YOLO-APLD: A lightweight apple leaf disease detection model based on multi-scale feature fusion

The precise and timely identification of apple leaf diseases play a key role in targeted pesticide application in orchards. Conventional deep learning techniques encounter issues like the substantial size of model parameters and low detection accuracy across various disease scales in natural environments. To overcome these limitations, this paper presents YOLO-APLD, a lightweight algorithm for detecting apple leaf diseases, utilizing the improved YOLOv8n model. The proposed model incorporates four key improvements to enhance its detection performance. First, an EP-C2f enhancement module is embedded at the output of the backbone to strengthen the representation of local and structural features of damaged area, thereby achieving significant improvements in the recognition of morphologically complex diseases such as Rust. Additionally, SIoU and Focal Loss are combined to form Focal-SIoU loss, which simultaneously optimizes bounding box regression and classification, thus enhancing the detection stability for hard-to-distinguish samples and few-shot categories including Mosaic and Brown spot. Meanwhile, bidirectional feature pyramid network (BiFPN) is adopted in the neck for efficient multi-scale feature fusion, which strengthens the perceptual capability for both large-scale damaged area (Powdery mildew and Scab) and small-scale damaged area (Alternaria blotch and Gray spot). Finally, a Slim-neck structure is employed to simplify the feature fusion architecture, reducing model size and accelerating inference speed. Comprehensive experiments demonstrate that YOLO-APLD achieves excellent performance while maintaining real-time capability, with precision (P), recall (R), mean average precision (mAP), and F1-score reaching 88.5%, 84.3%, 88.5%, and 86.4% respectively. Compared to YOLOv8n, these metrics show respective improvements of 1.7%, 1.5%, 0.8%, and 1.6%. Meanwhile, floating point operations (FLOPs), parameter count, and model size are reduced by 22.2%, 23.3%, and 17.5% respectively. The detection frame rate on edge computing devices reaches 90.3 f/s, indicating significantly accelerated inference speed. Additionally, testing performance on grape and tomato datasets further validates the generality of the proposed method. In summary, YOLO-APLD exhibits strong detection performance in the field of apple leaf disease detection, and can provide practical technical support for precision pesticide application in orchards and on-site disease monitoring.