Computer vision technique for freshness estimation from segmented eye of fish image

Preserving the quality of fish is a challenging task. Several different cooling methods and materials are used during their storage, transportation purpose. These are responsible factors that decide the freshness of a post harvested fish. In this proposed algorithm, a computer vision-based technique is developed to predict the freshness level of fish from its image. Eyes of the fish are considered as the region of interest, as a good correlation has been observed between the colour of the eye and different duration of storage day. It is segmented from the image of a fish sample and then a strategic framework is used for extraction of the discriminatory features. These extracted features show a degradation pattern which acts as an indicative parameter to determine the level of freshness of sample of fish. The proposed method provides a recognition accuracy of 96.67%. The experimental results indicate that this is an efficient and non-destructive methodology for detecting the fish freshness. The high accuracy of freshness detection and low computation time makes this non-destructive methodology efficient for real-world usage in the fish industry and market.

Automated summary

In this proposed algorithm, a computer vision-based technique is developed to predict the freshness level of fish from its image. By extracting features from the region of interest (fish eye) and observing the degradation pattern of these features, the freshness level of the sample fish can be accurately determined. The proposed method achieves a high recognition accuracy and low computation time, making it efficient for real-world usage in the fish industry and market.