# Lightweight Multiscale CNN Model for Wheat Disease Detection
## 3 Results
### Comparison of the Proposed Model with the Classical CNN Model  
To test the performance and effect of the proposed model, we compared it with six classic CNN models: AlexNet, VGG16, ResNet34, ResNet50, ResNet101, and InceptionresnetV2. After training, we drew the loss and accuracy curves of the seven models, as shown in Figure 11, to clearly and quickly compare the accuracy of the algorithms. The figure shows that the loss curves of InceptionresnetV2, ResNet34, ResNet50, and ResNet101 converge at the same speed as the ACC curves, slightly faster than those of the VGG16 and AlexNet models. Our IRCE model not only has the lowest training loss and the highest training accuracy but also the fastest convergence speed. In order to further demonstrate the advantages of our model, we compared seven indicators of various models. Table 6 shows that AlexNet has the least training time and the lowest FLOPs, but its F1-score is only 87.33%; VGG16 has the largest number of parameters, reaching 138.37 M, but its accuracy and F1-score are only 87.12% and 87%, respectively. The ResNet series models show little difference in the accuracy of disease detection. Among them, ResNet34 has an accuracy rate of 95.05%, ResNet50 has an accuracy rate of 96.52%, and ResNet101 has an accuracy rate of 95.68%. It is possible that Res  
Figure 10: Heat map of nine experiments. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Methods** & **CBAM** & **ECA** & **SE** & **CA** & **NAM** & **Accuracy (\%)** & **Precision (\%)** & **Recall** & **F1-Score** & **Param** (\%)** & **FLOPs** \\ \hline IR & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 96.05 & 96.13 & 96.17 & 96.15 & 4.22 & 0.83 \\ IRCBAM & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 96.46 & 96.54 & 96.55 & 96.55 & 4.24 & 0.84 \\ IRNAM & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) & 96.11 & 96.02 & 96.31 & 96.25 & 4.22 & 0.83 \\ IRSE & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 96.46 & 96.54 & 96.56 & 96.55 & 4.22 & 0.83 \\ IRCA & \(\times\) & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & 95.81 & 95.84 & 95.94 & 95.89 & 4.22 & 0.83 \\ IRECA & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 96.64 & 96.64 & 96.79 & 96.72 & 4.22 & 0.83 \\ IRCS & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 97.23 & 97.32 & 97.25 & 97.27 & 4.24 & 0.84 \\ IRCC & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & 97.66 & 97.66 & 97.67 & 97.66 & 4.24 & 0.84 \\ IRCE & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 98.76 & 98.77 & 98.81 & 98.79 & 4.24 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effect of attentional mechanisms on the model. other features as well as diseases, resulting in a lower accuracy rate than ResNet50. The accuracy rate of the InceptionresnetV2 model is 96.70%, but its parameters reach 55.80 M, its FLOPs reach 14.98 G, and its training time is as high as 5.8 h. The IRCE model has the minimum parameter of only 4.24 M, its FLOPs is second only to AlexNet, and its accuracy rate, precision rate, recall rate, and F1 average for wheat disease classification are 98.76%, 98.81%, 98.76%, and 98.78% respectively. The accuracy of the IRCE model is 2.06-3.71% higher than the ResNet series and InceptionresnetV2 models, and 11.61% higher than AlexNet. The time required to train the model is only 1.34 h, so the IRCE model proposed in this paper has excellent performance. To evaluate the recognition performance of the seven models for wheat diseases, we drew the confusion matrix of the seven models based on the test set, as shown in Figure 12. The models include AlexNet, VGG16, ResNet34, ResNet50, InceptionresnetV2, ResNet101, and IRCE. The actual class (abscissa) is compared with the predicted class (ordinate) in Figure 12 to describe the individual classification performance of each class.