Abstract: Up to now, the shape feature of potato shape detection based on machine vision is single with little relative investigation. Taking Zernike moments and support vector machine as shape detecting feature and classifier respectively, an approach to potato shape detection and classification, which yielded a relatively higher accuracy, was proposed in this paper. The image was first normalized by using best image segmentation method to obtain scale and translation invariance. The rotation invariant Zernike features were then extracted from the normalized images, among which 19 features were selected. At last, shape classification was accomplished by inputting the selected features into support vector machine classifier. A new mixed kernel function of RBF and Sigmoid kernel function was proposed, resulting in 93% and 100% detection accuracy for the perfect and malformation potatoes, respectively.