Chenhui Shao

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.

The research investigates AI-guided ultrasonic cleaning of produce, with a focus on enhancing cleaning effectiveness through shear induced on the produce surface by bubble-enhanced ultrasonic cleaning, spanning a range of amplitudes from oscillating non-condensing gas bubbles to cavitation near an artificial leaf surface designed to mimic real lettuce leaf characteristics.