Smart technology is changing how we spot marine pests before they spread

For decades, checking boats for marine pests has relied on a simple but logistically difficult method: sending divers underwater to visually inspect hulls, propellers and niche areas. 

New research is showing how autonomous underwater technology and artificial intelligence could dramatically improve how we detect marine biofouling, and stop invasive species before they spread.

A recent study by University of Auckland researchers, supported by Kōtare, tested whether computer vision and large language models could accurately assess biofouling using autonomous underwater vehicles (AUVs).

The researchers tested multiple approaches. Traditional computer vision models were good at spotting the obvious, such as very clean or very dirty hulls, but struggled with the grey area in between, where early-stage pests often hide. AI language models, when carefully guided with official LoF rules, performed surprisingly well, offering not just a score but a written explanation of what they could see and why it mattered.

Most promising of all was the idea of hybrid systems combining image analysis with AI reasoning to estimate fouling coverage and explain the risk in plain language.

This is where Kōtare’s work comes in. Their autonomous survey vehicles already collect high-quality underwater imagery without putting divers at risk, and this research shows how that data could soon be assessed automatically, consistently, and at scale.