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.