Abstract: It is necessary to improve the generalization of tomato maturity grading for better consistent criteria in modern agriculture. In this study, an extraction was proposed for red-colored regions from the fruit surfaces using region brightness correction. The R-G method was used to enhance the red areas on the tomato surface, and then the Otsu method was used to segment them. All the colored areas on the tomato surface were obtained to evaluate the relationship between the contours of each red region of tomato after segmentation by the contour tree structure. The proportion of the area of the red region to the total area of the image was calculated as the main feature of maturity grading. The surface red coloring area ratio, color moments, and color histograms were selected to perform the feature importance analysis, in order to screen out the color features with a significant impact on the maturity grading. The random forest model was used to determine and grade the different maturity stages of tomatoes. Among them, the image in-painting technique based on the fast marching method was used to reduce the high brightness reflection of surface that reduced by the illumination changes. The test results showed that the effect of the area ratio of fruit surface coloring areas on the tomato maturity grading was significantly greater than the color histogram and color moment. The coloring area ratio, G-component third order color moments, and G-component color histogram feature posed the greatest impact on the maturity grading, with importance scores of 0.293, 0.199, and 0.127, respectively. Among all color features, the tomato coloring area ratio as a classification index shared the highest classification accuracy, with an average classification accuracy of 92.96%, which was 6.53 and 20.6 percent point higher than the traditional color moment and color histogram indicators. The domain pixel-weighted sum performed an excellent correction effect on the brightness of the tomato-highlighted areas. After correction, the proportion of the fruit surface colored area of immature, slightly mature, and mature tomato images to the total area of the tomato images increased by 0.06, 0.15, and 0.11, respectively, compared with before correction. The classification accuracy was improved by 17.24, 11.47, and 4.69 percent point, respectively. The area extraction of tomato red areas with the image fast restoration can be used as the recommended extraction of tomato maturity grading.