  We present the first unsupervised deep learning method for pollen analysis
using bright-field microscopy. Using a modest dataset of 650 images of pollen
grains collected from honey, we achieve family level identification of pollen.
We embed images of pollen grains into a low-dimensional latent space and
compare Euclidean and Riemannian metrics on these spaces for clustering. We
propose this system for automated analysis of pollen and other microscopic
biological structures which have only small or unlabelled datasets available.
