In order to improve the carrot automatic grading system based on computer vision, which refers to the grading standard of purchases and sales of carrots, this article proposed a detection method of carrot with green-shoulder, fibrous roots and surface cracks which impact the carrot appearance grading greatly. Five hundred and twenty carrots were selected randomly as testing samples, and their photos were taken by camera for the next step of processing and research. To detect the fibrous roots, the skeleton was extracted from the binary image after necessary image preprocessing and binariazation. Based on the fact that normal carrots have 5 end points of skeleton, fibrous roots are detected by computing the number of the skeleton end points. The number of fibrous roots is computed by subtracting 5 from the number of skeleton end points and then divided by 2, which is taken as the measure of whether there are fibrous roots on the carrot image. Green-shoulders of carrots can be distinguished by the green color which is very obvious on the R component image, so it is detected by thresholding the R component image of the carrot and computing the area of the green-shoulder region. The ratio of the area of the green-shoulder region and whole carrot region is defined as green-shoulder ratio to measure whether there is green-shoulder on the carrot. As for surface cracks, surface cracks are more obvious on S component images than on the other components, so they are detected by region marking on the S component image and computing the area of surface cracks. The ratio of the area of cracks and the whole carrot is defined as the degree of surface cracking to measure whether there are cracks on the carrot surface. For each carrot image, the three quantitative criteria mentioned above are computed for statistics and analysis, and then the detection accuracy rates of the three criteria are tested. The result show that the detection accuracy rate for the green-shoulder, the fibrous roots and carrot surface cracking defects detection are 97.5%, 81.8%, and 92.3%, respectively. It is showed from the result that the detection algorithms of green-shoulder and fibrous roots have higher accuracy rates than that of surface cracks and have reached above 92%. However, the detection results of cracks are not ideal because the color of cracks area is similar to that of the normal surface and are not obvious on the two-dimensional image. Finally, the detection rates of the three criteria are calculated synthetically. Result shows that the overall accuracy rate can reach 91.3%, which can meet the need of defect detection. The method proposed in this paper has positive significance for the research of carrot appearance quality grading system and product line.% 为了实现基于计算机视觉的胡萝卜外观品质自动分级系统，基于图像处理的方法，参照国家标准（SB/T10450-2007），该文提出影响胡萝卜外观等级的须根、青头、开裂等关键参数的提取算法。须根检测算法通过提取骨架检测端点数来实现，青头检测算法通过R分量上二值化得到，开裂检测算法使用S分量结合区域标记的方法完成，在此基础上构建了须根数、青头比和开裂度3个量化标准，对试验随机采集的520个胡萝卜图像的青头、须根和开裂进行检测，正确率分别达到了97.5%，81.8%，92.3%，总体识别率91.3%。该文所构建的胡萝卜关键特征检测方法，对研究机器视觉的胡萝卜外观品质自动检测装置与分级生产线具有积极意义。
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