Can non-invasive methods be used for early detection of fish skin pathology? A red mark syndrome study

Fish diseases remain a critical bottleneck for aquaculture, threatening fish welfare and causing significant economic losses. Current diagnostic approaches rely on expert visual inspection or laboratory analysis, which are time-consuming, labour-intensive, and often only confirm disease presence at advanced stages. By contrast, automated systems have the potential to detect early symptoms under real farming conditions quickly and at low cost once established. In this study, we tested two approaches (image-based and eDNA-based) that could be used as early warning systems for disease detection. We developed and validated a computer vision system for the early detection of red mark syndrome (RMS) in rainbow trout ( Oncorhynchus mykiss ). Using still images and underwater videos from controlled cohabitation experiments, we trained deep learning models (YOLOv11) to recognise early and late RMS lesions. On still image datasets, the system achieved mean Average Precision at 0.5 IoU (mAP50, IoU - Intersection over Union) values of 0.87–0.92, while on underwater video frames, it reached 0.92, further improving to 0.96 after adding heuristic rules. Comparison with expert evaluations showed similar scoring of disease characteristics, demonstrating that the automated approach can replicate expert-level assessments. Parallel monitoring of environmental DNA for the suspected causative bacterial agent of RMS ( Midichloria -like organism) revealed only low-level signals, mostly in later stages, indicating that eDNA is not a reliable early indicator of RMS under flow-through aquaculture conditions. Our results demonstrate that automated computer vision can provide accurate, real-time, and remote detection of early RMS symptoms, offering a scalable early-warning system to support proactive fish health management and reduce disease impacts in aquaculture. • Automated computer vision detected early RMS lesions in rainbow trout with high accuracy (mAP50 up to 0.92 on images and 0.96 on video). • Automated lesion counts and colour trends closely matched expert assessments of disease progression. • eDNA monitoring detected the causative Midichloria-like organism only at low levels and mainly during late disease stages. • The image-based RMS signs detection can be adapted to any disease with visible signs • Image-based monitoring provides a reliable, real-time early warning system applicable under production aquaculture conditions.