One of the necessary stages of doing digital image processing is laid on the preprocessing phase, in which one of those techniques is segmentation. Segmentation plays an important role in separating a tomato as object and background. When capturing tomato image at outdoor, the segmentation for each device can be different and causes lower performance of maturity clustering problem. This paper proposes new framework in to enhance the evaluation measure of clustering by using combined segmentation of V and Cb color Channel. V color channel is taken from YUV color space, while Cb is obtained from YCbCr color space. Both two color channels are segmented using Otsu thresholding since Otsu is robust to segment each tomato image. Color feature extraction is then applied to the retrieved color of the segmented image. In this paper, RGB and L*a*b* color space model are used to get the feature, but only R, G, a*, b* color channel are examined. The 6-Means clustering algorithm is also applied to agglomerate tomato maturity based on six color levels. There are four smartphone cameras to capture the tomato image with unconditional lighting condition at outdoor. The experimental result shows that the smallest value from the average of Mean Square Error (MSE) in all devices reach 3.135. This indicates that the new framework can cluster the tomato maturity with only use G and B color channel.