YOLOv11-RCDWD: A New Efficient Model for Detecting Maize Leaf Diseases Based on the Improved YOLOv11

Detecting pests and diseases on maize leaves under complex conditions, such as varying lighting and occlusion, is challenging, suffering from low detection accuracy and insufficient real-time performance. Hence, this study introduces the lightweight detection method YOLOv11-RepLKNet-CBAM-DynamicHead-WIoU-DynamicATSS (YOLOv11-RCDWD) based on an improved YOLOv11 model. The proposed method builds on the YOLOv11 model by introducing the RepLKNet module as the backbone network, which enhances the model's ability to express features of maize leaf pests and diseases while reducing the network parameters and computational complexity due to its efficient feature extraction capabilities. The Convolutional Block Attention Module (CBAM) is integrated into the neck feature extraction network to enhance the model's ability to select key features by introducing attention mechanisms in both the channel and spatial dimensions, thereby improving feature expression accuracy. Additionally, the DynamicHead detection head in the feature fusion structure dynamically adjusts the focus of attention through a unified attention mechanism for scale, space, and task awareness, improving detection accuracy and efficiency. Furthermore, the Weighted IoU (WIoU) loss function effectively handles the impact of low-quality samples on bounding box regression through a dynamic non-monotonic focusing mechanism. WIoU reduces the competitiveness of high-quality anchor boxes and the harmful gradients generated by low-quality samples, thereby enhancing the model's localization performance. Finally, the Dynamic Adaptive Training Sample Selection (DynamicATSS) label assignment strategy optimizes sample assignment based on statistical information during training by dynamically adjusting the selection mechanism for positive and negative samples, further enhancing the model's detection capabilities. Experimental results demonstrate that the improved YOLOv11-RCDWD model effectively detects pests and diseases on maize leaves. The precision, recall, and F1 score reach 92.6%, 85.4%, and 88.9%, respectively, an improvement of 4.9% and 9.0% over the baseline YOLOv11s. Notably, YOLOv11-RCDWD outperforms models such as Faster R-CNN, SSD, YOLOv5, YOLOv7, YOLOv8n, YOLOv9t, and YOLOv10n in detection speed (0.035 s), number of parameters (0.81 M), computational load (27.23 GFLOPs), and memory usage (6.46 MB), achieving the best balance between detection performance and resource efficiency. Overall, the improved YOLOv11-RCDWD model significantly reduces detection time and memory usage while maintaining high detection accuracy, supporting the automated detection of maize pests and diseases, and offering a robust solution for intelligent agricultural pest monitoring.