Lead Researcher: Joshua Craver
Contact: Joshua Craver
Industry Partners: Vertical Irrigation LLC
Abstract:
Vertical indoor farming is capturing a larger market share of food production, yet problems in this nascent field remain. Indoor farming benefits from reduced water consumption by employing closed system techniques such as hydroponics and aquaponics, however, optimizations can be further improved with systems automation and waste reduction. Reducing operation expenses is achievable by deploying passive and reusable sensors that acquire actionable information that can then be used for machine learning optimizations. Our state-of-the-art opto-electronic and photonic sensors monitor environmental conditions, and, when paired with linear regression-based analysis techniques can correlate the signal outputs to product yield and operation efficiencies. The goal of this proposal is to study the efficacy of this “big-data” approach to agriculture by comparing profit margins of vertical farming systems with and without the use of our custom remote monitoring system over multiple grow cycles. We expect to see a simplified workflow by automating plant and systems monitoring, which reduces labor cost, and we expect a more uniform and high-volume product yield, both of which boost profit margins.