# Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning
## 3 Results and Discussions
### Experiments on the Flavia Leaf Dataset
#### 3.3.2 Performance Evaluation of the MEQ Method on Flavia Leaf Dataset  
In this classification task, six typical network models were again used to evaluate the effect of the MEQ method. The background of the sample image in this dataset contains fewer redundant features, thus enabling it to better demonstrate the impact of sample image deformation on the classification performance of the model. Table 5 enumerates the experimental results of these models on the Flavia leaf dataset. And their verification accuracy and verification loss comparison are shown in Figure 12. From the experimental results, it can be found that the MAC value of GoogLeNet reaches 99.30%, which is 0.17% higher than the classification result on the original dataset. At the same time, the classification results of ResNet50 and ResNet101 models are both improved by more than 0.30%. In addition, the classification performance of the VGG16 model is also significantly improved, and its MAC value reached 99.48%. As can be seen from the results in Figure 12, although the effective accuracy of the AlexNet and ResNet18 models on the two datasets before and after MEQ processing remains unchanged, the network convergence velocity becomes faster, and the training process is more stable on the standardized dataset. The abovementioned experimental results show that the MEQ method can also have a positive impact on the classification and recognition of the network model when the dataset has a unified background. Figure 11: Partial original sample images of the Flavia leaf dataset (**a**), input images obtained by direct compression (**b**), and input images obtained by MEQ standardization (**c**). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **AC** & **MAC** \\ \hline AlexNet & 98.78\% & 98.78\% \\ GoogLeNet & 99.13\% & 99.30\% \\ VGG16 & 98.95\% & 99.48\% \\ ResNet18 & 98.60\% & 98.60\% \\ ResNet50 & 99.13\% & 99.48\% \\ ResNet101 & 98.95\% & 99.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of classification results of CNN models on original and standardized Flavia leaf datasets. Figure 12: _Cont._  
The comparison models and methods employed in the experiment are identical to those utilized in Section 3.2.3. Detailed comparison results of these methodologies are presented in Table 6. It can be distinctly seen from the table that the classification accuracy of the MEQ + EC-ResNet50 method on this dataset reaches 99.83%, which is better than all the compared methods. The proposed MEQ + EC-ResNet50 method exhibits a superior classification accuracy of 0.67% compared to the newly proposed MTD + LBP-HF method based on manual features. Furthermore, the MEQ + EC-ResNet50 algorithm also exhibits significant advantages when compared to other leaf-recognition methods integrated with deep-learning techniques. Specifically, the recognition accuracy of the MEQ + EC-ResNet50 method surpasses that of the Dual-Path CNN method and Deep-Plant method by 0.55% and 1.61%, respectively. The recognition accuracy of our method on this dataset is approximately 0.2% higher compared to that of the recently proposed IMTD + relu5 algorithm, thereby further substantiating the advantages derived from the integration of sample standardization and transfer-learning algorithms. All of these experimental results show that the MEQ + EC-ResNet50 method still has excellent performance on monochromatic-background leaf datasets with less redundant information. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Classification Accuracy \%** \\ \hline MFD [32] & 89.51\% \\ HGO-CNN [30] & 97.53\% \\ Deep-Plant [31] & 98.22\% \\ VGG16 + relu5\_2 [34] & 98.25\% \\ MTD + LBP-HF [2] & 99.16\% \\ Dual-path CNN [29] & 99.28\% \\ Mask-COV [33] & 99.30\% \\ IMTD + relu5\_2 [11] & 99.63\% \\ MEQ + EC-ResNet50 & 99.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of classification results of different methods on the Flavia leaf dataset.