Fine-Grained Hierarchical Classification of Plant Leaf Images Using Fusion of Deep Models

Abstract

A fine-grained plant leaf classification method based on the fusion of deep models is described. Complementary global and patch-based leaf features are combined at each hierarchical level (genus and species) by pre-trained CNNs. The deep models are adapted for plant recognition by using data augmentation techniques to face the problem of plant classes with very few samples for training in the available imbalanced dataset. Experimental results have shown that the proposed coarse-to-fine classification strategy is a very promising alternative to deal with the low inter-class and high intra-class variability inherent to the problem of plant identification. The proposed method was able to surpass other state-of-the-art approaches on the ImageCLEF 2015 plant recognition dataset in terms of average classification scores.