Multi-Scale Context Fusion Method with Spatial Attention for Accurate Crop Disease Detection

Crop diseases not only severely affect crop quality but also lead to significant reductions in yield. To address the challenge of accurate crop disease detection in complex environments, we propose a novel detection method based on the YOLO11 model, termed YOLO-MSCM. To enhance the extraction of small-object features, we introduce the MCSA module, which improves the model’s spatial feature perception. Additionally, a SimRepHMS module is designed to leverage local contextual information and strengthen multi-scale feature fusion. To improve the model’s adaptability and generalization capability, we employ DynamicConv, which dynamically adjusts channel weights based on input-specific patterns. For more accurate bounding box localization, we incorporate the WIoUv3 loss function to optimize box regression. Experimental results demonstrate that YOLO-MSCM achieves improvements of 6.1% in precision, 9.1% in recall, 6.2% in mAP@50, and 3.7% in mAP@50:95 compared to the baseline YOLO11n model. Comparative evaluations with several mainstream and state-of-the-art models further validate the superior detection performance of YOLO-MSCM, offering a reliable and effective solution for accurate crop disease detection in complex scenarios.