Tomato is a major economic crop worldwide, and diseases on tomato leaves can significantly reduce both yield and quality. Traditional manual inspection is inefficient and highly subjective, making it difficult to meet the requirements of early disease identification in complex natural environments. To address this issue, this study proposes an improved YOLO11-based model, YOLO-SPDNet (Scale Sequence Fusion, Position-Channel Attention, and Dual Enhancement Network). The model integrates the SEAM (Self-Ensembling Attention Mechanism) semantic enhancement module, the MLCA (Mixed Local Channel Attention) lightweight attention mechanism, and the SPA (Scale-Position-Detail Awareness) module composed of SSFF (Scale Sequence Feature Fusion), TFE (Triple Feature Encoding), and CPAM (Channel and Position Attention Mechanism). These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness. Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%, a recall of 86.5%, and an mAP@0.5 of 90.6% on the test set, with a computational complexity of 12.5 GFLOPs. Furthermore, the model reaches a real-time inference speed of 987 FPS, making it suitable for deployment on mobile agricultural terminals and online monitoring systems. Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.