Addressing the limitations of fog concentration inspection in image defogging, an algorithm based on the scatterplot prior of the generalized pixel difference-ratio(GPDR) and the Naka-Rushton(NR) fitting function was proposed. First, the GPDR prior for gray scatterplots in standard foggy image sets across various scenes was extracted. Next, the NR function, constrained by the prior, was introduced, and a lookup table of parameters (n,k) corresponding to fog concentration levels was established by calculating the parameters (n,k) of NR function for standard image sets. Regression analysis was then used to calculate the parameters (n',k') for real foggy images, and the comprehensive correlation coefficient between (n,k) and (n',k') was calculated. Parameters (n,k) with correlation coefficients exceeding a set threshold were considered indicative of the fog concentration level. Simulations show that the algorithm accurately reflect changes in fog concentration across images with varying densities. Additionally, correlation coefficients between the algorithm’s results and PM2.5 measurements reached up to 0.95, both within the same and across different scenes. This shows that the algorithm can be effectively used for fog concentration rating in visual field. Horizontal comparison tests show that the inspection accuracy of the proposed algorithm can reach up to 4.8%, making it suitable for field fog concentration detection.