Water Quality Monitoring | 24/7 Live Streaming
Seaweave’s smart water quality monitoring technology delivers continuous measurement of key surface and, soon, through-water column parameters at farms, intakes and assessment sites.
Designed as a rugged, self powered platform, it keeps watching conditions between site visits and streams clean data straight into the Flow dashboard.
Sensors such as temperature, salinity, dissolved oxygen, turbidity, pH and chlorophyll stream data into our Seaweave FLOW cloud software for alerts, health indices and trend analysis. Operators can view multiple rafts on one screen, overlay sites and protected areas, and scroll back through graphs to understand how conditions have changed before, during and after key events. The system runs unattended and uses cellular (and optional satellite) connectivity, giving teams a practical way to monitor hard to reach locations and long term trends without constant grab samples or manual instruments. Operators see changing conditions in near real time, plan stocking and harvest around periods of stress, and build climate resilience tools and early warning systems without visiting every site.
A well designed water quality monitoring system provides the raw material for powerful data dissemination and decision making tools when it is coupled with ML and AI (machine learning and artificial intelligence).
Fixed and mobile instruments measure key variables such as temperature, salinity, dissolved oxygen, chlorophyll, pH, turbidity, sea level and currents at farm and regional scales. These streams are transmitted in near real time to FLOW cloud infrastructure, automatically quality controlled and stored in a unified data set that can be accessed by authorised users through web based dashboards. Instead of fragmented spreadsheets or delayed reports, growers and regulators see a single, consistent picture of current and recent conditions.
Machine learning and AI models then sit over these data, learning relationships between environmental drivers, farm operations and outcomes such as growth, stress events or closures. Multi modal models fuse sensor time series, camera imagery and contextual information to generate nowcasts and forecasts, detect anomalies and flag emerging risks that may not be obvious from single parameters alone. This allows the system to move from simple plotting of data to actionable analytics that anticipate problems and highlight opportunities.
The outputs feed into role specific dashboards and scenario tools that support day to day decisions. Growers can view real time site conditions, short term forecasts and threshold based alerts, and can test “what if” scenarios, such as changing cleaning schedules, stocking densities or harvest timing under different environmental trajectories. Regulators and regional managers can see aggregated views across multiple sites, identify hotspots and coordinate responses to events such as harmful algal blooms or low oxygen episodes.
By bringing together continuous water quality monitoring, ML/AI modelling and intuitive visual tools, the system helps shift aquaculture management from reactive to proactive. Farmers gain clearer guidance for operational choices, regulators have better evidence for balanced decisions and both can communicate conditions and stewardship more transparently to markets and communities