## 3 Results
### Visualization of Analysis Results  
This study comprehensively evaluated and analyzed the performance of the YOLOv11s-RCDWD model in corn leaf disease identification, using a confusion matrix. The confusion matrix visually presents the model's classification performance across various categories, including correct and incorrect classifications. In Figure 8, the diagonal elements of the confusion matrix indicate the number of samples accurately classified by the model. In contrast, the off-diagonal elements represent instances of misclassification among different categories. Detailed analysis of the confusion matrix analysis enables a better understanding of the model's recognition capabilities and shortcomings across different disease categories. The experimental results indicate that the improved YOLOv11s-RCDWD model achieved generally high accuracy in identifying different disease categories, particularly in recognizing healthy leaves, for which its accuracy rate was 96.82%. Its recognition accuracy for northern leaf blight and gray leaf spot is also relatively high, reaching 93.49% and 92.55%, respectively. However, the model exhibited some confusion in distinguishing between round spot disease and southern leaf blight, with accuracy rates of 89.68% and 90.99%, respectively. This may be attributed to the potential visual similarity between these two diseases, leading to misclassification by the model when differentiating between them. Figure 9 compares the detection results of the improved YOLOv11s-RCDWD model against the original model. The mutual misclassifications observed among gray leaf spot, round spot disease, and southern leaf blight may stem from the subtle differences present in the early stages of these diseases. Consequently, the model may not have developed sufficiently robust features to effectively distinguish between these conditions, resulting in erroneous classifications. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **F1 Score/\%** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]** & **Detection** & **Parameter/** & **GFLOPs** & **MemoryCost/** \\  & & & & & **Speed/ms** & **M** & **MB** & **MB** \\ \hline Faster & 82.6 & 77.2 & 80.0 & 80.2 & 60.1 & 23.0 & 28.86 & 48.6 & 42.3 \\ R-CNN & 85.2 & 78.1 & 80.9 & 82.1 & 61.6 & 15.2 & 25.6 & 36.2 & 30.6 \\ YOLOv5s & 85.9 & 78.9 & 82.3 & 84.3 & 61.0 & 3.2 & 7.81 & 18.7 & 16.0 \\ YOLOv6s & 82.8 & 77.9 & 80.3 & 81.9 & 60.5 & 2.5 & 15.97 & 42.8 & 32.3 \\ YOLOv7 & 87.7 & 79.6 & 83.5 & 86 & 63.1 & 3.9 & 9.82 & 23.4 & 20.0 \\ YOLOv9s & 88.6 & 83.3 & 85.9 & 88.7 & 65.4 & 2.7 & 6.31 & 22.6 & 13.3 \\ YOLOv9c & 87.6 & 83.3 & 85.4 & 88.0 & 67.0 & 4.2 & 21.35 & 84.0 & 43.3 \\ YOLOv10s & 88.6 & 78.2 & 83.1 & 85.1 & 63.4 & 2.6 & 8.03 & 24.5 & 16.6 \\ YOLOv11s & 87.7 & 78.0 & 82.6 & 85.3 & 63.5 & 1.3 & 10.52 & 21.2 & 19.2 \\ YOLOv11s- & 92.6 & 85.4 & 88.9 & 90.2 & 72.5 & 1.6 & 9.41 & 19.3 & 16.4 \\ RCDWD & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of Maize Disease Detection Performance Among Different Network Models. Figure 9 compares the detection results of the improved YOLOv11s-RCDWD model against the original model. The mutual misclassifications observed among gray leaf spot, round spot disease, and southern leaf blight may stem from the subtle differences present in the early stages of these diseases. Consequently, the model may not have developed sufficiently robust features to effectively distinguish between these conditions, resulting in erroneous classifications. Figure 8: Confusion Matrix: YOLOv11s-RCDWD. Figure 9: The detection results of the improved YOLOv11s-RCDWD model compared with the original model. In summary, the YOLOv11s-RCDWD model demonstrated high accuracy in the maize leaf disease identification task, particularly excelling in recognizing healthy leaves and northern leaf blight.