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Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms: Phase 2.

Bryan Gonzalez1, Gonzalo Garcia2, Sergio A Velastin3,4

  • 1Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

This study uses AI and computer vision for food portion estimation in dining halls. The system accurately identifies food items and estimates weight, improving catering service efficiency.

Keywords:
artificial intelligencecomputer visiondeep learningfood weight estimation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Food Science

Background:

  • Accurate quantification of food components is crucial for catering services.
  • Traditional methods for portion estimation can be labor-intensive and prone to errors.

Purpose of the Study:

  • To develop and validate an AI-powered system for food content identification and portion size estimation.
  • To leverage computer vision techniques for automated food quantification in self-service environments.

Main Methods:

  • Utilized the YOLO (You Only Look Once) deep learning architecture for object detection.
  • Employed RGB and depth cameras for capturing food volume and estimating weight.
  • Developed food-specific density models for accurate weight calculations.

Main Results:

  • Achieved a mean Average Precision (mAP) of 0.873 for food content identification.
  • Demonstrated low error margins in weight estimation: 5.07% for rice and 3.75% for chicken.
  • Validated the system's feasibility and accuracy in a real-world dining hall setting.

Conclusions:

  • The proposed AI and computer vision system offers a feasible and accurate solution for automated food quantification.
  • This technology can significantly enhance efficiency and precision in food catering services.
  • Further applications in nutritional analysis and inventory management are possible.