Oak-YOLO: A high-performance detection model for automated Oak seed defect identification

Oak seeds are highly susceptible to pest infestations due to their elevated starch content, which significantly impairs germination and subsequent growth. To address this challenge, we developed a high-resolution imaging system and proposed an improved YOLO-based model named Oak-YOLO for efficient and accurate defect detection in oak seeds. The proposed model enhances the YOLOv8 architecture by incorporating EfficientViT as the backbone to improve global feature extraction, and integrates a Ghost-DynamicConv detection head to enhance the representation of small and irregular defects such as insect holes and cracks. Additionally, the WIoUv3 loss function is introduced to optimize bounding box regression for complex target shapes and overlapping instances.Extensive experiments were conducted on both single-object and multi-object datasets. Oak-YOLO achieved a mAP50 of 94.5%, an F1-score of 95.3%, and a precision of 94.% on the oak-intensive dataset, with an inference speed of 132.2 FPS. Cross-device validation using mobile-captured images further demonstrated the model's robustness, achieving mAP50 scores of 94.7% and 93.8% on different smartphone test sets. Comparative evaluations show that Oak-YOLO outperforms existing YOLO models, including YOLOv9 to YOLOv12, by delivering a favorable trade-off between detection accuracy and computational efficiency. These results highlight the potential of Oak-YOLO as a practical solution for real-time seed quality inspection in forestry applications.