  Tree species identification using bark images is a challenging problem that
could prove useful for many forestry related tasks. However, while the recent
progress in deep learning showed impressive results on standard vision
problems, a lack of datasets prevented its use on tree bark species
classification. In this work, we present, and make publicly available, a novel
dataset called BarkNet 1.0 containing more than 23,000 high-resolution bark
images from 23 different tree species over a wide range of tree diameters. With
it, we demonstrate the feasibility of species recognition through bark images,
using deep learning. More specifically, we obtain an accuracy of 93.88% on
single crop, and an accuracy of 97.81% using a majority voting approach on all
of the images of a tree. We also empirically demonstrate that, for a fixed
number of images, it is better to maximize the number of tree individuals in
the training database, thus directing future data collection efforts.
