Using CNN to identify the condition of edible fish

Abstract

Evaluating the edibility of fish by it’s freshness is an essential process for the fisheries industry as it contributes to customers’ health and the taste of food. In general, identifying freshness of fish is a difficult task for the customers due to lack of experience or knowledge. Using a real-time application which employs real-time images of fish is the best solution to identify their freshness. In this study, a model was developed using VGG16 architecture in a deep convolutional neural network (CNN) to extract the features of the fish and to classify them based on their freshness. Here, Bluefin Trevally fish was selected as a sample and its freshness was detected using real-time images. Those images were collected in various backgrounds with different lightning by different devices. In real-time images, features of fish such as the colour of the eye and frozen blood colour of the operculum were used to identify the freshness of fish. An accuracy of 99% on identification of freshness was achieved by this model.