AI can help consumers understand the health effects of their food beyond Nutrition Facts Labels. The goal is to empower users to know what they eat and how it affects their health. We will develop a "food photo to ingredients" and an "ingredients to health outcome" module. In collaboration with the Dietary Assistant project, these will integrate into a mobile app.

This project supports sustainable agriculture by using AI to reduce fertilizers, pesticides, and water while maintaining crop quality and yield through improved modeling and precision application. It focuses on soil and hydroponic crops. Part A covers modeling, simulation, and data integration, optimizing resource use, while Part B focuses on field data hardware.

This project tackles four bottlenecks in digital food safety across the farm-to-table supply chain by developing algorithms, data standards, and frameworks for privacy and text analytics. Focus areas include: 1) Active learning for monitoring optimization, 2) Standardized data for sharing, 3) Privacy-guaranteed data sharing, and 4) Machine learning for text analysis.

Our goal is to deliver healthy foods at low cost to improve nutrition for all Americans. Advances in chemistry reveal thousands of unique food molecules. This project extends ML models to include the gut microbiome and explores cost-health trade-offs in dietary choices.

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.