# Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning
## 3 Results and Discussions
### Grad-CAM Visual Analysis  
Gradient-Weighted Class Activation Mapping (Grad-CAM) is a class-discriminative localization technique used to generate 'visual explanations' for decisions from a large class of CNN-based models, making them more transparent and interpretable. The gradient information from the last convolutional layer, which is fed into the CNN, is utilized to assign significance values to individual neurons for a specific decision of interest. The detailed principle of the Grad-CAM technique can be found in the literature [37]. Considering that there are differences in shape and texture information between the standardized samples and the directly compressed original samples, this may lead to changes in the regions of interest of the CNN models. Hence, the Grad-CAM technique is used to visualize the significant regions of interest for the above six models in high-resolution detail. Figure 15 shows the Grad-CAM images of input samples from the Swedish leaf dataset and the standardized Swedish leaf dataset, where the samples in Figure 15 are from class 5, and the samples in Figure 15 are from class 1. Figure 15: The Grad-CAM images of directly compressed and MEQ-standardized samples. (**a**) Sample with petiole and (**b**) sample without petiole. In both (**a**,**b**), the upper section displays the Grad-CAM image of the directly compressed sample, whereas the lower section showcases the Grad-CAM image of the MEQ standardized sample. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Classification Accuracy \%** \\ \hline MFD [32] & 89.31\% \\ Deep-Plant [31] & 92.16\% \\ HGO-CNN [30] & 94.02\% \\ Dual-path CNN [29] & 94.58\% \\ MTD + LBP-HF [2] & 95.64\% \\ VGG16 + relu5\_2 [34] & 98.06\% \\ MaskCOV [33] & 98.32\% \\ IMTD + relu5\_2 [11] & 99.09\% \\ MEQ + EC-ResNet50 & 99.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of classification results of different methods on the MEW2012 leaf dataset. As expected, the six models showed significant changes in the areas of attention on both the original and standardized leaf samples. As can be seen from Figure 15a, in the case of direct compression of the original sample, all models primarily focus on the petiole and its surrounding background. The AlexNet model, in particular, focuses almost exclusively on the background of the image. This indicates that even if the model can correctly classify such samples when training on specific datasets, it may not achieve ideal results in practical applications. The Grad-CAM images of standardized sample, however, did not exhibit a similar phenomenon. Conversely, the regions of interest for all models during the recognition process were predominantly concentrated on the leaf surface. Additionally, to enhance the demonstration of the MEQ method's effectiveness, a Grad-CAM image of a petiole-free sample was utilized for comparative analysis, as depicted in Figure 15b. The significant changes can still be observed in this case, and for the directly compressed original sample, the model's attention is primarily focused on the leaf's edge. However, for standardized samples, their area of interest remains concentrated on the leaf surface. These experimental results demonstrate the efficacy of the MEQ method in eliminating redundant information from images, thereby facilitating more accurate of texture and shape features by convolutional models. Furthermore, it can be found that the AlexNet network appears to exhibit greater susceptibility to sample deformation, and its attention primarily focused on the background and leaf edges of the sample image. These may be the reasons for the unsatisfactory performance of the model on the standardized MEW2012 leaf dataset. Many species of leaf samples in this dataset come from the same genus, resulting in a high degree of similarity in their leaf shape and texture features, which undoubtedly increases the difficulty of classification recognition.