As a core global food crop, the stability of maize yield is directly related to food security. However, maize leaf diseases exhibit diverse morphologies, small initial lesions, and high similarity among different types of lesions, posing significant challenges for timely and accurate identification. Therefore, this paper proposes a maize leaf disease diagnosis model based on an improved YOLO11n. First, a C3k2AFE module is designed, which adaptively captures rich features by operating a spatial context module and a feature refinement module in parallel. In addition, an adaptive downsampling module is adopted to enhance edge and fine-grained feature extraction. Finally, by integrating the C2PSA module with the CASAttention attention mechanism, symmetric collaborative modeling between the spatial and channel domains is achieved, enhancing the model’s perception of diseases. Experimental results show that the improved YOLO11n model achieves an accuracy of 89. 8%, mAP@50% of 88. 7%, and mAP@50-95% of 71. 8%, which are 1. 9%, 1. 5%, and 1. 4% higher than those of the baseline YOLO11n model, respectively. The number of model parameters is 2. 36 MB, which is 8% lower than that of the baseline model. The corn disease diagnosis model proposed in this study effectively addresses the problem of disease detection in corn under complex environmental conditions, significantly improving the accuracy of detection and providing a reference for the application of corn leaf disease detection.
Yao et al. (Fri,) studied this question.