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96.20%。 The quality of rice is the main factor that affects the market price of rice. Today, the detecting and grading of rice are mainly carried out by manual ways, which is time-consuming and toilful, and even easily leads to improper judgment. A detecting system of rice quality based on computer vision was developed in this paper. The methods that segmenting single kernel from mass rice image using gray transformation, automatic threshold segmentation, and region marking were discussed. In order to detect the head rice ratio, ten parameters were selected from the profile of rice kernels, such as the area and perimeter of rice kernel, the two axes of the equivalent oval, the inspection of the profile of rice kernel and head rice rate were discussed after using the principal components of the profile parameters of rice kernel. The results of detecting experiments on five varieties of rice indicated that the accurate ratio of detecting fissure is about 96.41%, the accurate ratio of chalkiness detecting is about 94.79%, and the correct ratio of detecting head rice is about 96.20%.