# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 3 Results
### Effect of Improved Method on Model  
Table 3 gives a comparison of the effects of the three improvement methods on the GhostNetV2 model. These methods enhance the effect of the model by using different modules or techniques. The table lists the performance of the four models (GhostNetV2, GhostNetV2_A, GhostNetV2_B, and GhostNetV2_C) obtained using different improvement combinations on the dataset, including the accuracy, F1 score, number of parameters, model calculation amount, inference time, and other indicators. It can be concluded from Table 3 that GhostNetV2_A replaces Ghost in the bottleneck with CS-Ghost. Although the accuracy of the model is increased by 0.36%, it increases the computational cost. To reduce the training time, GhostNetV2_B introduces the FusedMBConv module on the basis of GhostNetV2_A. The training time of the model is significantly shortened, and the accuracy of the model is slightly improved, but the number of parameters of the model is increased by 0.34 M, and the amount of calculation is increased by 0.48 G. Due to the increased computational resources of the model, more memory is required to store the weight parameters. To further improve the performance of the model, GhostNetV2_C introduces the ECA attention mechanism based on GhostNetV2_B. ECA weights the features in the channel dimension through 1D convolution, which is less computationally expensive and more efficient, and allows the network to better analyze disease signatures, thereby improving network performance. GhostNetV2_C reached the highest level in the five indicators of accuracy, F1 score, training time, parameter number, and inference time, which are 95.44%, 95.30%, 1197 s, 5.00 M, and 27.94 ms, of which the training time is reduced by 37.49%. Figure 13 shows some visualizations of the output feature maps of the improved model and the original model block module. It can be seen from the figure that the improved model extracts brighter image feature information in Block1, such as texture, edge, color, etc. The visual information of the feature map in the deeper Block2 is reduced and the  
Figure 12: Heat map of four experiments. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **CS-Ghost** & **Fused-MBConv** & **ECA** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & **F1 Score** & \begin{tabular}{c} **Time** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Param** \\ **(s)** \\ \end{tabular} & \
\begin{tabular}{c} **FIOPs** \\ **(M)** \\ \end{tabular} &
\begin{tabular}{c} **Infer-Time** \\ **(G)** \\ \end{tabular} \\ \hline GhostNetV2 & \(\times\) & \(\times\) & \(\times\) & 93.20 & 92.96 & 1915 & 6.16 & 0.18 & 28.52 \\ GhostNetV2\_A & \(\surd\) & \(\times\) & \(\times\) & 94.68 & 94.56 & 1978 & 6.18 & 0.18 & 33.07 \\ GhostNetV2\_B & \(\surd\) & \(\surd\) & \(\times\) & 94.76 & 94.62 & 1242 & 6.20 & 0.66 & 31.12 \\ GhostNetV2\_C & \(\surd\) & \(\surd\) & \(\surd\) & 95.44 & 95.30 & 1197 & 5.00 & 0.66 & 27.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Improvement in the method on the model results. abstract information is increased. In Figure 13b,c, the feature extraction of Block2 in the improved model is very similar to the feature extraction of block in the original model, which proves that our replacement of the model's shallow CS-Bneck with FusedBMcon does not affect the model's learning of disease characteristics. To accurately evaluate the classification effect of the improved model on wheat yellow rust diseases, we drew a confusion matrix based on the test set, as shown in Figure 14, comparing the actual category (abscissa) and the predicted category (ordinate) to show each classification performance of the category. In the figure, 0 indicates healthy wheat, R indicates mild infection, MR indicates medium and small infection, MRMS indicates moderate sensitivity, MS indicates moderate infection, and S indicates severe infection.