# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 4 Results and Analyses
### YOLOv11n-ECS Ablation Experiment Performance Comparison  
An ablation experiment [24] refers to the process of deep learning model design and optimization to analyze the impact of certain components, layers, or parameters of the model on the model performance by removing or setting them to fixed values. To verify the effectiveness of each improvement module, the ablation test is carried out by different combinations of multiple improvement modules in different ways, and the ablation test is carried out on the network model by adding three improvement modules step by step. \"\(\surd\)\" indicates that the module is used, and \"\(\times\)\" indicates that the module is not used. The test results are shown in Table 4.  
As indicated in Table 4, after adding the EMA mechanism module to the network, the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 improved by 1.4 and 1.7 percentage points compared to the original YOLOv11n model, although the FLOPs slightly increased and the model weight size remained unchanged. This demonstrates that the EMA module not only enhances the model's feature extraction ability but also introduces a small additional computational overhead. Replacing C3k2 with the C3k2_PConv module increased [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 by 1.1 and 2.3 percentage points, while reducing parameters and FLOPs by 3.1% and 4%, respectively. Model size concurrently decreased by 0.2 MB. This improvement is due to the PConv module, which reduces computational redundancy by utilizing redundant information in the feature map and performing convolution operations only on a subset of the channels. Substituting the original CIoU loss with Shape-IoU boosted [EMAIL_ADDRESS] by 0.9 percentage points without increasing FLOPs, parameters, or model size. By introducing both the EMA and C3k2_PConv modules, the improved model's [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 increased by 1.8 and 3.6 percentage points compared to the original YOLOv11n model, while the computational cost, number of parameters, and model size were all reduced. Incorporating all three modules achieved detection [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 of 85.6% and 62.7%--gains of 2.3 and 4.2 percentage points over the baseline model--while reducing FLOPs, parameters, and model size to 96.8%, 96.0%, and 96.3% of original values. This effectively controlled the model's complexity while improving its performance. As shown in Figure 10, the improved model performs better in the presence of background interference such as dried leaves and soil, reducing false negatives and false positives. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**EMA**} & **C3K3-** & **Shape-** & \multirow{2}{*}{**[EMAIL_ADDRESS]\%**} & **[EMAIL_ADDRESS]\%** & **FLOPs/G** & **Parameters/M** & **Model** \\  & & **PConv** & & **IoU** & & & & **Size/MB** \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) & 83.3 & 58.5 & 6.4 & 2.5 & 5.5 \\
2 & \(\surd\) & \(\times\) & \(\times\) & 84.7 & 60.2 & 6.5 & 2.5 & 5.5 \\
3 & \(\times\) & \(\surd\) & \(\times\) & 84.4 & 60.8 & 6.2 & 2.4 & 5.3 \\
4 & \(\times\) & \(\times\) & \(\surd\) & 84.2 & 60.7 & 6.4 & 2.5 & 5.5 \\
5 & \(\surd\) & \(\surd\) & \(\times\) & 85.1 & 62.1 & 6.2 & 2.4 & 5.4 \\
6 & \(\surd\) & \(\times\) & \(\surd\) & 84.9 & 61.4 & 6.5 & 2.5 & 5.5 \\
7 & \(\times\) & \(\surd\) & \(\surd\) & 85 & 61.1 & 6.2 & 2.4 & 5.3 \\
8 & \(\surd\) & \(\surd\) & \(\surd\) & 85.6 & 62.7 & 6.2 & 2.4 & 5.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: YOLOv11n-ECS ablation experiment performance comparison. Figure 11: Comparison of detection effects of YOLOv11n and YOLOv11n-ECS: (**a**) YOLOv11n detection samples and (**b**) YOLOv11n-ECS detection samples. Figure 10: Comparison of detection effects of YOLOv11n and YOLOv11n-ECS: (**a**) YOLOv11n detection samples and (**b**) YOLOv11n-ECS detection samples. Note: \(\triangle\) in the figure indicates a repeat detection, \(\circ\) indicates a false positive, and \(\lozenge\) indicates a false negative.