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

Author:

Shasteen KC1,Kacira Murat1ORCID

Affiliation:

1. Biosystems Engineering Department, University of Arizona, Tucson, AZ 85721, USA

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 (CO2) more efficiently later in their development, producing around 22% more biomass during high CO2 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 CO2 concentrations, justifying its use as a feedback metric.

Funder

USDA-SCRI

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference25 articles.

1. Despommier, D. (2010). The Vertical Farm: Feeding the World in the 21st Century, Picador.

2. Future food-production systems: Vertical farming and controlled-environment agriculture;Benke;Sustain. Sci. Pract. Policy,2017

3. Resource use efficiency of indoor lettuce (Lactuca sativa L.) cultivation as affected by red:blue ratio provided by LED lighting;Pennisi;Sci. Rep.,2019

4. Both, A.J., Mears, D.R., Manning, T.O., Reiss, E., and Ling, P.P. (2007, January 17–20). Evaluating energy savings strategies using heat pumps and energy storage for greenhouses. Proceedings of the 2007 ASABE Annual International Meeting, Minneapolis, MN, USA.

5. Modeling resource consumption and carbon emissions associated with lettuce production in plant factories;Eaton;J. Clean. Prod.,2023

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