## 4 Discussion  
Existing YOLO-based models have demonstrated notable achievements in various detection tasks, but they still face limitations in complex agricultural scenarios. For example, [PERSON] et al. [20] proposed YOLO-Leaf for apple leaf disease detection, which utilized DSConv, BiFormer, and IF-CIoU to enhance feature extraction and bounding box regression. Despite its improved accuracy, YOLO-Leaf still struggles with generalization in diverse environmental conditions. Similarly, [PERSON] et al. [21] developed COTON-YOLO by introducing the C2F-CBAM module and Gold-YOLO neck architecture, yet this model remains limited by rigid sample allocation strategies. [PERSON] et al. [22] enhanced the detection of maize leaf spot by integrating Slim-neck and GAM attention into YOLOv8, but challenges persist in efficiently handling small and densely packed targets. To address these limitations, our proposed model introduces novel techniques and optimizations tailored for agricultural detection tasks. The proposed YOLOv11s-RCDWD model demonstrates outstanding performance in detecting maize leaf diseases and pests by incorporating the RepLKNet backbone network, CBAM attention mechanism, Dynamic-Head detection head, WIoU loss function, and DynamicATSS label assignment strategy. Its detection accuracy, speed, and resource efficiency significantly surpass existing models. The model demonstrated an F1 score of 88.9%, precision of 92.6%, and a recall value of 85.4%. These findings represent improvements of 4.9% and 9.0% over the YOLOv11 baseline model. This suggests that the enhanced model is better able to locate and detect pests and diseases of maize leaves in complicated environments. Notably, the detection time for a single image is only 1.6 ms, significantly faster than traditional models (e.g., Faster R-CNN and YOLOv8), meeting the requirements for real-time detection. Furthermore, the model's parameter count and computational load are significantly lower than those of comparative models, with a memory footprint of only 16.4 MB, making it suitable for deployment on resource-constrained agricultural mobile devices. Compared with classic models such as SSD, Faster R-CNN, and YOLOv8, YOLOv11s-RCDWD exhibits clear advantages in terms of detection accuracy and speed. For instance, while Faster R-CNN performs well in detecting small targets, its detection speed is slower, making it challenging to meet real-time detection needs. On the other hand, YOLOv8 improves the speed but suffers from lower detection accuracy against complex backgrounds. Compared with recently proposed improved models, YOLOv11s-RCDWD outperforms other approaches in terms of detection accuracy and resource efficiency. For example, although YOLOv9c provides high detection accuracy, its large parameter count and computational load make deployment on devices with limited resources difficult. In contrast, the lightweight design of YOLOv11s-RCDWD significantly reduces the model's complexity. Compared with common attention mechanisms (e.g., ECA, GAM, and SimAM) and loss functions (e.g., CIoU and DIoU), CBAM, and WIoU enhance detection accuracy and precise localization. For instance, the CBAM's spatial attention and dual-channel processes greatly enhance the model's capacity to extract important characteristics. By dynamically modifying bounding box regression weights, WIoU simultaneously improves the model's learning capacity for difficult data. There are a number of important reasons why the YOLOv11s-RCDWD model performs better than other variants. By adding large-kernel convolutions, the RepLKNet backbone network improves the model's capacity to collect global characteristics. This architecture is particularly effective for detecting maize leaf diseases and pests in natural scenes characterized by complex backgrounds. Furthermore, the dual-channel and spatial attention processes of the CBAM attention mechanism improve the model's ability to extract features, greatly reducing false positives and missed detections.