Background/Question/Methods
Deep learning algorithms are revolutionalizing how hypothesis generation, pattern recognition, and prediction occurs in the sciences. In the life sciences, particularly biology and its subfields such as ecology, the use of deep learning is slowly but steadily increasing. However, prototyping or development of tools for practical applications in light of the big data explosion in ecology remains in the domain of experienced coders. Furthermore, many of the plug and play tools can be quite costly and difficult to put together without expertise in Artificial intelligence (AI) computing. While there are a few dozen papers using these powerful techniques - few describe beginner-friendly ways to prototype species classifiers.
In our talk, we will present a pipeline to prototype an expert taxonomist-level species classifier leveraging existing open-source tools and libraries. The user needs only basic skills in python and a small, but well-curated image dataset. We will also include annotated code in form of a Jupyter Notebook that can be easily adapted to any image dataset ranging from satellite images, animals, bacteria or even data such as bird song or bat echolocation recordings transformed into images. The prototype developer is publicly available and can be widely adapted for citizen science as well as other applications not envisioned in this paper.
We illustrate our approach with a case study of 219 images of 3 three seastar species - and show that with no parameter tuning of the AI pipeline we can create a classifier with 87% accuracy.
Results/Conclusions
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. In our talk, we will demonstrate one technique that could lead the way into this exciting future.