  Climate change is increasing the frequency and severity of harmful algal
blooms (HABs), which cause significant fish deaths in aquaculture farms. This
contributes to ocean pollution and greenhouse gas (GHG) emissions since dead
fish are either dumped into the ocean or taken to landfills, which in turn
negatively impacts the climate. Currently, the standard method to enumerate
harmful algae and other phytoplankton is to manually observe and count them
under a microscope. This is a time-consuming, tedious and error-prone process,
resulting in compromised management decisions by farmers. Hence, automating
this process for quick and accurate HAB monitoring is extremely helpful.
However, this requires large and diverse datasets of phytoplankton images, and
such datasets are hard to produce quickly. In this work, we explore the
feasibility of generating novel high-resolution photorealistic synthetic
phytoplankton images, containing multiple species in the same image, given a
small dataset of real images. To this end, we employ Generative Adversarial
Networks (GANs) to generate synthetic images. We evaluate three different GAN
architectures: ProjectedGAN, FastGAN, and StyleGANv2 using standard image
quality metrics. We empirically show the generation of high-fidelity synthetic
phytoplankton images using a training dataset of only 961 real images. Thus,
this work demonstrates the ability of GANs to create large synthetic datasets
of phytoplankton from small training datasets, accomplishing a key step towards
sustainable systematic monitoring of harmful algal blooms.
