## 3 Experimental Results and Discussion
### Comparison with Different Classical Models  
Currently, in the field of image recognition for classification research, five models are often used: AlexNet [37], Vgg [38], ResNet [39], MobileNet [40], and DenseNet-121, and DenseNet-121 is the benchmark model of RiceDRA-Net. Consequently, in order to ensure the objectivity of the experimental model, we compared the model of this study with these classical models to obtain objective results. Among these models, both Vgg and ResNet networks have a variety of models with different network sizes. In this experiment, while ensuring objectivity, it was not appropriate to use too large a network, considering the size of the dataset and the experimental hardware. Therefore, we used two models, Vgg-19 and ResNet-101, as the experimental models for comparison experiments. We compared the model in this study with five different types of classical models for rice leaf disease recognition, which set the epoch to 30. We used the Adagrad optimizer and a 0.01 learning rate. We applied the trained models to two test datasets, the SBG-Dataset and CBG-Dataset, respectively. The experimental results are shown in Tables 6 and 7, and Figure 9.  
As can be seen from Table 6 and Figure 9, the test results on the rice leaf disease dataset with single backgrounds showed that the accuracy of the model in this study improved by 15.34%, 6.35%, 5.06%, and 2.08% compared with AlexNet, Vgg-19, MobileNet, and ResNet-101, respectively. RiceDRA-Net had the highest recognition accuracy of 1.85% compared to its benchmark model DenseNet-121. It can be seen from Figure 9 that this model also had the highest accuracy of 99.71% compared to other models in terms of precision, recall, and the \(F1\) score. Furthermore, it can be observed from Figure 9 that this experimental model had a faster convergence speed compared to other classical models and did not overfit. We can also see from Table 7 and Figure 9 that RiceDRA-Net also produced good experimental results in terms of the rice leaf disease test dataset with complex backgrounds, with a 97.86% recognition accuracy on the test set, which was much higher than the other five models. Furthermore, it performed very well in terms of precision, recall, and the \(F1\) score. Therefore, the RiceDRA-Net studied in this paper had the highest recognition accuracy compared to the other five models with both single and complex backgrounds for rice leaf disease recognition. In addition, we also added the total loss values of the six different models on the two test sets, as shown in Figure 10. We can see very intuitively that, among the six models, the RiceDRA-Net proposed in this paper had the lowest total loss value compared to the other five models in the test set. Moreover, the loss value decreased the fastest, regardless of whether it had a single background or a complex background. We compared the recognition accuracy of the six models for two different test datasets for rice leaf diseases, as shown in Figure 11. It can be observed that the recognition accuracy of RiceDRA-Net was 99.71% for the SBG-Dataset and 97.86% for the CBG-Dataset. In addition, the rice leaf disease recognition accuracy of RiceDRA-Net with a single background decreased by only 1.85% compared with a complex background. The accuracy of AlexNet decreased by 8.49%, MobileNet decreased by 9.16%, Vgg-19 decreased by 9.16%, ResNet-101 decreased by 7.53%, and DenseNet-121 decreased by 5.34%. The exper \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score** \\ \hline AlexNet & 75.88 & 77.87 & 75.95 & 0.768 \\ Vgg-19 & 84.20 & 85.57 & 83.57 & 0.845 \\ MobileNet & 85.49 & 87.90 & 85.67 & 0.867 \\ ResNet-101 & 90.10 & 90.82 & 90.25 & 0.905 \\ DenseNet-121 & 92.52 & 92.75 & 92.82 & 0.927 \\ RiceDRA-Net & 97.86 & 97.95 & 97.87 & 0.979 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of various classical models under CBG-Dataset. Figure 9: Comparison of various classical models in a single context.