Matt Stasiewicz

The aims are to improve food safety and resilience in the poultry supply chain by: 1) Using machine learning to identify predictors of rare high-level Salmonella contamination in products, 2) Developing a federated learning approach for processors to privately improve safety, and 3) Modeling supply chain responses to contamination events to enhance AI-driven resilience.

The project targets the isolated nature of data in food processing, by fostering collaboration and developing AI infrastructure in areas like food safety, Federated Learning, and inventory strategies. The broader impact is the creation of a food safety testing database for public-domain data, addressing the need for data availability and becoming a widely used exploration tool.

The project focuses on accelerating the scaling of agricultural AI technologies by creating an open-source Python library, AgML, consolidating machine learning datasets, benchmarks, pre-trained models, workflows, and synthetic data generators, building on successes from two Year 1 AIFS proposals and aligning with the Year 2 RFP.