## 3 Experimental Results and Discussion
### Comparison with Different Classical Models
imental results show that complex backgrounds have the least impact on RiceDRA-Net, and its accuracy in terms of rice leaf disease recognition with complex backgrounds decreased the least compared with the remaining five comparison models. The experiments demonstrate that RiceDRA-Net has a better recognition effect for rice leaf diseases with complex backgrounds. Figure 11: Comparison of model accuracy under two data sets. Figure 10: Comparison of the total loss values of the five different models on the test set in a single context. We used six different models with two test sets, the SBG-Dataset and CBG-Dataset, to test their accuracy for four different disease categories on the rice leaf disease dataset, as shown in Figure 12. This allowed more intuitive analysis of the recognition effect of the models on different rice leaf diseases. From the figure, it can be seen that RiceDRA-Net outperformed the other five reference models in terms of its recognition of the four different rice leaf diseases, both with a single context and a complex context. As can be seen from Figure 12, RiceDRA-Net achieved 100% precision for three diseases, blast, brown spot and Tungro, with a single background. Although RiceDRA-Net had lower precision than MobileNet for bacterial blight with a complex background, RiceDRA-Net had better precision than the remaining five models for the remaining three diseases. This indicates that RiceDRA-Net had stronger robustness and stability, and high accuracy. Furthermore, the evaluation results can be seen in Table 8. RiceDRA-Net had a significantly higher recall and \(F1\) score for four different rice leaf diseases compared to the five reference models for both test sets, which indicates that RiceDRA-Net has a good recall ability and \(F1\) score and is less influenced by complex backgrounds. Consequently, it is suitable for rice leaf disease identification with a complex background. than those of the remaining five comparative models. This indicates that RiceDRA-Net had the highest recall and \(F1\) Score for each. It can be seen that RiceDRA-Net outperformed the classical models in the identification of different categories of rice leaf diseases. Overall, the RiceDRA-Net model studied in this paper had a good recognition effect and is able to meet the requirements of rice leaf disease recognition in general production. In classification tasks, PR curves are a common performance evaluation metric that can be used to measure the accuracy and recall of a classifier. PR curves visualize the trade-off between accuracy (precision) and recall. The average PR curve is a method of combining PR curves from multiple categories into a single graph to calculate their average. The average PR curve balances the performance of each category and provides an overall assessment metric. The area under the curve (AUC) is a commonly used performance metric. Higher AUC values indicate a better performance of the classifier. We compared the average PR curves of six different models using the CBG-Dataset in our experiments, as shown in Figure 15. The experiments showed that the RiceDRA-Net model proposed in this study had the highest AUC value, indicating that RiceDRA-Net exhibited the best performance in terms of the trade-off between precision and recall. The curve was very steep, indicating that the model was able to obtain higher precision while maintaining high recall. The computational complexity of a convolutional neural network can be determined by calculating the number of parameters and the computational volume of the model. Specifically, the number of parameters is the total number of weight parameters in all layers with parameters in the model. The computational volume is the number of floating-point of operations (FLOPs) required for forward inference.