THE FARM PROBLEM
What needs fixing.
Static feeding tables are useful references, but they cannot fully reflect differences in growth, survival, biomass, recent feeding and pond-specific operating history.
AquaNusa / AI
PrecisionFeed
Farm-adaptive machine learning for daily feed planning using growth, biomass, survival and operational data.
Concept visualizationPRODUCT PURPOSE
What PrecisionFeed is designed to do.
Turn farm-specific production data into daily feed recommendations that reflect how a pond is actually progressing.
OPERATIONAL VALUE
What changes for the farm.
A repeatable, data-driven recommendation layer that supports operator judgement and creates a stronger link between farm records and daily feeding decisions.
WHY IT MATTERS
Built around daily farm operations.
Not another isolated technology layer — the goal is to make the existing farm workflow easier to see, repeat and improve.
Farm-specific recommendations
Use the farm's own production history and current pond information rather than relying only on a generic feeding guide.
Consistent planning
Give operators a clear daily recommendation that can be reviewed against actual feed delivered.
Better operational records
Track predicted feed, actual feed and growth-related variables in one decision workflow.
Ready for automation
Pass approved feed quantities to AeroFeed or other delivery systems when automation is part of the deployment.
HOW IT WORKS
A simple path from input to action.
Turn farm-specific production data into daily feed recommendations that reflect how a pond is actually progressing.
AI Feed Planning
Collect
Bring together growth, biomass, survival, feed and other relevant farm records.
Learn
Fit and validate a farm-adaptive model against historical production patterns.
Recommend
Generate a daily feed recommendation for the selected pond and operating context.
Review
Compare recommendation, operator decision and actual feeding to improve the farm record.
SYSTEM VISUALS
PrecisionFeed in context.
Visual concepts showing how the proposed system, interface and field workflow could look in an aquaculture environment.
BUILT FOR EXISTING FARMS
Start where your operation is today.
Deployment can begin with one focused problem, validate the workflow in the field, then expand only where the farm sees value.
Works with farm records
Start from the data the farm already collects and identify only the additional inputs needed for the pilot.
Human-in-the-loop
Recommendations support operator decisions; farms keep control over final feeding actions.
Scales by pond
Begin with selected ponds and expand once the workflow and model performance are validated.
AQUANUSASTANDALONE OR CONNECTED
PrecisionFeed can be one module — or part of the wider AquaNusa layer.
PrecisionFeed decides how much feed is recommended. AeroFeed can help deliver that approved amount, while PondSense and the AquaNusa Dashboard provide additional context and visibility.
PILOT FIT
Where we would start.
- Define the farm problem and success criteria.
- Select the ponds, data and hardware required for the first pilot.
- Validate the workflow with operators before expanding automation.
- Measure what changed and decide whether to scale.
DEVELOPMENT FOCUS
What matters technically.
Generalization across ponds, interpretable inputs, farm-specific validation and integration with existing feeding workflows.
R&D / PRODUCT DEVELOPMENT
Final capabilities depend on the farm's data, hardware configuration, integration constraints and field validation.
PARTNER / PILOT
Could PrecisionFeed solve a problem on your farm?
Tell us how your farm operates today. We can define a focused pilot around your ponds, available data, existing equipment and operating constraints.