Real-time identification and quantification of apple scab on fruit in preharvest and postharvest conditions using...

Apple scab (AS), caused by the fungal pathogen Venturia inaequalis, is a major disease of apple that manifests as lesions on leaves and fruits. The disease compromises fruit quality and yield, leading to substantial economic losses. Traditional AS assessment relies on visual scoring, which is labor-intensive, subjective, and poorly reproducible. This study proposes a deep learning-based framework to overcome these limitations and to enable an accurate, scalable AS phenotyping approach.

Deep learning techniques were employed for the object detection and segmentation of AS symptoms in apple fruits. A two-stage fine-tuning process was applied to color images collected under orchard and laboratory conditions using the YOLO foundation model (YOLO11). The model was first trained to detect healthy apple fruits (Model 1) and subsequently refined to segment AS lesions (Model 2) using high-resolution imagery (864 × 864 pixels). Model 1 (Fruit Detection) achieved 0.98 precision, 0.95 recall, and 0.94 mAP50. Model 2 (Lesion Segmentation) achieved 0.64 precision, 0.75 recall, and 0.75 mAP50. The framework supports real-time processing of images and video. Despite challenges such as variable lighting and symptom heterogeneity, the use of high-resolution training data improved the segmentation accuracy (mAP50-95) of fine-scale lesions by over 50% compared to the previous YOLO architecture.

These results demonstrate that the proposed deep learning-based approach provides a reliable pipeline for automated AS phenotyping. By improving precision and efficiency in both controlled and field environments, the model enhances apple grading assessments and accelerates breeding efforts to identify AS-resistant genotypes. Furthermore, this work establishes a solid foundation for broader applications in real-time plant disease monitoring and future integration of additional apple diseases.