Intelligent visual analytics for food safety: A comprehensive review

Abstract

The emergence of food safety big data poses a huge challenge to data analysis and the application of technology. Intelligent visual analytics combines the advantages of artificial intelligence and visual analytics methods to process complex information more efficiently and accurately, providing technical support for intelligent food safety supervision. In this paper, we review the development and application of intelligent visual analytics for food safety over the past decade. First, we explore food safety data sources, data characteristics, and analytical tasks. Second, artificial intelligence methods and visualization techniques in food safety are presented respectively. Third, in-depth insights and applications of intelligent visual analytics methods from the perspective of food safety data characterization are provided, and typical cases are given. Finally, opportunities and challenges in intelligent visual analytics for food safety are proposed, including emerging technologies such as few-shot learning, automatic visualization generation, and large language models. The review aims to encourage researchers to propose more practical intelligent visual analytics solutions.