Real-time Identification and Quantification of Apple Scab on Fruit in Preharvest and Postharvest Conditions Using YOLOv11: A Deep Learning Approach
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Background Apple scab (AS), caused by the fungal pathogen Venturia inaequalis , is a major disease of apple that manifests as lesions on leaves and fruits. It significantly reduces 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. Results Deep learning techniques were employed for the object detection and segmentation of AS symptoms in apple fruits. A two-stage fine-tuning process using the YOLO foundation model (YOLOv11) was applied to color images collected under orchard and laboratory conditions. The first model achieved over 90% precision in detecting apples, while the second achieved 78% precision in identifying and quantifying AS lesions. The YOLO-based architecture supports the real-time processing of both images and video streams, enabling rapid in situ evaluation. Despite challenges such as variable lighting, shading, and symptom heterogeneity across developmental stages, the model’s performance was enhanced through extensive data augmentation, a diverse image dataset, and the use of high-resolution (840 × 840 pixels) training images, which improved detection of fine-scale features by 40%. Compared to manual scoring, this method is significantly faster, more objective, and more reproducible. Conclusion These results demonstrate the strong potential of the proposed deep learning-based approach as a robust and scalable tool for automated AS phenotyping. By improving the precision and efficiency of disease assessments in both controlled and field environments, this framework effectively supports apple grading assessments and accelerates breeding efforts aimed at identifying AS-resistant genotypes. Moreover, it establishes a solid foundation for broader applications in real-time plant disease monitoring and the future integration of additional apple diseases.