ROI – Enhancing Detection of Citrus Disease Based on YOLOv10
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One of the most important fruit crops in the world is citrus. However, some citrus diseases spread rapidly, which is why early detection at an accurate stage is important for timely intervention. YOLO-based object detection models, such as the latest YOLOv10, where small lesions are difficult to identify among noisy backgrounds, have recently been developed, yet their accuracy tends to degrade. Therefore, we proposed a citrus disease detection model by integrating the region of interest (ROI) for object segmentation with the YOLOv10 model, thus addressing the issues of low detection accuracy and slow inference time. The proposed model was trained on an annotated dataset of three major citrus pathologies: anthracnose, citrus canker, and leaf miner infestation. Results showed significant improvements in the performance of our proposed model, which incorporates the ROI mechanism into YOLOv10. Specifically, the ROI-YOLOv10 model achieved high mAP scores, reaching 0.99 and 0.985 during training and validation, respectively, and maintaining high generalization capabilities with a test mAP of 0.984. Precision and recall metrics similarly underline the enhanced accuracy and robustness of ROI-YOLOv10. Compared with previous YOLO-based studies, our model exhibits enhanced accuracy and faster inference times. The incorporation of ROI techniques into the YOLOv10 framework is a highly effective approach for improving agricultural productivity by facilitating early and precise detection of plant disease.
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