Aquaculture producers face increasing pressure to maximize yield while minimizing biological risk across diverse species and production systems. Disease challenges, environmental fluctuations, inconsistent growth, and welfare concerns can all lead to significant operational and financial impacts. This presentation explores how machine learning (ML) and artificial intelligence (AI) are reshaping risk management and performance optimization by transforming complex biological and environmental data into actionable predictions.
A core pillar of this approach is blood biochemistry, which provides direct, physiological insight into how fish are responding to their environment. When integrated with water quality information, feeding data, health observations, and growth metrics, blood biomarkers significantly strengthen model accuracy by detecting early signs of stress before they appear in visible indicators. This positions WellFish Tech’s predictive tools apart from systems that rely solely on environmental or production datasets.
By combining these diverse data streams, our AI models can identify early warning signals of stock stress, predict mortality risk, and simulate the outcomes of management decisions. Predictive dashboards and risk scoring systems help farmers intervene earlier, optimize resource allocation, and improve stock robustness. Scenario modelling further enables producers to explore trade-offs among growth, welfare, survival, and market conditions, empowering more confident, data-driven decision-making.
This session will also address practical implementation considerations, including data quality, model transparency, and producer adoption, and outline scalable frameworks for integrating AI into daily operations. By uniting deep biological insight—anchored by blood biochemistry—with advanced computational methods, machine learning provides a pathway toward more resilient, efficient, and profitable aquaculture.