Virtual Booth

Food waste in institutional cafeterias presents a significant challenge to environmental sustainability, operational efficiency and cost management. This project proposes the development of a Food Waste Tracker for Cafeterias, an AI-powered system designed to monitor, analyze and reduce food waste through real-time data collection and intelligent decision support. The system utilizes image recognition technology to detect and estimate food waste at tray return stations, eliminating reliance on manual tracking methods. Collected data is processed through an analytics dashboard that provides insights into waste patterns based on meal types, food categories and time trends.

Leveraging historical and real-time data, the system generates AI-driven recommendations to optimize portion sizes, improve menu planning and enhance demand forecasting. Automated reporting features further support management in identifying cost implications and implementing corrective actions. Additionally, an optional student engagement component promotes awareness and responsible consumption behavior.

The proposed solution offers significant environmental, economic and operational benefits by reducing waste, lowering costs and enabling data-driven decision-making. With strong scalability and applicability across educational institutions, corporate cafeterias and hospitality sectors, the system demonstrates high commercialization potential through flexible business models such as Software-as-a-Service (SaaS) and integrated hardware-software solutions. Overall, the Food Waste Tracker contributes to sustainable practices and supports institutions in achieving their environmental and operational goals.