Rapid detection of common scab, powdery scab, and enlarged lenticels in potato tubers using deep learning.

Abstract

Background Differentiating between potato common scab, powdery scab, and the physiological disorder of enlarged corky lenticels is challenging due to their similar visual symptoms. To address this, we propose YOLOv8-ST, an enhanced deep learning model that incorporates the Swin Transformer and Triplet Attention modules to effectively distinguish between these visually similar tuber blemishes. Results YOLOv8-ST is an enhanced YOLOv8 model with the integration of Triplet Attention and the Swin Transformer, which achieved significant accuracy improvements. Compared to the baseline of YOLOv3, YOLOv5, YOLOv6, and YOLOv8, YOLOv8-ST achieved the highest precision (0.903), recall (0.831), F1-score (0.866), mAP@0.5 (0.931), and mAP@0.5:0.95 (0.616), with strong performance in detecting common scab and powdery scab (both >0.9 at mAP@0.5 or precision). Detection outputs showed higher confidence (e.g., 0.94 for scab), fewer false positives, and no missed lesions, outperforming models prone to misclassification or overlap. Conclusion The YOLOv8-ST model enables fast, accurate, and reliable detection of common scab, powdery scab, and enlarged lenticels on potato tubers. This field-deployable solution supports early disease diagnosis and timely intervention, thus reducing crop losses. The model is available through the mobile app Plant Guardian, enabling growers to identify potato skin blemishes directly in the field, thereby advancing both practical disease management and agricultural AI applications. © 2025 Society of Chemical Industry.

Plant phenotyping relevance

ジャガイモ塊茎の病斑・生理障害を画像から検出する深層学習モデルを開発・比較し、精度を検証しているため、植物フェノタイピング手法が中心です。

Code and data availability

The paper's potato tuber disease image dataset (common scab, powdery scab, enlarged lenticels) and annotations are paper-specific phenotyping assets, but the authors state they are not publicly available and must be requested from the corresponding author. The only cited code (LabelImg) is a generic third-party tool, a

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