Automatic identification of tiger puffer in an aquaculture tank using deep learning

This paper presents an experiment to realize an automatic identification system of tiger puffer (torafugu) using Deep Learning. To meet the operation of growing and selling aquaculture fish, we tried to use Transfer Learning to reduce the operation cost to identify torafugu. In this trial, we used three torafugu. We took a video of them swimming in an aquaculture tank then extracted figures of each torafugu. Moreover, to increase the number of data for learning and testing, we finally got 150 pictures of each torafugu by rotating and flipping. As the result of the experiment, 60 to 80 pictures for each torafugu are enough for automatic identification. The generated learning model can identify torafugu under severe conditions such as unclear photos by waves and change of brightness, and movement of back born during swimming.