feasibility of sorting the tigertooth croaker (otolithes ruber) and silver pomfret (pampus argenteus) fishes using computer vision technology

grading science and grading equipment for many kinds of sea products are growing rapidly in developed communities and variety of grading equipment can be found in most of the large fishery units. computer vision has the potential to be used as a precise method for recognition and assessment of apparent characteristics. in this study, machine vision technology was used to sort fish based on species, size and weight. tiger-toothed croaker and silver pomfret fishes were selected for this study. in the first stage, each sample fish was weighted and put in the illumination chamber and images were captured. matlab environment was used for segmentation and image processing tasks. linear and non linear regressions were used to estimate fish weight. seven variables extracted from each image (length, height, area, perimeter, equal diameter, major axis length and minor axis length) in four models of mathematical approach (linear, logarithmic, binomial and exponential) were considered for developing each weight prediction equation. results indicated that fish weight can be estimated with r2 values of 95.4% and 94% for tiger-toothed croaker and silver pomfret, respectively. model validation was investigated with new data. results showed that there is not a significant difference between the actual and estimated weight at 5% significance level for all fish species in this study. it was also concluded that the system can accurately measure the length of the fishes using machine vision technology envisaged in this study. the algorithm was also able to sort two species of fishes including tiger-toothed croaker and silver pomfret with an accuracy of 100%.