In order to segment uneven colored apple fruits in natural environment, the fruit segmentation method based on image features extracted from superpixels was proposed for apple harvesting robot. Firstly, simple linear iterative clustering (SLIC), which was one of superpixel clustering algorithm was employed to segment original images into a set of superpixels. The color of pixels in the same superpixel was uniform relatively. Then, the color and texture features of superpixels were extracted. According to combined feature vectors, these superpixels were classified into fruit class and non-fruit class by support vector machine (SVM). Finally, the classification results were modified based on the adjacency relation of superpixels. The segmented fruits were made up of a set of superpixels in fruit class. The experiment results showed that the proposed method can classify a majority of superpixels and there were average of 2.28 superpixels in one image were classified falsely. Compared with the segmentation method based on pixel-level features and the segmentation method based on features of neighborhood pixels, the proposed method based on superpixel features had a better performance on fruit segmentation. The experiment of image segmentation with 100 images indicated that the precision and recall of proposed method can reach 0.9214 and 0.8565 respectively before modifying classification results. The running time of proposed method was 0.6087s per image.