Two-view fine-grained classification of plant species

Abstract

Automatic plant classification is challenging due to the vast biodiversity of the existing plant species in a fine-grained scenario. Robust deep learning architectures have been used to improve the classification performance in such a fine-grained problem but usually build models that are highly dependent on a large training dataset and are not scalable. This paper proposes a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained plant species recognition. 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 many 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., PlantCLEF 2015 and LeafSnap) have shown the proposed method’s effectiveness, which achieved recognition accuracy of 0.87 and 0.96, respectively.