To address the issues of low detection accuracy, high computational cost and deployment difficulties in existing tomato leaf disease detection algorithms, this paper proposes a lightweight improved algorithm, YOLO11-WCL, based on the YOLO11n model. The algorithm introduces lightweight anti-aliasing wavelet pooling in the backbone network to replace traditional downsampling operations, effectively reducing network complexity. In the Neck part, a lightweight CA-HSFPN module is incorporated to enhance multi-scale feature fusion, while the LADH detection head is adopted to improve inference speed. Based on the experimental dataset, comparative analyses of lightweight networks, different detection algorithms, and ablation experiments were conducted. The experimental results show that the YOLO11-WCL model achieves only 50.1% and 60.3% of the parameters and computational complexity of the original YOLO11n model, corresponding to 1.29 MB and 3.8 GFLOPs, respectively, while reaching an mAP of 98.5%. These findings demonstrate that the proposed algorithm significantly improves the detection accuracy of tomato leaf diseases while maintaining model compactness, achieving high detection efficiency and good generalization performance. It is suitable for deployment on UAVs and other mobile devices, with broad application prospects and practical market potential.