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How AI Food Recognition Works

Understanding the technology behind automated food waste identification

FoodSight•2025

2,400+

foods in database

~80%

accuracy rate

100%

weight accuracy

The Recognition Process

1

Image Capture

Camera detects motion and captures an image as food enters the bin. Scale records weight.

2

Neural Network Analysis

Image processed by AI trained on millions of food images, comparing against 2,400+ categories.

3

Confidence Scoring

Model calculates how confident it is about each possible match.

4

Classification Decision

If confidence exceeds threshold, item is classified. Otherwise, it goes to "Mixed Waste".

Understanding Mixed Waste

"Mixed Waste" is assigned when the AI isn't confident enough to classify an item. This is intentional—honest uncertainty is better than false precision.

Low Mixed Waste (under 15%)

Great conditions. AI is confidently classifying most items.

Moderate Mixed Waste (15-30%)

Normal range for most kitchens. Good category-level data.

High Mixed Waste (over 30%)

Worth investigating—may indicate lighting, camera, or menu issues.

Common Mislabeling Scenarios

Foods that look alike can get confused. The AI sees what the camera sees—it can't taste or smell.

Rice vs couscous vs quinoaSimilar texture and colour
Chicken vs pork vs turkeyPale proteins
Different pasta shapesMay not distinguish types
Sauces and liquidsHard to identify from appearance

Why 80% Accuracy Is Actually Good

The choice isn't between 80% AI accuracy and 100% manual accuracy. It's between 80% AI accuracy and effectively 0% usable data from abandoned manual logging.

80% accurate data that exists beats 100% accurate data that doesn't.

What matters is consistent measurement over time—trends are reliable even with some individual errors. And the scale never lies—you always know exactly how much went in the bin.

Best Practices for Better Accuracy

Optimise lighting: Ensure the bin area is well-lit without harsh shadows
Keep the camera clean: Wipe the lens periodically with a soft, clean cloth
One item at a time: When possible, dispose of one type of food at a time
Add custom foods: Add signature dishes or regional specialties to the database
Flag recurring errors: Flag items that are repeatedly misclassified

Reading Reports with AI in Mind

  • Category totals are more reliable than individual items
  • Trends over time are more reliable than single data points
  • Weight is always accurate—even when identification isn't
  • High Mixed Waste is a diagnostic signal, not a failure

See AI Food Recognition in Action

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