Prediction of the weight of Alaskan pollock using image analysis

Determining the size and quality attributes of fish by machine vision is gaining acceptance and increasing use in the seafood industry. Objectivity, speed, and record keeping are advantages in using this method. The objective of this work was to develop the mathematical correlations to predict the weight of whole Alaskan Pollock (Theragra chalcogramma) based on its view area from a camera. One hundred and sixty whole Pollock were obtained fresh, within 2 d after catch from a Kodiak, Alaska, processing plant. The fish were first weighed, then placed in a light box equipped with a Nikon D200 digital camera. A reference square of known surface area was placed by the fish. The obtained image was analyzed to calculate the view area of each fish. The following equations were used to fit the view area (X) compared with weight (Y) data: linear, power, and 2nd-order polynomial. The power fit (Y = A · X(B)) gave the highest R(2) for the fit (0.99). The effect of fins and tail on the accuracy of the weight prediction using view area were evaluated. Removing fins and tails did not improve prediction accuracy. Machine vision can accurately predict the weight of whole Pollock. Practical Application: The weight of Alaskan Pollock can be predicted automatically by taking the image of the fish and using it in one of the correlations developed in this study. The removal of the fins or the fins and the tail did not increase the prediction accuracy of the method. Therefore, intact fish images should be used.