The tuna processing industry currently uses individual fish weight to estimate fish size. The correlation between fish weight and other physical properties are important because they could also be used in both size grading methods and in heat transfer modeling. Table 2 shows the regression equations and correlation coefficient (R2) obtained between fish weight and other physical properties determined in this study. All but one of the relationships studied exhibit large correlation coefficients (R2 =0.74–0.97), including volume, projected area (side and top view), length, width (measured at the thickest part), and perimeter (measured at the thickest part). These correlations are illustrated in Figs. 4a–4h. Only the relationship between fish weight and thickness at the thickest part of the fish was relatively poor (R2 =0.49). Strong correlation between fish weight and fish projected area (top view and side view) were found (R2 =0.92 and 0.94, respectively), which indicatesthatanimageprocessinggradercouldbeanattractivealternativegradingmethod. Insuchagradingmethod,thesideviewprojectedareaimagewouldbepreferabletothetop view, because the fish could be measured while traveling on their sides on a conveyor belt. Figure 4h shows the linear relationship between fish volume and surface area (R2 =0.84). This information could be used in the fish wrapping process as well. When the volume of a fish is known, the surface area can be calculated from the relationship equation, and the required wrapping area can be estimated.