Case Study

AI-powered food waste & nutrition monitoring in canadian healthcare facilities

In partnership with Agriculture and Agri-Food Canada (Government of Canada), LogMeal launched a pilot program across hospitals and senior residences to monitor food intake and waste in real time using its LogMeal Kiosk—an advanced AI and computer vision system equipped with depth-sensing cameras.

Background & Objectives

Food waste in hospitals and long-term care facilities presents serious challenges, not only in terms of environmental and economic impact, but also in relation to nutritional accuracy and patient well-being. Significant amounts of uneaten food, particularly

high-impact items like red meat, often go unmeasured, leading to inefficiencies in cost, sustainability reporting, and dietary planning.

In partnership with Agriculture and Agri-Food Canada (Government of Canada), LogMeal launched a pilot program across hospitals and senior residences to monitor food intake and waste in real time using its LogMeal Kiosk—an advanced AI and computer vision system equipped with depth-sensing cameras.

Project Objectives

Quantify food waste and intake with precision
Monitor consumption patterns of high-impact food types (e.g., red meat)
Reduce carbon footprint and food costs
Align with UN SDG 12.3 on responsible food consumption
Support SDG 3 by optimizing patient nutrition management

Initial Assessment

Identification of food service pain points: lack of visibility on food waste, overproduction, and inefficiencies in menu planning
Manual waste tracking was found to be inconsistent, labor-intensive, and prone to error
Deployment of the LogMeal Kiosk in tray-return areas to collect before-and-after images of patient meals
Focus on facilities with high variability in diets and consumption, including senior care environments

Food Waste Monitoring with LogMeal Kiosk

Pre-meal scan: The Kiosk captures an image of each tray before delivery, identifying:

- - Dishes
  - Exact portion sizes using depth data
  - Ingredients (including red meat)

Post-meal scan: When the tray is returned, the Kiosk automatically detects uneaten food:

- - Segments waste by food type (e.g., vegetables, proteins, starches)
  - Calculates consumed vs. wasted quantities
  - Identifies remaining ingredients

Data platform: Generates detailed, real-time reports for:

- Sustainability teams to track food waste and CO₂ emissions
- Operations teams to optimize procurement and menu rotation
- Nutritionists to improve meal planning

Results & Value of Logmeal in the programm

Outcomes

Higher compliance with prescribed dietary plans
Significant reduction in tray assembly and delivery errors
Improved patient safety, especially for those with food allergies or swallowing disorders
Transparent data logs for audit trails and quality reporting
Increased efficiency for kitchen staff and clinical nutrition teams

LogMeal Technology Value in the Program

Real-time food recognition using computer vision and AI
Instant comparison between delivered meal and planned diet
Alerts for mismatches to prevent delivery of incorrect trays
Professional dashboard for monitoring quality performance
Plug-and-play system, easily integrated with existing kitchen workflows
Scalable across networks of hospitals and long-term care facilities

Conclusion

Through this solution to the Government of Canada we have implemented a cutting-edge, AI-driven solution to ensure meal accuracy, patient safety, and clinical nutrition compliance. The use of the LogMeal Kiosk brings automation, traceability, and peace of mind to foodservice operations in complex clinical settings.

partners and collaborators

HEALTH & NUTRITION REFERRALS

Dr Tania NGAPO (elle, she/her)

Meat Scientist, Science and Technology Branch (Saint Hyacinthe, Quebec)
Agriculture and Agri-Food Canada / Government of Canada