Abstract: Much work has long been devoted to plant leaf classification,but these methods cannot be applied well in real applications,though they may achieve good results in public datasets.Moreover,they are hardly employed to more complex problems,e.g.leaf ranking,which requires the classification of leaves first and then ranking leaves of the same class.This paper proposes a new model for plant leaf classification as well as leaf ranking,which focuses on the granularity information of leaves and integrates multi-level granularity from coarse to fine.Specifically,the model contains two branches,coarse-grained and fine-grained,which are linked by a coarse-fine hybrid loss,prompting the model to progressively learn a coarse-to-fine representation.A multi-step training approach is used,with different levels of features extracted at each step,therefore enabling the fusion of shallow features with deep features.In addition,a geometric channel attention module,which consists of a spatial transformation and a bili-near attention pooling module,is proposed to allow our model to focus on more discriminative local regions in the image and extract more discriminative features.Our method achieves 99.8% and 99.7% classification accuracy on two publicly available leaf classification datasets,Flavia leaf and Swedish leaf,respectively,and 71.9% classification accuracy on our constructed tobacco leaf ranking dataset,both outperform the state-of-the-art methods.

Key words: Leaf classification, Leaf ranking, Multi-granularity fusion, Spatial transformation network, Bilinear attention pooling