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. This team studied the efficacy of a “big-data” approach that compared profit margins of vertical farming systems over multiple grow cycles with and without using our custom remote monitoring system. We saw a simplified workflow by automating plant and systems monitoring, which reduces labor cost, and more uniform and high-volume product yield, both of which boost profit margins.

Project Background: Vertical irrigation technologies can reduce some of the water and energy used for crop production and optimizing lighting in these irrigation systems may make it possible to significantly reduce other inputs. This project focused on developing and testing computer vision techniques to measure real time growth of microgreens, and determine if they can be used to dynamically and automatically adjust growing conditions.