  Marine ecosystems are vital for the planet's health, but human activities
such as climate change, pollution, and overfishing pose a constant threat to
marine species. Accurate classification and monitoring of these species can aid
in understanding their distribution, population dynamics, and the impact of
human activities on them. However, classifying marine species can be
challenging due to their vast diversity and the complex underwater environment.
With advancements in computer performance and GPU-based computing,
deep-learning algorithms can now efficiently classify marine species, making it
easier to monitor and manage marine ecosystems. In this paper, we propose an
optimization to the MobileNetV2 model to achieve a 99.83% average validation
accuracy by highlighting specific guidelines for creating a dataset and
augmenting marine species images. This transfer learning algorithm can be
deployed successfully on a mobile application for on-site classification at
fisheries.
