# Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning
## 1 Introduction
Therefore, more and more scholars adopt the strategy of transfer learning to avoid the long-term training of network models. Transfer learning is a method of reapplying pre-trained convolutional neural networks to new problems. Currently, mature CNNs (e.g., AlexNet, ResNet, VGG16, etc.) are pre-trained on large-scale annotated natural image datasets (e.g., ImgeNet, which includes 15 million images) and then used to solve cross-domain image classification problems. [PERSON] et al. [12] proposed a leaf-recognition method that combines Deep Convolutional Generative Adversarial Networks (DCGANs) and transfer learning. First, DCGAN was used to expand the sample database, and then the pre-trained Inception V3 network was applied to image data processing. The experimental results show that the classification rate of the proposed method on Swedish leaf dataset reached 96.57%. [PERSON] et al. [13] proposed a method for leaf recognition based on transfer learning. Based on the number of categories in the Flavia leaf and Swedish leaf datasets, the fully connected layer of the Inception-v3 model was retuned and trained. In addition, data enhancement was used to suppress overfitting and underfitting. The experimental results show that the classification accuracy of the proposed method on the above two datasets reached 98.95% and 99.11%, respectively. [PERSON] et al. [14] proposed an improved InceptionResNetV2 model for the classification of rice-leaf diseases. In this classification task, the authors compared the proposed method to a simple model without transfer learning. The experimental results show that the transfer-learning method has a better classification performance. [PERSON] et al. [15] proposed a CNN model based on transfer learning to identify tomato-leaf diseases. Moreover, adaptive moment estimation (Adam), stochastic gradient descent (SGD), and RMSprop optimizers were used to evaluate the performance of the proposed model. The experimental results show that the Modified-Xception model based on Adam optimizer had the best performance, and the classification rate of this method reached 99.55%. [PERSON] et al. [16] proposed an improved CNN model based on mixed activation functions for plant-leaf recognition. The five activation functions with good performance were combined in pairs to replace the _ReLU_ activation functions at different positions in the original model. The experimental results show that the ELU-Swish1 model has the best performance among the 20 combinations proposed. Furthermore, the classification rate of the method on the Fluxa leaf and Swedish leaf reached 99.30% and 99.39%, respectively. The authors, [PERSON] et al. [17], employed multiple pre-trained CNN models to achieve efficient recognition of plant leaves. Initially, three pre-trained networks were utilized as feature-extraction models to capture advanced leaf features. Subsequently, attention weights were employed for recalibrating and emphasizing valid features. Ultimately, the fusion of spatial and channel features was accomplished through element multiplication, followed by classification using SVM. This approach effectively leverages the complementary information from different CNNS and achieves an impressive 99.97% accuracy in classifying the Swedish leaf dataset. However, employing multiple models with a large number of parameters as feature extractors leads to excessive utilization of computing resources, thus hindering practical applicability. The abovementioned results fully prove the superiority of transfer learning in the field of leaf recognition. In comparison to manual feature-recognition methods and deep-learning approaches that necessitate model retraining, transfer learning is more apt for practical applications. This is due to its ability not only to conserve substantial computing resources but also to significantly enhance tree species-identification accuracy. Although the current methods of species identification have reached an unprecedented level, there are still certain limitations.