## 5 Conclusions  
This study proposes an improved YOLOv11-based model, YOLOv11-RCDWD, for detecting maize leaf diseases and pests. Incorporating the RepLKNet module, CBAM module, DynamicHead detection head, WIoU loss function, and DynamicATSS label assignment strategy significantly enhances the model's performance in maize leaf disease and pest detection tasks. The experimental results demonstrated the following:  
1. The improved model, YOLOv11s-RCDWD, exhibited the best detection performance, with all accuracy metrics surpassing those of the comparative models. Its precision reached 92.6%, recall was 85.4%, and F1 score was 88.9%, all higher than the other models;
2. In terms of [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95, YOLOv11s-RCDWD achieved 90.2% and 72.5%, respectively, improvements of 4.9% and 9.0% over YOLOv11s, indicating stronger detection capabilities in complex scenarios;
3. The parameter count and computational load (GFLOPs) of YOLOv11s-RCDWD remained largely consistent with YOLOv11s, at 9.41 M and 10.52 M, respectively, which are significantly lower than those of more complex models like YOLOv9c. Its memory footprint (memory cost) was found to be 16.4 MB, a reduction from YOLOv11s's 19.2 MB, demonstrating the improved model' more efficient utilization of resources;
4. Different backbone networks exhibited significant performance variations in maize disease detection tasks. The YOLOv11 model with RepLKNet as the backbone (YOLOv11s-RepLKNet) demonstrated notable comprehensive advantages in four key aspects: detection accuracy, detection speed, model complexity, and resource usage, particularly excelling in detection accuracy. RepLKNet achieved the highest values in both precision and F1 score, reaching 89.1% and 84.2%, respectively;
* Comparing seven common attention mechanisms--CBAM, EC, EMA, GAM, SA, SimAM, and SK--demonstrated that the CBAM attention module significantly outperformed other attention mechanisms in terms of detection accuracy. Its precision reached 90.5%, and its F1 score was 84.1%. Furthermore, YOLOv11s-RCDWD efficiently utilizes computational resources, attaining an ideal equilibrium between detection accuracy and speed. Its streamlined architecture with the fewest parameters and FLOPs ensures minimal usage of computational resources, facilitating deployment on agricultural machinery equipped with embedded systems. The findings indicate that the YOLOv11s-RCDWD model offers substantial advantages in the detection of maize leaf diseases. offering an efficient and practical solution for intelligent detection of agricultural diseases. Overall, YOLOv11-RCDWD performed well in the maize leaf disease and pest detection tasks, offering a robust technical method for the intelligent detection of agricultural diseases. Future research could further optimize the model's computational efficiency and extend its application to broader agricultural scenarios. Additionally, we will further enhance the scale and diversity of the dataset to improve the model's generalization capabilities. We will also investigate more efficient techniques for model compression to satisfy the deployment requirements of mobile devices. Furthermore, we aim to extend this approach to disease and pest detection in various crops, thereby providing more comprehensive technical support for the advancement of smart agriculture. Conceptualization, J.H. ; methodology, J.H. and Y.R. ; software, J.H. and W.L. ; validation, J.H. and W.L. ; formal analysis, J.H. and W.F. ; investigation, Y.R. ; resources, J.H. ; data curation, J.H. ; writing--original draft preparation, J.H. ; writing--review and editing.J.H. and W.L. ; visualization, W.F. ; supervision, W.L. ; project administration, W.L. ; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Proof of Concept Fund (XJ2023230052), the Shaanxi Provincial Water Conservancy Fund Project (2024 SLKJ-16), and the research project of Shaanxi Coal Geology Group Co., Ltd. (SMDZ-2023 CX-14). Not applicable.