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In this paper, we experimentally verified that deep learning based on Convolutional Neural Network (CNN) is an effective method to judge the freshness of seafood fillets from its images. Currently, freshness of seafood is generally estimated by human’s eyes (connoisseurs) in Japanese fishing industry. However, it requires many years of experience and is a difficult task for new workers due to lack of experience. Furthermore, the Japanese fishing industry is faced with a worker shortage that makes it difficult to pass on their skills to next generation due to an aging and a lack of new workers. Meanwhile, CNN has been achieved a certain success in image recognition areas. If this deep learning can be applied to predict the freshness of fishes, i.e., if the freshness of a subject can be inferred from an image of seafood, it would be a solution for the problems of aging and labor shortage in Japan’s fisheries industry. Therefore, in this paper, we verify that CNN-based deep learning models are effective in estimating/predicting seafood freshness using tuna and squid, which are commonly caught fish in Iwate Prefecture (in Japan) and show that representative CNN models such as ResNet-50 achieved nearly 100% of predicting accuracy from experiments with over 12,000 images. |
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