Lightweight Fish Classification Model for Sustainable Marine Management:
  Indonesian Case
Febrian Kurniawan Gandeva Bayu Satrya Firuz Kamalov
The enormous demand for seafood products has led to exploitation of marine
resources and near-extinction of some species. 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 state-of-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.