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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.

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Prediction relationships between weight and image features were established with a high correlation for whole aquacultured rainbow trout. Three hundred fish from three different farms were used. The fish were temporarily removed from the raceway, anesthetized, and their picture was taken by a digital camera. A reference square of known surface area and color was placed beside the fish. The fish were then returned to the raceway alive. The image was analyzed, and the view area of the fish was calculated using the area of the reference square. The average color of the fish was also determined (L * , a * , and b * values). The following equations were used to fit the view area (X) vs. weight (Y) data: linear, power, and second order polynomial. The R 2 values for the used equations were: linear = 0.98; power = 0.99; polynomial = 0.98. Image analysis can be used reliably to predict the weight of whole aquacultured rainbow trout. In addition, color and other visual attributes can be objectively determined by image analysis to sort by visual quality.

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