Francesco Borrelli

Food digestion is crucial for nutrient release and macromolecule breakdown, yet the absence of predictive models hinders health and innovation in the food industry. This project seeks to fill this gap, aiming to develop AI technologies for improved food manufacturing, personalized nutrition, and digital twin models of gut and food digestion.

The project aims to build infrastructure for the rapid scaling of agricultural AI technologies by creating an open-source Python library, AgML, providing access to agricultural-specific machine learning datasets, benchmarks, pre-trained models, workflows, and synthetic data generators, building on successes from the Year 2 AIFS proposal.

The project aims to develop digital twin and machine learning algorithms for optimizing LED-driven pod-based hydroponic farming, with a focus on addressing plant pathogen challenges, offering potential impacts on predicting pathogen spread, decontamination, simulating hydroponic systems, and revolutionizing the food processing industry if successful.