Fish Farm Monitor

AI-driven geospatial insights detect water quality changes, algal blooms, and biomass distribution to optimize feeding and health on fish farms.

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How does it work?

Conventional monitoring relies on manual sampling and labor-intensive inspections, leading to delayed detection of water quality issues and fish health risks. AI-based geospatial analytics automate continuous surveillance, enabling early detection and targeted interventions to reduce mortality and feed waste.

Early Anomaly Detection: High-resolution satellite and drone imagery feed AI models that detect water temperature, turbidity, and pH deviations in real time. Early warnings enable targeted aeration, oxygenation, or corrective dosing before fish stress escalates.
Algal Bloom Alerts: Spectral analysis identifies chlorophyll and cyanobacteria signatures associated with harmful algal blooms before they become visible. This allows operators to adjust water flow or apply treatments to prevent toxin accumulation and fish kills.
Biomass Estimation: AI algorithms analyze water clarity and fish schooling patterns to estimate biomass density non-invasively. Accurate stock assessments support optimal harvest timing and prevent overstocking, improving yield predictions.
Feed Optimization: Spatial distribution of fish is monitored to tailor feed delivery schedules and quantities to actual biomass needs. This reduces feed waste by up to 20% and lowers operational costs while maintaining growth rates.
Scalable Surveillance: Cloud-based processing handles imagery from multiple ponds, sea cages, and facilities simultaneously. Centralized dashboards offer unified views across sites, simplifying large-scale operations and rapid decision-making.
Regulatory Compliance: Automatically generated water quality and biomass reports align with industry regulations and certification standards. This streamlines audit preparation and ensures transparency with stakeholders and authorities.

Frequently asked questions

Have a different question and can’t find the answer you’re looking for? Reach out to our support team by sending us an email and we’ll get back to you as soon as we can.

How does AI-based geospatial monitoring compare to manual sampling?: AI-driven methods provide continuous, high-frequency data across all ponds or pens, whereas manual sampling is limited to periodic checks at specific locations. This comprehensive coverage reduces blind spots and reaction time for water quality issues.
Why is geospatial analytics popular in modern fish farming?: It combines remote sensing with AI to deliver real-time environmental and biomass insights without intrusive sensors. Farms adopt it for improved operational efficiency, lower labor costs, and proactive risk management.
What are limitations of satellite imagery for aquaculture monitoring?: Satellite data may be affected by cloud cover and limited temporal resolution, delaying critical alerts. Combining satellite with drone imagery can mitigate these gaps, offering higher frequency and resolution.
Can this platform replace in-situ sensors?: While geospatial analytics reduce reliance on physical sensors, they complement rather than entirely replace in-situ measurements for parameters like dissolved oxygen. Integrating both sources yields the most accurate and robust monitoring system.
How does this solution compare to acoustic fish biomass estimation?: Acoustic methods require specialized equipment and expert analysis, which can be costly and time-consuming. Geospatial AI offers a non-invasive, scalable alternative that leverages existing imagery with lower deployment overhead.
Are there alternatives for detecting algal blooms?: Traditional methods rely on manual water sampling and laboratory analysis, resulting in delayed feedback and limited spatial coverage. Automated spectral detection from imagery speeds up alerts and covers the entire water surface in near real time.

Geospatial AI Platform

Intelligence

AI & foundation models

Deep-learning and foundation models turn raw imagery into ready-to-use insights, so you ship answers instead of training pipelines.

Experience

Conversational workflow

Ask questions in plain language and the platform responds with charts, visualizations, and next step suggestions.

Speed

GPU-accelerated cloud

Cloud-native architecture spins up on-demand GPU clusters that scale from a single scene to global archives—no manual ops, no bottlenecks.

Data

Any sensor, any format

Optical, SAR, drone, IoT, vector or raster—ingest, fuse, and analyze without conversion headaches.

Visualization

Insight you can see

Real-time 2D / 3D maps and export-ready plots make results clear for engineers, execs, and clients alike.

Boost your productivity. Experience Geospatial AI.

Turn satellite, drone, and sensor data into clear, real-time insights using powerful AI – no complex setup, just answers you can see and act on.

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