Digital Twin for Shelf Intelligence: AI-Driven Inventory Management for Minimizing Food Waste

This project aims to develop a solution for improving grocery store inventory management by leveraging AI-driven image recognition. Traditional inventory methods, which rely on manual counting or barcode scanning, are inefficient, labor-intensive, and prone to human error. Over an 8-week period, we designed and developed a basic iPad app capable of identifying specific types of fruit and automatically updating inventory records in real time. By utilizing the iPad’s camera and machine learning algorithms, the app demonstrates the potential to streamline inventory tracking, reduce manual labor, and improve accuracy in managing perishable goods. Future work will focus on expanding the app’s capabilities to include a wider range of products, integrating real-time tracking, detecting spoilage, and syncing with existing store management systems. This project provides a foundation for creating more efficient, automated inventory systems to minimize food waste and enhance operational efficiency in grocery stores.