A lightweight， real-time model of detecting leaf diseases in apple， named as SMPD-YOLO， was proposed based on YOLOv8n to solve the problems in existing methods of detecting leaf diseases in apple in terms of accuracy， real-time performance， and robustness under complex and noisy environments. The spatial pyramid pooling fast cross stage partial CSP （SPPFCSPC） module was embedded into the backbone network to enhance the capability of feature fusion. The minimum point distance-IoU （MPD-IoU） was introduced as the bounding box regression loss function to improve the accuracy and convergence speed of the model. The model volume and floating point operations were further reduced via layer adaptive magnitude-based pruning （LAMP）. A channel-wise knowledge distillation （CWD） strategy was used to boost detection performance. The results showed that the improved SMPD-YOLO model had a mean average precision（mAP@0.5） of 90.20% and a frame rate of 133.3 frames per second （FPS），while the weight and FLOPs of model was 5.0 MB and 7.3×10⁹ s^-1， respectively. The improved model maintained excellent robustness under complex and noisy environments including strong illumination， weak light， and blurred images. It is indicated that the SMPD-YOLO model combines high accuracy， lightweight design， and real-time performance， enabling the efficient detection of leaf diseases on resource-constrained equipment.