IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p7812-d1143540.html
   My bibliography  Save this article

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

Author

Listed:
  • KC Shasteen

    (Biosystems Engineering Department, University of Arizona, Tucson, AZ 85721, USA)

  • Murat Kacira

    (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 (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.

Suggested Citation

  • KC Shasteen & Murat Kacira, 2023. "Predictive Modeling and Computer Vision-Based Decision Support to Optimize Resource Use in Vertical Farms," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7812-:d:1143540
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/7812/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/10/7812/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Van Henten, E. J., 1994. "Validation of a dynamic lettuce growth model for greenhouse climate control," Agricultural Systems, Elsevier, vol. 45(1), pages 55-72.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Graamans, Luuk & Baeza, Esteban & van den Dobbelsteen, Andy & Tsafaras, Ilias & Stanghellini, Cecilia, 2018. "Plant factories versus greenhouses: Comparison of resource use efficiency," Agricultural Systems, Elsevier, vol. 160(C), pages 31-43.
    2. Graamans, Luuk & Tenpierik, Martin & van den Dobbelsteen, Andy & Stanghellini, Cecilia, 2020. "Plant factories: Reducing energy demand at high internal heat loads through façade design," Applied Energy, Elsevier, vol. 262(C).
    3. Sungyul Chang & Unseok Lee & Min Jeong Hong & Yeong Deuk Jo & Jin-Baek Kim, 2021. "Lettuce Growth Pattern Analysis Using U-Net Pre-Trained with Arabidopsis," Agriculture, MDPI, vol. 11(9), pages 1-8, September.
    4. Jacqualine A Thomas & Mikhail Vasiliev & Mohammad Nur-E-Alam & Kamal Alameh, 2020. "Increasing the Yield of Lactuca sativa , L. in Glass Greenhouses through Illumination Spectral Filtering and Development of an Optical Thin Film Filter," Sustainability, MDPI, vol. 12(9), pages 1-17, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7812-:d:1143540. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.