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Shape is one of the most important indexes for fruit quality evaluation, and extracting the shape characters of fruit is the key step for the shape grading. However, it is very difficult to get the accurate description of fruit shape because of a variety of fruit shape in different attitudes. In this research, 4 images were taken for each apple respectively. There are 144 images for 36 apples, in which 12 apples being considered to have the best shape, and 11 ones having big deformation, and the other 13 ones having small deformation. Then process these images and get 245 shape character parameters of each boundary of the fruit through signature description, Hu moments, Zernike moments, wavelet moments, Fourier description, and wavelet description. This paper is trying to get efficient and practical shape indexes and find a feasible way for describing fruit shape.