This is the part B of project 8. The overall goal is to develop digital twin and machine learning algorithms for addressing key aspects of LED driven pod-based hydroponic farming, resource use efficiency, and related plant pathogen challenges, using data and models from the companion project 8 (Part A). We are motivated by recent products which are presented without much analysis or optimization so that their efficacy is unclear. These are essentially self-contained pods, made of 40-foot-long shipping containers filled with cutting-edge, high-tech equipment for hydroponic agriculture.

If successful, this project will have a huge impact on providing predictive/analytical models to the food industry for predicting spread of pathogens and decontamination, realistic simulation of hydroponic systems, quantitative comparison of the performance of these systems with conventional greenhouses and outdoor farming and increasing their widespread usage. The specific focus of the research team on pathogens is timely, considering the ongoing spread of the COVID-19 related pathogens. It is expected that the proposed methods, i.e., digital twins and deep learning, as well as the project deliverables will contribute to revolutionizing the food processing industry.

Learn more about our other indoor farming research projects.