## 4 Experiments
### Results and Analysis  
In this section, we first present the performance results of the proposed multi-scale classifier. The number of branches was set to two. Following the selector addition and deblurring processes, the samples were transferred to the corresponding branch network, according to their resolution. The backbone networks of the two branches were the same. The resolution ranges of the two branches were 0-280 and 281-650. Their normalized resolutions were then set to 224 \(\times\) 224 and 448 \(\times\) 448, respectively. Table 4 shows the number of samples in the different categories of these two branches. Since the number of samples in the different categories of our dataset was imbalanced, we used the undersample approach to solve this problem. Specifically, the number of _Artemisia_ samples in Branch 1, Chenopodiaceae samples in Branch 2 and Cupressaceae samples in Branch 2 were relatively large. In order to eliminate the impact of this data imbalance on the classifier, we randomly  
Figure 7: Our proposed multi-scale classification architecture for easy-to-implement and efficient pollen classification: (**a**) the multi-scale classification architecture, which combined several branch networks (the multi-scale architecture included \(N\) branches and the loss function of each branch was combined for training); (**b**) the branch network structure, in which each branch separately trained the backbone network according to the resolution range. picked 400 _Artemisia_ samples for Branch 1 and 300 Chenopodiaceae and Cupressaceae samples for Branch 2 (see Table 4). We used five metrics to evaluate the performance of the multi-scale classifier with the VGG-19 and Resnet-50 backbones: precision, recall (or true positive rate, TPR), specificity (or true negative rate, TNR), F1-score and accuracy. Out of these metrics, F1-score and accuracy were selected as the primary criteria. In the proposed multi-scale classifier, we adopted a selector to pick out the images with low resolutions and then transfer them to the deblurring process. The whole process was named \"Multi-Scale + Selector + Deblur\". The classification results for the VGG-19 and Resnet-50 backbones are shown in Tables 5 and 6, respectively. The classification results include the precision, recall, specificity and F1-score for each pollen category, the average F1-score and the total accuracy. From the tables, it can be seen that the total accuracy of the multi-scale classifier was 0.977 with the Resnet-50 backbone. It also yielded an average F1-score of 0.966 using our proposed dataset. Meanwhile, the multi-scale classifier with the VGG-19 backbone achieved a total accuracy of 0.968 and an average F1-score of 0.949. Figure 8 shows the confusion matrices of the proposed method, based on the VGG-19 and Resnet-50 backbones. It can be seen that the performance of the Resnet-50 backbone in each category was better overall than the performance of the VGG-19 backbone. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Precision** & **Recall** & **Specificity** & **F1-Score** & **Accuracy** \\ \hline _Artemisia_ & 0.971 & 0.981 & 0.993 & 0.976 & / \\ Chenopodiaceae & 0.988 & 0.988 & 0.998 & 0.988 & / \\ Cupressaceae & 0.986 & 1.000 & 0.995 & 0.992 & / \\ Poaceae & 1.000 & 0.920 & 1.000 & 0.958 & / \\ Moraceae & 0.964 & 0.964 & 0.996 & 0.964 & / \\ Pinaceae & 0.983 & 0.992 & 0.995 & 0.987 & / \\ _Salix_ & 0.935 & 0.879 & 0.996 & 0.906 & / \\ Total & / & / & / & **0.966** & **0.977** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The classification results for the multi-scale classifier with the Resnet-50 backbone. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Branch 1** & **Branch 2** \\ \hline _Artemisia_ & 400 & 119 \\ Chenopodiaceae & 103 & 300 \\ Cupressaceae & 396 & 300 \\ Poaceae & 37 & 87 \\ Moraceae & 193 & 86 \\ Pinaceae & 347 & 243 \\ _Salix_ & 118 & 47 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The experimental statistics of each branch.