  In this paper, we present our first proposal of a machine learning system for
the classification of freshwater snails of the genus Radomaniola. We elaborate
on the specific challenges encountered during system design, and how we tackled
them; namely a small, very imbalanced dataset with a high number of classes and
high visual similarity between classes. We then show how we employed triplet
networks and the multiple input modalities of images, measurements, and genetic
information to overcome these challenges and reach a performance comparable to
that of a trained domain expert.
