John Q Liu¹ and Tyler Boulom², ¹USA, ²California State Polytechnic University, USA

This paper addresses the critical issue of food waste, which contributes to economic losses and environmental harm [1]. We propose a mobile application, Foodnomics, leveraging AI technology to estimate food waste costs and raise awareness among users [2]. The app utilizes image recognition to identify leftover food, estimate portion sizes, and calculate associated costs. Our experiments revealed strong accuracy in classifying common food items but highlighted challenges with less familiar items and regional price discrepancies. The proposed solution builds on existing methodologies by focusing on individual consumer behavior and providing actionable insights. By empowering users to track and reduce their food waste, our project offers a scalable and impactful tool for promoting sustainability and reducing food insecurity [3]. The study concludes with recommendations for further improvements, including expanding the training dataset and incorporating real-time pricing models.

Food Waste Reduction, AI-Powered Cost Estimation, Image Recognition Technology, Sustainability, Consumer Behavior