarities in machine vision than RGB. Histogram equalization is applied in single Y channel of an image. Afterwards merge a V channel to YUV channel then transform it to RGB color model to observe the difference and convert it back to YUV for segmentation. Otsu combined with V channel thresholding is utilized to segment image better. To evaluate the segmentation performance, clustering method is computed based on retrieved color of segmented image using K-Means, in which k=6 because of there are 6 stages of tomato ripeness. Color feature extraction by means of R, G, a∗, and b∗ color channel are treated subsequently. Experimental results show the system yield 1% Mean Square Error in clustering the ripeness of tomatoes.