## 1 Introduction  
Plants can be seen everywhere in our daily life, such as soybeans, maple leaves, grasses and vegetables. We can get abundant resources from plants, for instance, vitamins, energy, medicines, protein, fiber and oxygen. It is estimated that there are about 400,000 plant species [1] in existence in the world, and there is a certain percentage of plants that we do not recognize adequately. According to recent research [2], human beings cause more than two kinds of plants to disappear from the earth every year on average. Hence, it is urgent to study and protect plants, which is beneficial to plant rediscovery, rare plant conservation and environment protection. The most critical and important step in protecting plants is identifying them. Additionally, once we have an accurate plant recognition software in our mobile phones [3; 4], we can recognize and learn more about plants anytime and anywhere; this could further raise public awareness of plant protection. Consequently, plant recognition [5; 6] is a significant research topic. It is noteworthy that the most popular trait used for plant recognition is leaf; however, there are also many works that use other traits to accomplish plant identification and perception. [PERSON] established a new dataset for Greek vascular plant recognition [7], in which the image trait includes leaf, flower, fruit and stem, the plant images are collected from tree, herb and fern. [PERSON] constructed a minirikirotron image dataset for plant root architecture understanding [8]. Because leaf is the most basic and significant appearance feature for plants, we follow common plant recognition works [5] to utilize leaf images as our research object. According to the summary and analysis, the difficulty of plant recognition comes from three aspects. Firstly, biological categorization is a hierarchical structure from coarse to fine: kingdom, phylum, class, order, family, genus and species; so the leaf species from the same genus share similar appearance; their visual differences are small. What is more, plant leaf images are always degraded by viewpoint, illumination, occlusion, resolution and other factors in the collection and imaging process, as a result, the similarity of homogeneous plant images decreases and the similarity of heterogeneous plant images increases. Thirdly, the semantic gap between leaf image visual content and the corresponding category labels is huge for computers. The above difficulties render plant recognition an open and challenging research direction. How to learn effective plant leaf representation is a long-standing pattern recognition problem. Extracting discriminative features from leaf images has been recognized as an indispensable method to reduce the semantic gap. Over the past two decades, considerable efforts have been dedicated to learning effective leaf representation. In the literature, the existing plant recognition methods can be grouped into two categories: handcrafted feature methods and learning feature methods. In the early stage, researchers mainly developed plant features from the raw pixels, shape and texture of leaf images manually. Representative handcrafted methods are height description [3], shape context [9; 10], local binary pattern and texture [11], triangle-based representation [12; 13] and Fourier description [14]. Such features have the advantage of simple calculation and good performance for ordinary and unconstrained plant datasets; however, the accuracy will decrease dramatically for the plant leaf dataset collected in uncontrolled and wild environment. Learning features in a data-driven fashion is gradually becoming the mainstream method of improving feature generalization abilities. [PERSON] et al. proposed bag of fragment to discern middle-level shape features in the bag of visual words framework [15]. [PERSON] et al. presented a robust plant leaf identification method via locality constrained dictionary learning and sparse representation [16].