Aiming to address the challenge of accurately identifying similar features of peanut leaf diseases in complex environments, an improved detection algorithm, YOLO-ADM, was proposed based on the YOLO v8n model. Firstly, the ADown module replaced part of the CBS module, reducing information loss during down sampling and decreasing the model’s parameter count. Secondly, a deformable attention mechanism was integrated into the C2f module to form the C2f-DA structure, which replaced the C2f module in the upper layer of the SPPF. This modification enabled the model to focus on critical regions of peanut leaf diseases and effectively capture their distinguishing features. Finally, a novel multi-scale feature fusion network, termed MFI Neck, was designed to replace the original YOLO v8n neck network, enhancing the model’s capacity for multi-scale feature fusion. Experimental results showed that the improved YOLO-ADM algorithm achieved precision of 92.3%, recall rate of 91.0%, mean average precision (mAP@0.5) of 95.6%, and mean average precision (mAP@0.5:0.95) of 85.2%. Compared with the original YOLO v8n model, these metrics were increased by 4.5, 0.2, 1.6, and 3.0 percentage points, respectively. This approach enhanced detection performance while maintaining model efficiency, effectively meeting the identification requirements of peanut leaf diseases in complex environments, and provided a reliable reference for the detection and monitoring of leaf diseases.