Predictive Modeling and Computer Vision-Based Decision Support to Optimize Resource Use in Vertical Farms

Shasteen Kc and Murat Kacira ()
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Shasteen Kc: Biosystems Engineering Department, University of Arizona, Tucson, AZ 85721, USA
Murat Kacira: Biosystems Engineering Department, University of Arizona, Tucson, AZ 85721, USA

Sustainability, 2023, vol. 15, issue 10, 1-19

Abstract: This study evaluated several decision-support tools that can be used to create a control system capable of taking advantage of fluctuations in the price of resources and improving the energy use efficiency of growing crops in vertical farms. A mechanistic model was updated and calibrated for use in vertical farm environments. This model was also validated under changing environmental conditions with acceptable agreement with empirical observations for the scenarios considered in this study. It was also demonstrated that lettuce plants use carbon dioxide (CO 2 ) more efficiently later in their development, producing around 22% more biomass during high CO 2 conditions during the fourth-week post-transplant than in the first week. A feedback mechanism using top-projected canopy area (TPCA) was evaluated for its ability to correlate with and provide remote biomass estimations. It was shown that for a given set of constant environmental conditions, a scaling factor of 0.21 g cm −2 allowed the TPCA to serve as a rough proxy for biomass in the period prior to canopy closure. The TPCA also was able to show deviation from expected growth under changing CO 2 concentrations, justifying its use as a feedback metric.

Keywords: computer vision; top-projected canopy area; predictive model; lettuce; dynamic control system; vertical farm; controlled environment agriculture; digital twin (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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