Abstract: Huanghua pear is an important fruit in China. The fruit diameter and surface defects are important indices in the classification of Huanghua pear. To detect the surface quality of Huanghua pear, a machine vision system was set up on the basis of investigating the spectral reflectance of the Huanghua pear. The effects of different backgrounds on the acquired images were studied, and the method to seek the optimum background in the light of the grayscale histogram was found to be very effective. Clearer images were acquired when the background was white. As we know, fruits in actual situation are commonly random in orientation. Therefore, the Minimum Enclosing Rectangle (MER) method was designed and used to estimate the maximum diameter, and the correlation coefficient of real maximum diameter versus the maximum diameter measured by MER method reached 0.9962. According to color difference in the neighboring area of the defected and non defects, the grayscale values of the R (Red) frame and G (Green) frame were used to find the suspectable defects, and the whole defect was found by the region growing method. To decrease the relative errors, the pixel transform method was adopted to recover the geometrical feature of sphere fruit surface from projected image while the area of defect was calculated. Moreover, a method to revise the estimated area was advanced. Compared to the method to directly calculate the real area of defect from the projected image, the pixel transform method could decrease the relative error by about 38%. These results lay a solid foundation for further development of a Huanghua pear quality detection system with machine vision.