# Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning
## 1 Introduction  
Plants are an integral part of the Earth's ecosystem, play an important role in the global balance of nature, and are closely related to human life. It is estimated that there are more than 420,000 species of plants in the world [1]. It is difficult for the average person to accurately identify these species, and this difficulty is not conducive to the conservation of plant diversity. Hence, it is necessary to use computer technology to realize the automatic classification of plant species. Identification of plants is often based on their leaves, which can be described by texture, color, and shape. In recent years, many scholars have carried out in-depth research according to the above characteristics and achieved certain results. [PERSON] et al. [2] adopted the proposed multiscale triangle descriptor (MTD) to describe the shape information and used the local binary pattern histogram Fourier (LBP-HF) as the texture feature to identify plant leaves. The classification accuracy of the proposed method on the Flavia leaf, Swedish leaf, and MEW2012 leaf datasets reached 99.16%, 98.48%, and 95.64%,respectively. [PERSON] et al. [3] used Margin Feature (MF) to represent the outline and edge of leaves and used Global Shape Feature (GSF) to represent the global shape of leaves. Based on the above features, a series of multi-grained fusion (MGF) methods were proposed to capture the global shape and margin details of the leaves effectively. After experimental comparison, the classification accuracy of the proposed method on Swedish leaf dataset reached 95.82%. [PERSON] et al. [4] proposed a method to describe the shape of leaves called histogram of Gaussian convolution vectors (HoGCV). By convolving the contour vector functions with Gaussian functions of different widths and calculating the spatial distribution of the resulting Gaussian convolution vectors, a multiscale, invariant, and discriminative shape descriptor, HoGCV, was obtained. The classification accuracy of this method on the Flavia leaf dataset reached 96.56%. [PERSON] et al. [5] proposed a new descriptor for plant-leaf classification called the Pyramid Blurred Shape Model (PBSM). The PBSM algorithm used a pyramid structure to capture leaf features. With the transformation of the pyramid level from coarse to fine, the extracted features also change from global information to high-level local features. Then, a sequential feature fusion method was used to fuse the shape and texture features of each pyramid level to form a new descriptor. In addition, to avoid extracting redundant and unimportant features from leaf images, feature selection based on grey wolf optimization (GWO) was adopted. This method can effectively determine the leaf features that are conducive to recognition and avoid the waste of computing resources. The experimental results show that the classification accuracy of the proposed method on Flavia leaf, Swedish leaf, and MEW2012 leaf datasets reached 96.34%, 96.89%, and 94.14%, respectively. [PERSON] et al. [6] proposed a novel leaf-contour representation method called local triangle feature (LTF) and constructed a composite descriptor based on this approach. This composite descriptor incorporates two fusion schemes: one captures low-level contour features and integrates them with low-level appearance features using the LTF method, followed by the identification of plant species using Hausdorff distance; and the other employs Fisher vector to transform these aforementioned low-level features into high-level representations, which are then merged and differentiated using Euclidean distance for distinguishing between leaves. Experimental results demonstrate that this method achieves an accuracy of 98.75% on the CVIP100 dataset, surpassing existing manual feature-based methods for plant-leaf recognition. Although these methods based on geometric features have achieved good research results and classification effects, their limitations have become apparent with the increase in species.