# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 5 Discussion and Conclusions  
### Discussion  
To mitigate issues like occlusion, false positives, and false negatives encountered during the original model's detection, we introduce the EMA mechanism to enhance feature extraction capabilities and integrate PConv into the C3k2 module to reduce computational redundancy. Furthermore, the CIoU loss function is replaced with Shape-IoU to improve the localization accuracy for diseased cotton bolls. The [EMAIL_ADDRESS] of the improved YOLOv11N-ECS model reaches 85.6%, representing an increase of 2.3 percentage points compared to the base YOLOv11n network model. Compared to the original model, the improved model demonstrates higher detection accuracy in scenarios with numerous targets and significantly reduces both false positives and false negatives in complex environments featuring distractions like falling leaves. [PERSON] et al. [30] proposed an improved YOLOv5s-ESTC model for detecting Castanche deserticola in natural environments characterized by occlusion and dense distributions. Their model achieves an mAP of 89.8%. It is important to note that the images used in YOLOv5s-ESTC were captured at close range, where targets occupy a larger image area. In contrast, our study involves downscaling images (from 4000 \(\times\) 3000 to 640 \(\times\) 640), which reduces the pixel proportion of cotton bolls, leading to the loss of some detailed information and consequently increasing the difficulty of detecting diseased bolls. Despite this challenge, YOLOv11n-ECS achieves competitive performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **[EMAIL_ADDRESS]/\%** & **FPS** \\ \hline YOLOv11n & 81.4 & 52 \\ YOLOv11n-ECS & 84.2 & 56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model deployment test results on Jetson TX2. In terms of model complexity, the FLOPs, parameters, and model size of the improved YOLOv11n-ECS were reduced to 96.8%, 96%, and 96.3% of the original YOLOv11n network, respectively. The introduction of PConv was pivotal to achieving this efficient design, reducing computational redundancy while maintaining high recognition accuracy. This lightweight approach prepares the model effectively for engineering deployment. [PERSON] et al. [31] proposed a lightweight rice disease recognition model named YOLOv8--DiDL. The model incorporates modules, including the inverted residual mobile block and deformable convolutional networks v2, and replaces the spatial pyramid pooling fast (SPPF) module with a large separable kernel attention (LSKA) module. Compared with YOLOv8n, the model weight is decreased by 9.7%, and the number of parameters is reduced by approximately 9.3%. In this study, the PConv-based design and architectural refinements enabled a significant reduction in computational burden while still achieving a substantial 2.3 percentage point increase in mAP over the base YOLOv11n. This balance of high accuracy, reduced parameters, and faster detection speed better meets the real-time requirements of agricultural production. During data collection, some images exhibited issues like blurred backgrounds and overexposure. To address these issues, the deep learning-based Super-Resolution Convolutional Neural Network (SRCNN) algorithm could be applied prior to data processing to enhance blurred images and restore detail. Simultaneously, algorithms such as adaptive histogram equalization can be used to adjust the brightness of overexposed images to enhance contrast. Additionally, the approach combining YOLOv5 with dark channel prior enhancement, proposed by [PERSON] et al. [32,33], could also be referenced to address low-light nighttime image challenges. Furthermore, collecting images under diverse nighttime conditions also could enrich the dataset and enhance model robustness.