Despite advancements in detecting pests and diseases in maize leaves, existing methods still struggle with complex agricultural scenarios. For instance, occlusion and overlapping of maize leaves lead to missed and false detections [23], while variable lighting conditions can significantly degrade detection performance [24]. Moreover, the classification accuracy of existing methods is inadequate for differentiating between pests and diseases with similar morphologies [25]. The YOLO series algorithms, widely used for object detection, also exhibit limitations in this context. Their complex multi-branch backbone structure hampers the speed of disease detection and leads to underperformance when processing small and dense targets in complex environments [26]. Additionally, these methods rely heavily on predefined anchor boxes, resulting in slower inference speeds. The rigidity of sample allocation strategies further reduces the efficient utilization of features during training. To address these challenges, we propose a lightweight and efficient solution tailored for mobile agricultural environments. Our model aims to improve detection accuracy, enhance robustness under varying conditions, and optimize computational efficiency. This not only holds practical significance for real-time monitoring and management in agriculture but also contributes to the scientific development of efficient detection algorithms for use in resource-constrained settings. Motivated by the concerns outlined above, this study proposes a high-performance and lightweight model for detecting maize leaf diseases, named YOLOv11s-RCDWD (YOLOv11s-RepLKNet-CBAM-DynamicHead-WIoU-DynamicATSS). The proposed model is built upon the latest YOLO series object detection algorithm, YOLOv11s, with the aim of accurately and efficiently locating and detecting maize leaf diseases in natural scenes with complex backgrounds. Furthermore, this model is suitable for deploying on mobile devices. The main contributions of this study are as follows:  
1. In the backbone layer, we replace the C3k2 backbone network with the RepLKNet network, improving detection performance and efficiency through re-parameterization;
2. Introducing the CBAM (Selective Kernel Attention, CBAM) attention module to enhance the model's ability to capture multi-scale features. The CBAM allows the model to focus on key features of diseased maize leaves, thereby improving feature extraction capability;
3. Adopting the DynamicHead detection head to unify the scale-aware, space-aware, and task-aware aspects of object detection by integrating multiple attention mechanisms. This strategy significantly improves the representation ability of the detection head without increasing computational overhead;
4. Optimizing the loss function using the symmetric IoU loss function WIoU to improve the bounding box regression accuracy and enhance the model's localization capability;
5. Replacing the original fixed-ratio positive and negative sample allocation strategy with DynamicATSS to adjust the selection mechanism for positive and negative samples based on statistical information obtained during training. DynamicATSS improves the model's generalization ability.