  With the rapid emergence of deep learning (DL) technology, it has been
successfully used in various fields including aquaculture. This change can
create new opportunities and a series of challenges for information and data
processing in smart fish farming. This paper focuses on the applications of DL
in aquaculture, including live fish identification, species classification,
behavioral analysis, feeding decision-making, size or biomass estimation, water
quality prediction. In addition, the technical details of DL methods applied to
smart fish farming are also analyzed, including data, algorithms, computing
power, and performance. The results of this review show that the most
significant contribution of DL is the ability to automatically extract
features. However, challenges still exist; DL is still in an era of weak
artificial intelligence. A large number of labeled data are needed for
training, which has become a bottleneck restricting further DL applications in
aquaculture. Nevertheless, DL still offers breakthroughs in the handling of
complex data in aquaculture. In brief, our purpose is to provide researchers
and practitioners with a better understanding of the current state of the art
of DL in aquaculture, which can provide strong support for the implementation
of smart fish farming.
