  Planktonic organisms are key components of aquatic ecosystems and respond
quickly to changes in the environment, therefore their monitoring is vital to
understand the changes in the environment. Yet, monitoring plankton at
appropriate scales still remains a challenge, limiting our understanding of
functioning of aquatic systems and their response to changes. Modern plankton
imaging instruments can be utilized to sample at high frequencies, enabling
novel possibilities to study plankton populations. However, manual analysis of
the data is costly, time consuming and expert based, making such approach
unsuitable for large-scale application and urging for automatic solutions. The
key problem related to the utilization of plankton datasets through image
analysis is plankton recognition. Despite the large amount of research done,
automatic methods have not been widely adopted for operational use. In this
paper, a comprehensive survey on existing solutions for automatic plankton
recognition is presented. First, we identify the most notable challenges that
that make the development of plankton recognition systems difficult. Then, we
provide a detailed description of solutions for these challenges proposed in
plankton recognition literature. Finally, we propose a workflow to identify the
specific challenges in new datasets and the recommended approaches to address
them. For many of the challenges, applicable solutions exist. However,
important challenges remain unsolved: 1) the domain shift between the datasets
hindering the development of a general plankton recognition system that would
work across different imaging instruments, 2) the difficulty to identify and
process the images of previously unseen classes, and 3) the uncertainty in
expert annotations that affects the training of the machine learning models for
recognition. These challenges should be addressed in the future research.
