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82.1%。Automatic recognition research on distinguishing copperleaf herb from cotton was developed by machine vision based on the different color features. The binary images were obtained by segmenting five feature images, which were the color-difference methods (R—G, R—B, G—B), the super-green’s method (2G—R—B), and chromatometry (H) respectively. The chromatometry feature images segmented by Otsu’s method could achieve better results by comparing. The double precision matrix as 0, 1 was created with the corresponding binary image, and multiplied by the component plans of R, G and B respectively. The gray images were gained. Their foregrounds were the component plans of R, G and B and their backgrounds were black. The standard deviations of R, G and B in the foregrounds of the cotton and the copperleaf herb images were analyzed. The threshold value for the judgment of the copperleaf herb, which was the margin between R’s standard deviation and B’s standard deviation less than 5, was determined. The identifiable results show that the recognition rates of the cotton and the copperleaf herb are 71.4% and 92.9% respectively, and the overall recognition rate is 82.1%.