## 4 Experiment and Analysis
### Attentional Visualization Experiment  
In order to better understand how convolutional neural networks identify diseases in different parts of the images during training, this paper utilizes the Grad-CAM attention visualization tool to generate attention heat maps of the network's predictions [34; 35]. The heat map highlights important regions by color-coding them, where redder and warmer colors correspond to regions of greater importance that the network is most interested in, while cooler colors (bluish) denote regions of less interest. Attention visualizations were obtained for some diseased leaves, and the corresponding heat maps are shown in Figure 13. From the heat maps, we can observe that within the PlantVillage model cluster, the red areas of the three models, except IBSA_Net, are small and scattered. In contrast, the warm areas of the IBSA_Net network are mainly concentrated around the disease spots, but the attention is not focused on the disease spot area. This indicates that the disease information learned from a single background dataset may not generalize well to more complex background datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**IBSA\_Net** & **Precision** & **Recall** & **F1-Score** & **AlexNet** & **Precision** & **Recall** & **F1-Score** \\ \hline Bacterial spot & 0.914 & 0.885 & 0.899 & & 0.917 & 0.355 & 0.512 \\ Early blight & 0.959 & 0.959 & 0.959 & & 0.849 & 0.625 & 0.720 \\ Late blight & 0.922 & 0.941 & 0.931 & & 0.769 & 0.83 & 0.798 \\ Leaf Mold & 0.934 & 0.855 & 0.893 & & 0.58 & 0.622 & 0.600 \\ Septoria leaf spot & 0.932 & 0.925 & 0.928 & & 0.504 & 0.917 & 0.650 \\ Spider mites & 0.974 & 0.966 & 0.970 & & 0.817 & 0.767 & 0.791 \\ Target Spot & 0.982 & 0.991 & 0.986 & & 0.686 & 0.449 & 0.543 \\ Yellow Leaf Curl Virus & 0.93 & 0.985 & 0.957 & & 0.707 & 0.803 & 0.752 \\ Mosaic virus & 0.932 & 0.953 & 0.942 & & 0.75 & 0.643 & 0.692 \\ healthy & 0.943 & 0.98 & 0.961 & & 0.916 & 0.87 & 0.892 \\ avg & 0.942 & 0.944 & 0.943 & & 0.749 & 0.688 & 0.695 \\ \hline
**VGG** & **precision** & **recall** & **F1-score** & **GoogLeNet** & **precision** & **recall** & **F1-score** \\ \hline Bacterial spot & 0.494 & 0.448 & 0.470 & & 0.85 & 0.354 & 0.500 \\ Early blight & 0.43 & 0.671 & 0.524 & & 0.806 & 0.74 & 0.772 \\ Late blight & 0.854 & 0.406 & 0.550 & & 0.697 & 0.842 & 0.763 \\ Leaf Mold & 0.461 & 0.422 & 0.441 & & 0.833 & 0.602 & 0.699 \\ Septoria leaf spot & 0.516 & 0.85 & 0.642 & & 0.683 & 0.842 & 0.754 \\ Spider mites & 1.0 & 0.718 & 0.836 & & 0.898 & 0.974 & 0.934 \\ Target Spot & 0.672 & 0.815 & 0.737 & & 0.815 & 0.935 & 0.871 \\ Yellow Leaf Curl Virus & 0.785 & 0.761 & 0.773 & & 0.823 & 0.97 & 0.890 \\ Mosaic virus & 0.704 & 0.442 & 0.543 & & 0.733 & 0.767 & 0.750 \\ healthy & 1.0 & 0.703 & 0.826 & & 0.947 & 0.881 & 0.913 \\ avg & 0.691 & 0.623 & 0.634 & & 0.808 & 0.791 & 0.785 \\ \hline
**MobileNetV3** & **precision** & **recall** & **F1-score** & **EfficientNet** & **precision** & **recall** & **F1-score** \\ \hline Bacterial spot & 0.696 & 0.667 & 0.681 & & 0.827 & 0.948 & 0.883 \\ Early blight & 0.762 & 0.877 & 0.815 & & 0.812 & 0.945 & 0.873 \\ Late blight & 0.832 & 0.881 & 0.856 & & 0.960 & 0.950 & 0.955 \\ Leaf Mold & 0.902 & 0.554 & 0.686 & & 0.875 & 0.928 & 0.901 \\ Septoria leaf spot & 0.777 & 0.759 & 0.768 & & 0.957 & 0.835 & 0.892 \\ Spider mites & 0.826 & 0.932 & 0.876 & & 0.926 & 0.957 & 0.941 \\ Target Spot & 0.936 & 0.676 & 0.785 & & 0.837 & 1 & 0.911 \\ Yellow Leaf Curl Virus & 0.792 & 0.91 & 0.847 & & 0.94 & 0.94 & 0.940 \\ Mosaic virus & 0.711 & 0.628 & 0.667 & & 1 & 0.791 & 0.883 \\ healthy & 0.744 & 0.98 & 0.846 & & 0.917 & 0.653 & 0.763 \\ avg & 0.797 & 0.786 & 0.782 & & 0.905 & 0.8947 & 0.894 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of evaluation metrics of PDDA model clusters on PlantDoc++.