Computer Vision-Based Food Wastage Detection System in University Dining Halls
Challenge
Food wastage is a pervasive issue in university dining halls, contributing to unnecessary operational costs, environmental concerns, and inefficient resource management. The challenge lies in accurately detecting and classifying food wastage, understanding the consumption patterns, and holding individuals accountable without creating an intrusive or complex monitoring system.
Key Challenges:
- Inaccurate Waste Detection:
Manually tracking food wastage is prone to human error and inconsistencies. - Identifying Repeated Offenders:
Tracking frequent food wasters and linking them to specific individuals without violating privacy. - Over-portioning in Kitchen:
Determining food wastage caused by over-portioning and food handling errors during the serving process. - Data Collection & Reporting:
Gathering actionable insights from food waste data in real-time and generating automated reports for university administrators.
Solution
To address these challenges, we developed a Computer Vision-Based Food Wastage Detection System that employs AI-driven image processing to detect and classify food waste in real-time. The system is designed to capture and analyze images of leftover food, track consumption behaviors, and provide insights for better food waste management.
Key Features of the Solution
- Advanced Camera Setup: High-resolution IP cameras capture detailed images of food waste at strategic disposal points. Infrared cameras are added for scale-up phases to ensure accurate detection under varying lighting conditions. Edge AI cameras provide real-time processing and analytics, reducing the need for heavy cloud computing.
- Food Waste Detection: The system uses deep learning models such as YOLOv8, Faster R-CNN, and custom CNNs to identify food items on plates before disposal, classify the type of food, and quantify the amount of waste.
- User Tracking: Integration with university systems, including Face Recognition or mobile apps, enables tracking of users who frequently waste food. This allows for identification and accountability.
- Automated Reporting & Alerts: Automated reports are triggered at predefined intervals, detailing specific offenders and the amount of food wasted. Real-time alerts are sent to stakeholders for immediate action when significant waste patterns are detected.
- Real-Time Monitoring Dashboard: A web-based analytics dashboard allows university administration to monitor food wastage in real-time, visualize consumption patterns, and track waste reduction efforts over time.
- Scalable Deployment Strategy: The system is implemented in phases, starting with a single disposal point and expanding to multiple points across the cafeteria. Future phases will include integration with kitchen stations and serving areas to monitor over-portioning trends.
Key Benefits
- Cost Savings: By detecting food wastage in real-time, the system helps reduce food waste, which translates into significant cost savings for the university’s dining services.
- Environmental Impact Reduction: With better monitoring and reduction of food waste, the system contributes to reducing the carbon footprint associated with food production and disposal.
- Accountability & Behavioral Change: The ability to track and identify individuals responsible for food wastage encourages students to consume responsibly and reduce unnecessary waste. This behavior shift can significantly cut down on waste over time.
- Data-Driven Decision Making: The automated reporting and analytics dashboard provide university administrators with insights into wastage patterns, enabling them to make informed decisions about portion sizes, menu planning, and waste reduction strategies.
- Efficient Waste Management: The system’s automated nature reduces the need for manual monitoring and reporting, streamlining waste management operations and allowing staff to focus on other tasks.
- Scalable & Future-Proof: The system is designed to be scalable, allowing it to expand across multiple dining halls and other university areas (e.g., kitchen, serving stations) as needed. Future enhancements may include integrating predictive analytics for even more efficient waste reduction.
- Real-Time Monitoring & Alerts: Stakeholders receive real-time notifications when waste patterns deviate from acceptable levels, ensuring quick corrective actions are taken, minimizing wastage before it becomes a larger problem.
- Improved Dining Experience: By optimizing food portions and reducing waste, students enjoy a better dining experience with better-served meals and an overall enhanced university dining environment.
Conclusion
The Computer Vision-Based Food Wastage Detection System is an innovative solution to a long-standing problem in university dining halls. By leveraging the power of AI and computer vision, the system not only reduces food wastage and associated costs but also promotes responsible consumption and sustainability. The phased implementation and scalable nature of the system provide universities with an effective tool for managing food waste while improving operational efficiency and contributing to environmental sustainability. The system’s ability to track, classify, and report waste will lead to more informed decision-making, better resource allocation, and, ultimately, a more sustainable dining experience for students.