  Plant species identification in the wild is a difficult problem in part due
to the high variability of the input data, but also because of complications
induced by the long-tail effects of the datasets distribution. Inspired by the
most recent fine-grained visual classification approaches which are based on
attention to mitigate the effects of data variability, we explore the idea of
using object detection as a form of attention. We introduce a bottom-up
approach based on detecting plant organs and fusing the predictions of a
variable number of organ-based species classifiers. We also curate a new
dataset with a long-tail distribution for evaluating plant organ detection and
organ-based species identification, which is publicly available.
