IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i1p152-d1312235.html
   My bibliography  Save this article

Computer Model for an Intelligent Adjustment of Weather Conditions Based on Spatial Features for Soil Moisture Estimation

Author

Listed:
  • Luis Pastor Sánchez-Fernández

    (Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, Mexico City 07738, Mexico)

  • Diego Alberto Flores-Carrillo

    (Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, Mexico City 07738, Mexico)

  • Luis Alejandro Sánchez-Pérez

    (Department of Electrical and Computer Engineering, University of Michigan, Dearborn, MI 48126, USA)

Abstract

In this paper, an intelligent weather conditions fuzzy adjustment based on spatial features (IWeCASF) is developed. It is indispensable for our regional soil moisture estimation approach, complementing a point estimation model of soil moisture from the literature. The point estimation model requires the weather conditions at the point where an estimate is made. Therefore, IWeCASF’s aim is to determine these weather conditions. The procedure begins measuring them at only one checkpoint, called the primary checkpoint. The model determines the weather conditions anywhere within a region through image processing algorithms and fuzzy inference systems. The results are compared with the measurement records and with a spatial interpolation method. The performance is similar to or better than interpolation, especially in the rain, where the model developed is more accurate due to the certainty of replication. Additionally, IWeCASF does not require more than one measurement point. Therefore, it is a more appropriate approach to complement the point estimation model for enabling a regional soil moisture estimation.

Suggested Citation

  • Luis Pastor Sánchez-Fernández & Diego Alberto Flores-Carrillo & Luis Alejandro Sánchez-Pérez, 2024. "Computer Model for an Intelligent Adjustment of Weather Conditions Based on Spatial Features for Soil Moisture Estimation," Mathematics, MDPI, vol. 12(1), pages 1-33, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:1:p:152-:d:1312235
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/1/152/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/1/152/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Romero, R. & Muriel, J.L. & García, I. & Muñoz de la Peña, D., 2012. "Research on automatic irrigation control: State of the art and recent results," Agricultural Water Management, Elsevier, vol. 114(C), pages 59-66.
    2. Jaenam Lee, 2022. "Evaluation of Automatic Irrigation System for Rice Cultivation and Sustainable Agriculture Water Management," Sustainability, MDPI, vol. 14(17), pages 1-12, September.
    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. Silva, Marcos Dornelas Freitas Machado e & Calijuri, Maria Lúcia & Sales, Francisco José Ferreira de & Souza, Mauro Henrique Batalha de & Lopes, Lucas Sampaio, 2014. "Integration of technologies and alternative sources of water and energy to promote the sustainability of urban landscapes," Resources, Conservation & Recycling, Elsevier, vol. 91(C), pages 71-81.
    2. Pedro Garcia-Caparros & Juana Isabel Contreras & Rafael Baeza & Maria Luz Segura & Maria Teresa Lao, 2017. "Integral Management of Irrigation Water in Intensive Horticultural Systems of Almería," Sustainability, MDPI, vol. 9(12), pages 1-21, December.
    3. Hrozencik, Aaron & Wallander, Steven & Aillery, Marcel, 2021. "Irrigation Organizations: Water Storage and Delivery Infrastructure," Economic Brief 327232, United States Department of Agriculture, Economic Research Service.
    4. Petit, Julien & García, Sílvia Mas & Molle, Bruno & Bendoula, Ryad & Ait-Mouheb, Nassim, 2022. "Methods for drip irrigation clogging detection, analysis and understanding: State of the art and perspectives," Agricultural Water Management, Elsevier, vol. 272(C).
    5. Chiara Bersani & Ahmed Ouammi & Roberto Sacile & Enrico Zero, 2020. "Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption," Energies, MDPI, vol. 13(14), pages 1-17, July.
    6. Leonardo D. Garcia & Camilo Lozoya & Antonio Favela-Contreras & Emanuele Giorgi, 2023. "A Comparative Analysis between Heuristic and Data-Driven Water Management Control for Precision Agriculture Irrigation," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
    7. Hrozencik, Aaron & Wallander, Steven & Aillery, Marcel, 2021. "Irrigation Organizations: Water Storage and Delivery Infrastructure," USDA Miscellaneous 314931, United States Department of Agriculture.
    8. Yonela Mndela & Naledzani Ndou & Adolph Nyamugama, 2023. "Irrigation Scheduling for Small-Scale Crops Based on Crop Water Content Patterns Derived from UAV Multispectral Imagery," Sustainability, MDPI, vol. 15(15), pages 1-21, August.
    9. Aydin, Boran Ekin & Oude Essink, Gualbert H.P. & Delsman, Joost R. & van de Giesen, Nick & Abraham, Edo, 2022. "Nonlinear model predictive control of salinity and water level in polder networks: Case study of Lissertocht catchment," Agricultural Water Management, Elsevier, vol. 264(C).
    10. Vasilenko, Alexandr & Ulman, Miloš, 2015. "Concept of Horticulture Ambient Intelligence System," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 7(4), pages 1-8, December.

    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:jmathe:v:12:y:2024:i:1:p:152-:d:1312235. 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.