Lightweight Fish Classification Model for Sustainable Marine Management: Indonesian Case

In particular, overfishing is one the main issues in sustainable marine development. In alignment with the protection of marine resources and sustainable fishing, this study proposes to advance fish classification techniques that support identifying protected fish species using stateof-the-art machine learning. We use a custom modification of the MobileNet model to design a lightweight classifier called M-MobileNet that is capable of running on limited hardware. As part of the study, we compiled a labeled dataset of 37,462 images of fish found in the waters of the Indonesian archipelago. The proposed model is trained on the dataset to classify images of the captured fish into their species and give recommendations on whether they are consumable or not. Our modified MobileNet model uses only 50% of the top layer parameters with about 42% GTX 860M utility and achieves up to 97% accuracy in fish classification and determining its consumability. Given the limited computing capacity available on many fishing vessels, the proposed model provides a practical solution to on-site fish classification. In addition, synchronized implementation of the proposed model on multiple vessels can supply valuable information about the movement and location of different species of fish. Fish and seafood are among the most highly marketed foods in the world. According to WWF's report [29, 52], over 740 million people (10%) are reliant on catching, measuring, producing, and selling fish and seafood, and the statistics are continuously growing. People in developing maritime countries are largely dependent on fish as their primary livelihood, distributing the largest volume of fish catch and production worldwide and contributing 97% of the world's fishing workforce [27]. This also applies to the overwhelming majority of small-scale fishermen for whom fishing makes up the basis of their earnings as well as an essential part of their daily nourishment.