Abstract: Abstract: In order to make the C-V model segment plant lesion images more accurate and display quicker, the Gaussian mix model was introduced to set up an improved C-V model in this paper. In view of the drawbacks of long processing time and determining the R, G, B channel energy coefficients artificially for the weighted color information-based C-V model, an improved C-V model based on a Gaussian mix model was proposed in this paper and applied to plant lesion image segmentation. At first, a point in the lesion area was selected and the averages over its 3×3 neighbor was taken as the internal energy in the C-V model. Then the Gaussian mix model was used to model the image, and the sign distance function was initialized by prior probability. Finally, the ratios of the averages on the foreground and background from R, G, B channels were used as the weights of these three channels respectively, and the level set function was iterated to obtain the segmentation contour. To verify the improved C-V model for plant lesion color image segmentation, the traditional C-V model and the weighted color information-based C-V model were used as counterpart algorithms in term of quantitative evaluation of image segmentation, respectively. The experimental results were the averages over ten experiments. In every experiment, two important things had to be decided: one was that the selected point must fall into larger lesion areas, the other was that the Gaussian mix model was calculated by the EM method. The experimental results and analysis on capsicum lesion image and cucumber lesion image mainly lay in the following three aspects. First, for the noised slightly capsicum lesion images, the undetected ratio and the over detected ratio of segmentation for the proposed method were 0.02 lower and 0.01 higher than that of the weighted color information-based C-V model, respectively. For the larger pixels cucumber lesion images, the undetected ratio was about equal to and the over detected ratio was 1.7 lower than that of the weighted color information-based C-V model. The traditional C-V model had the minimum undetected ratio and the maximum over detected ratio. The running time of the proposed method in segmenting capsicum and cucumber lesion images was less than that of the weighted color information-based C-V model and that of the traditional C-V model. Secondly, by making use of the ratios of the averages on foreground and background from R, G, B channels as the weights of these three channels respectively, not only was the contrast between lesion area and background area stressed, but also the randomness to artificially compute the weights through many trials in weighted color information-based C-V model was reduced. Thirdly, the proportion of the target pixels in the entire image was utilized as the final weights of image energy, which decreased the possibility of over-segmentation. Above all, the proposed method in this paper obtained better performance than the weighted color information-based C-V model and the traditional C-V model. Therefore, the algorithm based on C-V and Gaussian mix model provides an effective means to separate the lesions in an image.