Abstract: Assessment of fruit ripeness based on fruit size and color indicators provides a numerical basis for optimal harvesting time in cherry storage and transportation. The improved intelligent scissors algorithm was used to segment the fruit feature regions from the light background at different periods. The image projection area of the feature area, the variance and mean of the H component of the image under the HSV color space model were calculated. Based on the obtained fruit area and color index data, the weighted assessment method was used to calculate the fruit maturity at different periods. Each index changed dynamically during fruit development. The ripeness reached the maximum when the fruit area reached 190 000 pixels and the skin color was purple-red. Real-time access to indicators for dual assessment of cherry fruit ripeness effectively enables digital assessment of fruit ripeness.