Abstract
Although rotifers (Brachionus plicatilis sp. complex) are a very important first feed source in marine fish aquaculture, the managementof rotifers is quite time consuming because their population and movements need to be monitored on a daily basis. This management is still performed manually, and automation is required. If we could make good use of recent breakthroughs in deep learning, the automation of a rotifer culture system could be realized. We propose a deep learning framework for detecting and tracking rotifers as a basis for such automation and carefully verified its accuracy. Experimental results show that a mean average precision of 88.5% was achieved for detection and a higher order tracking accuracy of 88.7% was achieved for tracking, indicating the suitability of deep learning methods for predicting the state of rotifers. In addition, this research will contribute to the development of the field by releasing the trained model and code for visualizing the tracking results as well as an annotated dataset with over 30K instances.