AI-based Automated Food Recognition and Pay-As-You-Eat Billing System

Abstract

The traditional billing and nutrition tracking method still dependent on manual observation processes, leading to inaccurate food billing, food wastage, nutritional imbalance and fraudulent activities during food scanning. Food analysis and secure billing are still difficult to accomplish in a large-scale tray assembly system, in a hospital, in an educational institution or in a smart food service area. The study introduces an innovative AI-powered Smart Food Tray Analysis and Billing System with Nutrition and Fraud Detection, leveraging deep learning, image processing, and intelligent analytics to facilitate automated food recognition and dietary assessment. The proposed system uses YOLO-based object detection and segmentation methods that are advanced to detect food items in the images of the tray and determine the size of the portions in real time. The system also includes anti-fraud features to deter invalid or obstructed scans and automatically creates billing, nutritional, and recommendations for users. Plus, there's an inventory forecast and analytics feature for smart resource management and forecasting. The outcomes of the system are expected to be accurate billing, reduced human intervention, an increased nutritional awareness, minimized food wastage, and increased operational efficiency in smart cafeteria environments. The proposed framework is designed to be scalable, secure, and intelligent, while also offering a solution that is applicable to the modern food service ecosystem.