I-Corps: Transformational Nutrient Curation and Management for Hydroponic Systems

Cuny City University Of New York, New York NY

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project focuses on the development of a data driven approach combined with automation equipment to optimize plant nutrition in real time. The project goal is the development of an efficient and practical machine learning system for nutrient curation and management that may revolutionize commercial farming. Current technologies on the market lack adequate performance specifications and are not cost effective for commercial applications. As a result of changing climate conditions and economic factors, many farms struggle to maintain profitability due to new operational challenges and risks. Worldwide, arable outdoor farmland is dramatically being reduced due to changing climate conditions. The proposed I-Corps project will initially explore the warehouse farm sector that grows produce indoors close to the point where this produce is consumed. Direct beneficiaries of this project include the US agriculture industry, as well as those responsible for national food security. Development of technologies to assist new forms of farming are now required to ensure access to fresh and more nutritionally dense produce in the decades ahead. This I-Corps project is based on the premise that the rational addition of nutrients to hydroponic systems based on real time feedback optimizes plant growth. The core technology includes an automated nutrient dosing and pH balancing system which collects data and acts on environmental data. This system consists of a sensor module monitoring hydroponic nutrient reservoir conditions and pump modules, combined with a cloud based data storage and processing component that adjusts chemical conditions as needed. The driving algorithms utilize analytical techniques that model nutrient factor usage and predicts their effects on cellular gene expression and overall plant health. Together, the hardware and software components derive a variety of plant growth metrics that will form the basis for a larger framework of environmental controls to dictate optimal plant growth. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.