As concerns regarding animal welfare in aquaculture grow, researchers continue to explore new and less invasive monitoring techniques, such as machine vision (MV). This technology provides real-time, non-invasive monitoring of animal growth, behaviour, and welfare by assessing size-related parameters, stress indicators, and parasite load.
A recent review conducted by IGNITION researchers highlights the increased use of MV in aquaculture yet reveals that welfare data remains under-monitored. Additionally, MV has been used under conditions that do not fully reflect real aquaculture environments, which often have crowded tanks and low visibility. As camera hardware and detection algorithms continue to advance, MV holds great potential to significantly improve welfare monitoring in aquaculture.
For this technology to reach its full potential, three key challenges need to be addressed. First, MV algorithms should be developed and tested in commercial aquaculture settings, ensuring they can function in environments with suboptimal visibility and high animal densities. Second, multiple welfare metric systems should be designed to make them more economically viable and increase uptake by the aquaculture industry. Lastly, the cost, maintenance, and training requirements associated with implementing MV need to be carefully considered for effective industry integration.
To learn more about the role of MV in aquaculture welfare monitoring, read the full article here.
