AbstractFood information engineering relies on statistical and AI techniques (e.g., symbolic, connectionist, and neurosymbolic AI) for collecting, storing, processing, diffusing, and putting food information in a form exploitable by humans and machines. Food information is collected manually and automatically. Once collected, food information is organized using tabular data representation schema, symbolic, connectionist or neurosymbolic AI techniques. Once collected, processed, and stored, food information is diffused to different stakeholders using appropriate formats. Even if neurosymbolic AI has shown promising results in many domains, we found that this approach is rarely used in the domain of food information engineering. This paper aims to serve as a good reference for food information engineering researchers. Unlike existing reviews on the subject, we cover all the aspects of food information engineering and we linked the paper to online resources built using Open Research Knowledge Graph. These resources are composed of templates, comparison tables of research contributions and smart reviews. All these resources are organized in the “Food Information Engineering” observatory and will be continually updated with new research contributions.