This paper presents an improved YOLOv8 model for the recognition of corn leaf spot disease. The model is enhanced by incorporating Slim-neck modules and GAM attention modules to improve the accuracy and efficiency of disease identification in complex field environments. The improved model achieves a precision (P) of 95.18%, a recall (R) of 89.11%, an average recognition accuracy (mAP50) of 94.65%, and an mAP50-95 of 71.62%. Compared to the original YOLOv8 model, the enhanced model shows improvements of 3.79%, 4.65%, 3.56%, and 7.3% in P, R, mAP50, and mAP50-95, respectively. The model also reduces parameter complexity and simplifies the network structure, leading to faster convergence and improved performance. The improved model outperforms other models such as YOLOv3, YOLOv5, YOLOv6, Faster R-CNN, and SSD in terms of accuracy and computational efficiency. The model is capable of accurately identifying and localizing corn leaf spot disease in complex field environments, providing robust technical support for agricultural production. The study demonstrates that the enhanced YOLOv8 model is effective in detecting corn leaf spot disease with high accuracy and efficiency, making it a valuable tool for agricultural disease identification.This paper presents an improved YOLOv8 model for the recognition of corn leaf spot disease. The model is enhanced by incorporating Slim-neck modules and GAM attention modules to improve the accuracy and efficiency of disease identification in complex field environments. The improved model achieves a precision (P) of 95.18%, a recall (R) of 89.11%, an average recognition accuracy (mAP50) of 94.65%, and an mAP50-95 of 71.62%. Compared to the original YOLOv8 model, the enhanced model shows improvements of 3.79%, 4.65%, 3.56%, and 7.3% in P, R, mAP50, and mAP50-95, respectively. The model also reduces parameter complexity and simplifies the network structure, leading to faster convergence and improved performance. The improved model outperforms other models such as YOLOv3, YOLOv5, YOLOv6, Faster R-CNN, and SSD in terms of accuracy and computational efficiency. The model is capable of accurately identifying and localizing corn leaf spot disease in complex field environments, providing robust technical support for agricultural production. The study demonstrates that the enhanced YOLOv8 model is effective in detecting corn leaf spot disease with high accuracy and efficiency, making it a valuable tool for agricultural disease identification.