Precision Fish Detection (PFD) Analysis

Precision Fish Detection (PFD) Analysis is an advanced aquaculture intelligence system designed to identify hidden inefficiencies in fish production—particularly those that traditional metrics fail to detect.

In modern aquaculture, performance is typically assessed using indicators such as growth rate, feed conversion ratio (FCR), and feeding regimes. While these metrics are essential, they often mask underlying biological and operational issues—especially when fish losses occur unnoticed.

PFD Analysis addresses this gap.

The Core Problem

Fish loss is rarely visible as a single event.
Instead, it manifests subtly through distortions in production data:

  • Improved FCR despite underlying biomass loss

  • Stable feeding patterns masking reduced stock

  • Growth deviations that appear “acceptable” but are fundamentally misleading

This creates a dangerous scenario where performance appears optimal, while actual biomass—and therefore profitability—is compromised.

What PFD Analysis Does

PFD Analysis combines growth modelling, feed performance analysis, and statistical deviation detection to uncover these hidden inefficiencies.

It works by continuously comparing actual farm data against validated biological models, identifying discrepancies that indicate:

  • Missing fish within a cage

  • Incorrect stocking assumptions

  • Sampling inconsistencies

  • Environmental or operational stress factors

Key Analytical Modules

1. Snapshot Analysis (FCR-Based Detection)

A single-point analysis using observed weight and FCR to estimate actual fish numbers versus expected.

This allows rapid identification of biomass discrepancies and quantifies potential missing fish.

Example output includes:

  • Estimated actual fish count vs expected

  • FCR deviation from model

  • % biomass discrepancy

  • Actionable recommendations

As demonstrated in the system, even a small FCR deviation can indicate significant stock loss (e.g., ~7–8% missing fish in a single cage)

2. Growth vs Model Analysis

Compares real growth data against a validated biological growth curve.

This module detects:

  • Underperformance or overperformance

  • Sampling errors

  • Abnormal growth trends

  • Early signals of biomass inconsistency

It also classifies trends and provides structured interpretation, such as identifying volatile performance or environmental instability

3. Growth Prognosis

Projects future growth, feed requirements, and biomass based on current data and model alignment.

This enables:

  • Accurate harvest planning

  • Feed budgeting

  • Scenario modelling under different conditions

For example, the system can forecast time to target weight, total feed required, and final biomass under different temperature or growth scenarios

4. Missing Fish Intelligence

A core layer across all modules that interprets deviations in data patterns to determine the most likely causes.

This includes:

  • Detection of missing fish through FCR and growth discrepancies

  • Identification of false positives caused by sampling errors

  • Differentiation between biological underperformance and stock loss

The system assigns confidence levels and ranks likely causes, allowing operators to act with clarity rather than assumption.

5. Feed Optimisation (SFR Integration)

PFD integrates feeding intelligence through Specific Feed Rate (SFR) calculations, aligning feeding strategies with:

  • Fish size

  • Water temperature

  • Stocking levels

This ensures feeding decisions are biologically aligned and reduces inefficiencies in feed usage

Why PFD Analysis Matters

Feed represents the largest operational cost in aquaculture. Any undetected loss in biomass directly distorts:

  • Feed efficiency (FCR)

  • Growth performance metrics

  • Financial forecasting

  • Harvest planning

Without accurate biomass visibility, farms risk making decisions based on incorrect assumptions.

PFD Analysis restores data integrity.

It ensures that:

  • Reported performance reflects biological reality

  • Feeding strategies are aligned with true biomass

  • Losses are identified early—not after harvest

The Outcome

By implementing PFD Analysis, aquaculture operators gain:

  • Early detection of missing fish

  • Improved feeding accuracy

  • More reliable growth forecasting

  • Better financial control over production cycles

  • Reduced reliance on manual interpretation

Ultimately, PFD transforms aquaculture from a reactive process into a data-driven, predictive operation.

The Bottom Line

Your data can look right—while your stock is already wrong.

PFD Analysis ensures that what you see in your reports reflects what is actually happening in your cages.