  Automatic plant classification is a challenging problem due to the wide
biodiversity of the existing plant species in a fine-grained scenario. Powerful
deep learning architectures have been used to improve the classification
performance in such a fine-grained problem, but usually building models that
are highly dependent on a large training dataset and which are not scalable. In
this paper, we propose a novel method based on a two-view leaf image
representation and a hierarchical classification strategy for fine-grained
recognition of plant species. It uses the botanical taxonomy as a basis for a
coarse-to-fine strategy applied to identify the plant genus and species. The
two-view representation provides complementary global and local features of
leaf images. A deep metric based on Siamese convolutional neural networks is
used to reduce the dependence on a large number of training samples and make
the method scalable to new plant species. The experimental results on two
challenging fine-grained datasets of leaf images (i.e. LifeCLEF 2015 and
LeafSnap) have shown the effectiveness of the proposed method, which achieved
recognition accuracy of 0.87 and 0.96 respectively.
