To obtain the weight information required for weight classification of Golden pomfret and realize rapid and non-contact preprocessing. This research is based on the front and back projection images of multiple Golden pomfret, and an image processing algorithm is used to obtain the projected area, perimeter, long and short axes, etc. and is combined with artificially measured body length, body width, fin weight, head fin width, the width of the narrowest point of fishtail and other related data, through linear regression, curve estimation, principal component analysis, RBF neural network, and other methods build a weight prediction model and validate it. The results show that among the 14 weight prediction models established, the multiple linear regression model based on the image area, the proportion of fin weight, and the proportion of finless area have higher prediction accuracy, and the model's coefficient of determination is 0.919. The coefficient of determination of the linear regression model based on the image area, image perimeter, long axis, and short axis is 0.918. Among the established 9 principal component regression models, the quadratic regression model is the best, with a coefficient of determination of 0.877 and a standard estimation error of 17.094. The model is verified by modeling out-of-sample data. The correlation coefficient between the predicted value and the actual value of the regression model based on the image area, the proportion of fin weight, and the proportion of the finless area is 0.944, the average relative error is 2.43%, and the standard deviation is 2.32%. The research results show that the quality prediction model of Golden pomfret based on machine vision technology has high reliability and can be applied to the quality classification of Golden pomfret.
