Initializing a public repository for hosting benchmark datasets to facilitate machine learning model development in food safety.

Abstract

While there is clear potential for artificial intelligence (AI) and machine learning (ML) models to help improve food safety, the development and deployment of these models in the food safety domain is by and large lacking. The absence of publicly available databases that host well-curated datasets that can be used to develop and validate AI /ML models represents one likely barrier. Thus, we took three previously published datasets, which we further cleaned and annotated, and made them publicly available in a repository called Cornell Food Safety ML Repository. The selected datasets include (i) presence or absence of Listeria spp. in soil samples collected across the U.S. with paired metadata for soil properties, geolocation, climate, and surrounding land use, (ii) presence or absence of Salmonella and Campylobacter in young chicken carcasses tested in processing facilities with associated meteorological and temporal metadata, and (iii) presence or absence of fecal contamination as well as E. coli concentration in New York watersheds with associated metadata for land use, water attributes, and meteorological factors. These datasets can serve as benchmark datasets for developing ML models. To demonstrate the utility of the repository, we developed customizable scripts as well as LazyPredict (a quick screening method) scripts for training different types of ML models using the shared datasets. While this repository provides an important starting point that will allow for the development and testing of ML models to predict foodborne pathogens contamination in different sources, inclusion of further datasets is clearly needed to advance this field. This paper thus includes a call to action for the deposit of well-curated datasets that can be used for further development of predictive models in food safety. This paper will also discuss the benefits of such public databases, including the assessment of data-sharing scenarios using existing privacy-preserving techniques.