Point 1: Deep learning algorithms are revolutionizing how
hypothesis generation, pattern recognition, and prediction occurs in the
sciences. In the life sciences, particularly biology and its subfields,
the use of deep learning is slowly but steadily increasing. However,
prototyping or development of tools for practical applications remains
in the domain of experienced coders. Furthermore, many tools can be
quite costly and difficult to put together without expertise in
Artificial intelligence (AI) computing.
Point 2: We built a biological species classifier that
leverages existing open-source tools and libraries. We designed the
corresponding tutorial for users with basic skills in python and a
small, but well-curated image dataset. We included annotated code in
form of a Jupyter Notebook that can be adapted to any image dataset,
ranging from satellite images, animals to bacteria. The prototype
developer is publicly available and can be adapted for citizen science
as well as other applications not envisioned in this paper.
Point 3: We illustrate our approach with a case study of 219
images of 3 three seastar species. We show that with minimal parameter
tuning of the AI pipeline we can create a classifier with superior
accuracy. We include additional approaches to understand the
misclassified images and to curate the dataset to increase accuracy.
Point 4: The power of AI approaches is becoming increasingly
accessible. We can now readily build and prototype species classifiers
that can have a great impact on research that requires species
identification and other types of image analysis. Such tools have
implications for citizen science, biodiversity monitoring, and a wide
range of ecological applications.
Marine soundscape as an additional biodiversity monitoring tool: A case study from the Adriatic Sea (Mediterranean Sea)
