Abstract: In natural green apple images, the color difference between targets and background is small, which increases the difficulty of segmenting out interested objects. To overcome this problem, a novel apple image segmentation method based on multi-feature fusion and Random Forest (RF) is proposed. Firstly, six texture features of energy, entropy, contrast, correlation, entropy, the standard deviations of entropy and contrast are extracted from the raw image based on Gray Level Co-occurrence Matrix (GLCM). Secondly, the color components of G+0.5R-B in RGB space and S+I in HSI space are computed as the combined color features to avoid the influence of sky and highlight area to the segmentation result. Then, the extracted texture features and color features are fused together in pixels. Finally, the image segmentation is performed by applying Random Forests to the fused features and the segmentation results are compared with the traditional method which only considers the unique kind of features. Extensive experimental results qualitatively and quantitatively demonstrate the advantages of our algorithm by a significant improvement of 22.18% segmentation accuracy than the one only with texture features.