Machine learning to predict and prevent aquaculture mortality losses

Each year up to 20% of farmed salmonids die before slaughter. The death of large fish is of particular concern, considering the investment required for growing fish to this size. A high mortality rate also raises questions about the ethics and sustainability of the aquaculture industry.   Typically, mortality occurs during stressful incidents and interventions such as parasite treatment, deteriorating water quality, and transport to slaughter. Preliminary research has identified impaired cardiac health, often linked to deviating heart morphology, as a subsidiary element in viral diseases, such as Cardiomyopathy Syndrome (CMS), also known as “heart rupture”. The causes and effects of impaired cardiac health are poorly understood and the DigiHeart project aims to elucidate these in order to improve decision making in all production stages.    

Developing tools to improve diagnostic accuracy   

Working with partners in other Nordic countries, DNV will take advantage of the knowledge regarding different production practices used and the prevalence of heart disease in the three countries to analyze and understand the causes behind this issue. Today, operators lack models and tools to fully understand the causal relations between operations and cardiac health and predict the outcome of stressful interventions.