Multimodal Deep Learning-Based Intelligent Food Safety Detection and Traceability System

Abstract

Food safety has become a critical global issue, requiring effective solutions to reduce health risks and economic losses. The rapid advancement of artificial intelligence (AI) and deep learning (DL) provides new opportunities to address this challenge. This study presents a multimodal food safety detection system that integrates computer vision (CV), natural language processing (NLP), and sensor data analysis to comprehensively monitor food contamination, quality deterioration, and supply chain security. Specifically, the Swin Transformer model is employed for surface defect detection, while temporal convolutional networks (TCN) predict storage environment conditions. Additionally, blockchain and federated learning technologies are incorporated to establish a secure and efficient data-sharing framework, enabling cross-supply chain collaboration and enhancing traceability accuracy. Experimental results show that the system achieves an accuracy rate of over 98% in food contamination detection and supply chain anomaly monitoring, significantly improving food safety management. This study offers a practical and innovative approach to enhancing intelligent food safety regulation.