DTF-YOLOv11n: An Improved YOLOv11n Model for Apple Mosaic Disease Severity Detection

Apple mosaic disease can impair leaf photosynthetic capacity, reducing apple yield and fruit quality. Accurate detection of leaf disease severity is essential for disease monitoring and developing effective management strategies. However, subtle phenotypic differences between adjacent disease severity levels, particularly in local chlorosis, mosaic texture, and lesion boundaries, make accurate severity discrimination challenging. These fine-grained disease features may also be attenuated during feature propagation and conventional fixed upsampling, further hindering severity detection. To address these issues, we adopted YOLOv11n as the baseline model and integrated DySample, Triplet Attention, and FasterNet-PConv to develop DTF-YOLOv11n for apple mosaic disease severity detection. In the Neck feature-fusion pathway, DySample replaced conventional fixed upsampling operations, enabling the model to learn adaptive sampling locations and better preserve local disease information during upsampling. We introduced Triplet Attention after fusing P3, P4, and P5 features to enhance disease-characteristic representation across severity levels through cross-dimensional feature interactions. In addition, we incorporated FasterNet-PConv into the Backbone’s deep feature extraction stage to reduce redundant spatial computation while controlling model complexity. Experimental results show that DTF-YOLOv11n achieved mean average precision (mAP) values of 91.31% at an intersection over union (IoU) threshold of 0.50 (mAP50) and 86.73% averaged across IoU thresholds of 0.50–0.95 (mAP50-95), representing relative improvements of 2.73% and 3.66% over YOLOv11n, while reducing the number of parameters from 2.591 M to 2.471 M. Compared with multiple YOLO-series and non-YOLO detection models, DTF-YOLOv11n demonstrated strong overall detection performance. The proposed model improves the representation of apple mosaic disease features across severity levels while maintaining relatively low model complexity, providing a technical reference for automated detection and severity grading of apple mosaic disease.