The quail egg is a without cholesterol food and riches of different vitamins. This food expunges the radioactive particle and the carcinogen of human’s body. For mechanizing its preparation process, the quality parameters should be modeled with computer techniques. Therefore in this research, the image processing technique was used to determine the physical properties of quail egg, then egg volume was calculated by slicing each egg and rotating the slices around its main axis. Furthermore to determine the egg weight, a regression multi-variable model and a neural network model were developed. The results of suggested methods and models in this research were compared with real values and previous methods (Hoyt model). Finally, the influence of nutrition, ventilation and female quail age on quality parameters of eggs was determined. By comparing the real volume of eggs with the Hoyt model, accuracy was ??% and with the slices-rotation method, accuracy was ???% which well shows the importance of slices-rotation method. By comparing the real weight of eggs with the Hoyt model, accuracy was ??%, with the regression multi-variable model, accuracy was ??%, and with the neural network model, accuracy was ??%. The neural network model not only presented the egg weight with high accuracy but also considered the effect of laying time which is very important for the egg weight estimation. After comparing the results, it was shown that the nutrition, ventilation and female quail age influence egg quality. The eggs of younger females were limited to spherical geometry that may be due to the mother's womb not being expanded. Furthermore, the female quails in topper cages which had better nutrition and ventilation showed more weighting and sizing. These results show that coupled the image processing and artificial neural network techniques can help very well to mechanize the preparation process of quail egg.