YOLOV8-CMS: a high-accuracy deep learning model for automated citrus leaf disease classification and grading.

Abstract

Background Citrus leaf diseases significantly affect production efficiency and fruit quality in the citrus industry. To effectively identify and classify citrus leaf diseases, this study proposed a classification approach leveraging deep learning techniques (YOLOV8 equipped with CSPPC, MultiDimen, SpatialConv, YOLOV8-CMS). Additionally, a segmentation method was utilized to extract leaf and lesion areas for disease severity grading based on their pixel ratio. Results By collecting and preprocessing a citrus leaf image dataset, the YOLOV8-CMS model was trained for disease classification. The model integrated MultiDimen attention, SpatialConv, and the CSPPC module to enhance performance. Furthermore, a segmentation approach was applied to precisely segment both leaf and lesion areas, enabling a quantitative assessment of disease severity. To verify the effectiveness of the proposed approach, multiple YOLO-based architectures, including different YOLOV8 series models, YOLOV5, and YOLOV3, were compared and analyzed. Results demonstrated that the proposed method achieved outstanding performance in citrus leaf disease classification, with an mAP50 of 98.2% in distinguishing healthy and diseased leaves and an accuracy of 97.9% in multi-class disease classification tasks. Conclusions The proposed YOLOV8-CMS model outperformed traditional methods in citrus leaf disease classification, while the segmentation-based approach enabled an accurate and quantitative assessment of disease severity. These findings highlighted the potential of deep learning in precision agriculture, contributing to more effective disease management in citrus production.

Plant phenotyping relevance

柑橘葉の病害分類と、葉・病斑の画像分割による病害重症度の定量化手法が研究の中心であり、植物状態の表現型を直接推定・検証している。

Code and data availability

The supplied blocks describe a compiled citrus leaf disease image dataset (public sources plus 1300 self-collected images) and the YOLOV8-CMS model, but contain no data availability statement, code deposit, repository link, or author-provided public URL for the dataset, annotations, trained model, or analysis code. No

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