A lightweight YOLOv11-based model for rice false smut detection under complex field conditions.

Rice false smut is an important fungal infection in the rice panicle stage, which occurs only in the panicle. Rice yield and quality will be seriously threatened after the occurrence of panicle disease. Early identification of disease is very important for precise prevention and control. However, in the actual field environments, complex light changes, the dense distribution of small disease spots, panicle overlapping shading, and other factors often result in the semantic attenuation of key discriminant information in the stage of visual feature extraction, which has brought great challenges to the early detection and prevention of the disease. To resolve the above problems, this study introduces a rice false smut detection model derived from an improved YOLOv11 framework, named Rice-Smut, to bolster the resilience and stability of the network regarding the identification of rice false smut disease under complex field backgrounds. Firstly, in order to enhance the feature capture capabilities for multi-scale and densely distributed lesions, the C3SC backbone feature extraction network combining the SCConv block is integrated. This architecture can significantly suppress the spatial and channel redundancy and augment the precise characterization of the texture features of the lesion. Then, the C2PSA-SE attention module is introduced to effectively filter the background interference and improve the precise positioning of dense small targets. Finally, to address the irregular structure of rice false smut lesions, the GIoU loss function serves as a substitute for the conventional CIoU, which enhances the network's proficiency in locating the irregular shape lesions. Experimental outcomes revealed that the Rice-Smut model yielded a precision of 79.3% and mAP@50 of 75.3%, which represented a 7.6 and 4.5 percentage point improvement over the baseline model YOLOv11. The model requires 2.41M parameters, with a model size of 4.9MB, which results in low computational complexity. The preliminary validation on mobile platforms shows that the method is viable for the potential to be applied to the real-time field detection and disease monitoring of rice false smut, and can provide support for disease control decision-making and field management.