Smart Aquaculture with AI Insights - VROC

Sea and land‑based fish farms face a host of complex challenges that impact operational efficiency, economic sustainability, and their potential to contribute to global food security. Sudden mortality events, disease outbreaks (such as sea lice), and unexpected equipment failures can devastate production, drive up operating costs, and strain resources. Traditional monitoring approaches — reliant on manual checks, reactive responses, and fragmented data — often fail to provide the insights needed to prevent losses or optimize performance.

To stay competitive and sustainable, aquaculture operators need predictive, data‑driven tools that can not only alert them to current conditions but also forecast future risks and opportunities before they materialize.

VROC’s Smart Aquaculture solution delivers an end‑to‑end data and AI platform tailored for modern fish farming operations. By aggregating real‑time and historical data from existing sources into the DataHUB4.0 central‑data warehouse, operators gain a holistic view of environmental conditions, animal health indicators, and system performance across the entire farm.

Once all time‑series data is efficiently stored and standardized, the OPUS no‑code AI platform empowers aquaculture teams — including engineers, asset operators, and subject matter experts — to build AI models without any programming knowledge. These models generate predictive insights, forecasts, and automated alerts that help management anticipate issues days or even weeks in advance.

OPUS analyzes data holistically across the entire operation rather than just in isolated silos. This broad scope enables the AI to detect subtle patterns and anomalies that traditional analytics might miss, providing actionable intelligence for decision‑making.

With VROC's Smart Aquaculture solution, fish farms can transform their data into practical foresight — enabling early intervention, predictive maintenance, and operational optimization that enhance farm performance while minimizing risks.