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89.4%。During the production and storage of Chinese dates, some of them are easy to mould rot because of high water content. The defect dates appear darker than the normal ones. Based on support vector machine, the recognition of the defect Chinese date machine vision was proposed. After the acquisition of the Chinese dates images, the color model was changed from RGB to HIS. Then, the average value H and standard square deviation value σH of dates hue values were calculate. Depending on the two values, there was few overlaps between defect dates and normal ones in the plot of H and σH. Therefore, H and σH were treated as the feature parameters. Artificial neural network (ANN) and support vector machine (SVM) model were used to analysis the dates features respectively. The experimental results show that SVM has a better performance than ANN on distinguish defect Chinese dates from normal ones, and the correct recognition rate of SVM is 96.2%.