Computer Vision in Food Recognition: How It Works

Computer vision—the field of AI that enables machines to interpret images—powers food recognition in waste monitoring systems. Understanding the basics helps set realistic expectations and evaluate solutions.

The Fundamentals

Computer vision for food recognition involves:

Image acquisition: Camera captures image of food waste Preprocessing: Image enhanced for analysis (lighting normalisation, cropping) Detection: AI identifies regions containing food objects Classification: Each region assigned to a food category Post-processing: Results validated and formatted

Neural Network Architecture

Modern food recognition uses deep learning—neural networks trained on large datasets of food images.

Convolutional Neural Networks (CNNs): Process images through layers that detect increasingly complex features—edges, textures, shapes, objects.

Transfer learning: Models pre-trained on millions of general images, then fine-tuned on food-specific data.

Multi-task learning: Single model handles detection and classification simultaneously.

Training the Models

AI models learn from labelled examples:

1. Collect thousands of food images
2. Label each image with food types present
3. Train model to predict labels from images
4. Test on held-out data to measure accuracy
5. Iterate to improve performance

Quality and diversity of training data largely determines model capability.

Challenges in Food Recognition

Visual similarity: Many foods look alike (different lettuces, minced meats) Preparation variation: Same ingredient looks different raw/cooked/chopped Occlusion: Items hidden by other items Mixed dishes: Soups, stews, casseroles with multiple ingredients Lighting variation: Kitchen lighting isn't controlled like lab conditions

These challenges explain why no system achieves 100% accuracy.

Accuracy vs. Utility

Perfect item-level accuracy isn't always necessary:

• For cost tracking: Category-level accuracy (proteins vs. vegetables) may suffice
• For intervention: Knowing "prep waste is up" matters more than exactly which prep
• For reporting: Aggregate accuracy matters more than individual-item accuracy

Match accuracy expectations to your actual use case.

Edge vs. Cloud Processing

Edge (on-device): Faster response, works offline, data stays local Cloud: More powerful models, easier updates, requires connectivity

Many systems use hybrid approaches—initial processing on edge, complex analysis in cloud.

Learn about our AI recognition technology and how we approach food identification.