Computer vision for infectious disease surveillance; Saprolegnia spp. in salmonids

Effective disease surveillance in wild fish populations is essential for food security and biodiversity conservation, but data acquisition can be limited by ad hoc reporting and resource-intensive laboratory diagnostics. We developed and evaluated a computer vision pipeline to detect saprolegniasis-like infections, a devastating disease in salmonids that manifests as visible signs. Compiling a dataset of 4526 images (494 infected, 4032 healthy) from citizen science platforms and stakeholders, we used data augmentation to address the significant class imbalance. We then fine-tuned and compared four pre-trained convolutional neural network architectures (EfficientNetV2S, EfficientNetV2B0, ResNet50, and MobileNetV3S), chosen to represent a range of standard and efficient models, to classify healthy versus infected fish across datasets of varying host taxonomic specificity. The EfficientNetV2S model achieved the highest performance on a Salmo spp. specific dataset, with a mean recall (proportion of infected fish images correctly identified) of 0.898 ( ± 0.043) and precision (proportion of correctly identified infected fish among all fish identified as infected) of 0.858 ( ± 0.067). Performance varied with host taxonomic scope, with models achieving lower metrics on broader host taxa datasets. Despite challenges including variable image quality, water surface reflections, and inherent class imbalance, these results show computer vision can support large-scale disease surveillance in wild fish populations. Computer vision-based surveillance could enable earlier outbreak detection and targeted diagnostics, improving freshwater ecosystem health management. While successful implementation hinges on acquiring sufficient high-quality imagery, this study highlights the potential of applying tailored Artificial Intelligence tools for monitoring visually detectable diseases across diverse wildlife species.