# Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning
## 1 Introduction
These low-level visual features are not sufficient to effectively represent high-level semantics, thus limiting the performance of plant-leaf recognition. Deep learning is a new research direction in the field of machine learning. Deep convolutional neural networks can learn autonomously and represent the high-level semantic information of plant leaves well. With the great breakthrough of the AlexNet model proposed by [PERSON] in the field of image recognition, the academic circle has set off a wave of deep-learning research based on convolutional neural network (CNN). [PERSON] et al. [7] proposed a two-way attention model combined with deep CNN for plant-leaf recognition. It has two types of attention: family-first attention and max-sum attention. The experiment results show that the proposed method achieved good classification results on several challenging datasets. [PERSON] et al. [8] proposed the Attentional Pyramid Network (APN) for the recognition of Chinese herbal medicine, as it combines competitive attention and spatial collaborative attention. On the self-established Chinese herbal-medicine dataset, the accuracy of this method reached 94.90%. [PERSON], [PERSON], et al. [9] proposed a novel method to solve the leaf recognition problem. Firstly, the leaves were preprocessed to extract texture features, Fourier descriptors, and vein images. These properties were then transformed into a better representation by a neural network-based encoder. To this end, the authors designed a simple and effective 2D convolutional encoder. The proposed model extracted features from the processed color image by stacking convolutional layers and then used a fully connected layer for encoding. And, finally, the support vector machine (SVM) model was used to classify different leaves. The recognition accuracy of this method on the Flavia leaf dataset reached 99.58%. [PERSON], [PERSON], et al. [10] designed several composite models to improve the accuracy of tree species identification. In contrast to previous deep-learning recognition algorithms, the authors integrated three models with distinct classifiers as the decision model and further employed the k-fold cross-validation (k-fold cv) method to mitigate overfitting issues. The composite model design facilitates efficient feature extraction across different plant organs based on their respective preferences for feature extraction. Moreover, a leaf sample dataset that aligns closely with tree-growth characteristics was constructed in order to evaluate the proposed model's performance. This dataset comprises images showcasing multiple branches and leaves, effectively demonstrating diverse leaf-arrangement patterns on branches. Experimental results demonstrate that the MixNet XL CNN model combined with the K-nearest neighbor (KNN) classifier achieves an exceptional classification rate of 99.86%, surpassing other classical CNN algorithms. [PERSON] et al. [11] combined leaf-shape features with convolutional features extracted from deep-learning models for the classification of plant species. Firstly, aiming at tackling the issue of the lack of internal salient points in common leaf-shape descriptors, the multi-scale triangle descriptor was improved. Then, the classical VGG16 model was used to accumulate convolutional features of different depths. Finally, the shape features and convolution features were fused for plant-leaf recognition. Experimental results demonstrate that this proposed method achieves an impressive 99.47% classification accuracy on the Swedish leaf dataset and also performs well on several other commonly used leaf datasets. These findings highlight the complementary nature of high-level convolutional information and low-level visual characteristics in leaf recognition. The above results prove the effectiveness of the deep-learning method in the field of leaf recognition. However, the disadvantage of this approach is that it requires a large number of samples to train the network models.