Cotton is an important economic crop in China, and its diseases have a significant impact on yield and quality. Therefore, it is crucial to quickly and accurately identify the types of diseases. However, existing object detection models mostly focus on improving detection accuracy while neglecting detection efficiency. These models typically have large computational requirements and a large number of parameters, making it difficult to deploy them on resource-constrained edge devices. To address this issue, this study proposes an improved YOLO11 algorithm——SDP-YOLO. The StarNet is used as the backbone network structure to reduce the number of model parameters. A DRBNCSPELAN4 module is proposed to replace C3K2 in the neck network, enhancing the semantic and positional information within the features to improve the model’s feature extraction capability. A lightweight partial convolution detection head, EPCD, is introduced to improve the model’s ability to extract important features and significantly reduce complexity. The Wise-IoU bounding box loss function is used to improve the network’s performance in bounding box regression and detection effectiveness for target diseases. Experimental results show that the improved model demonstrates significant reductions in various metrics: a 43.8% decrease in the number of parameters, a 96.9% decrease in the total floating-point operations, and a 39.6% decrease in model size, while increasing the detection accuracy by 1.3% and the FPS by 40 frames, significantly improving detection efficiency.