  Marine ecosystems and their fish habitats are becoming increasingly important
due to their integral role in providing a valuable food source and conservation
outcomes. Due to their remote and difficult to access nature, marine
environments and fish habitats are often monitored using underwater cameras.
These cameras generate a massive volume of digital data, which cannot be
efficiently analysed by current manual processing methods, which involve a
human observer. DL is a cutting-edge AI technology that has demonstrated
unprecedented performance in analysing visual data. Despite its application to
a myriad of domains, its use in underwater fish habitat monitoring remains
under explored. In this paper, we provide a tutorial that covers the key
concepts of DL, which help the reader grasp a high-level understanding of how
DL works. The tutorial also explains a step-by-step procedure on how DL
algorithms should be developed for challenging applications such as underwater
fish monitoring. In addition, we provide a comprehensive survey of key deep
learning techniques for fish habitat monitoring including classification,
counting, localization, and segmentation. Furthermore, we survey publicly
available underwater fish datasets, and compare various DL techniques in the
underwater fish monitoring domains. We also discuss some challenges and
opportunities in the emerging field of deep learning for fish habitat
processing. This paper is written to serve as a tutorial for marine scientists
who would like to grasp a high-level understanding of DL, develop it for their
applications by following our step-by-step tutorial, and see how it is evolving
to facilitate their research efforts. At the same time, it is suitable for
computer scientists who would like to survey state-of-the-art DL-based
methodologies for fish habitat monitoring.
