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A Comparative Analysis between Heuristic and Data-Driven Water Management Control for Precision Agriculture Irrigation

Author

Listed:
  • Leonardo D. Garcia

    (Tecnologico de Monterrey, School of Engineering and Science, Monterrey 64849, Mexico)

  • Camilo Lozoya

    (Tecnologico de Monterrey, School of Engineering and Science, Monterrey 64849, Mexico)

  • Antonio Favela-Contreras

    (Tecnologico de Monterrey, School of Engineering and Science, Monterrey 64849, Mexico)

  • Emanuele Giorgi

    (Tecnologico de Monterrey, School of Architecture, Art and Design, Monterrey 64849, Mexico)

Abstract

Modeling and control theory applied to precision agriculture irrigation systems have been essential to reduce water consumption while growing healthy crops. Specifically, implementing closed-loop control irrigation based on soil moisture measurements is an effective approach for obtaining water savings in this resource-intensive activity. To enhance this strategy, the work presented in this paper proposed a new set of water management strategies for the case in which multiple irrigation areas share a single water supply source and compared them with heuristic approaches commonly used by farmers in practice. The proposed water allocation algorithms are based on techniques used in real-time computing, such as dynamic priority and feedback scheduling. Therefore, the multi-area irrigation system is presented as a resource allocation problem with availability constraints, where water consumption represents the main optimization parameter. The obtained results show that the data-driven water allocation strategies preserve the water savings for closed-loop control systems and avoid crop water stress due to the limited access to irrigation water.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11337-:d:1198857
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    References listed on IDEAS

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    1. Panigrahi, B. & Panda, Sudhindra N., 2003. "Field test of a soil water balance simulation model," Agricultural Water Management, Elsevier, vol. 58(3), pages 223-240, February.
    2. Bwambale, Erion & Abagale, Felix K. & Anornu, Geophrey K., 2022. "Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review," Agricultural Water Management, Elsevier, vol. 260(C).
    3. Daniel Simon & Alexandre Seuret & Olivier Sename, 2017. "Real-time control systems: feedback, scheduling and robustness," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(11), pages 2368-2378, August.
    4. M. Safdar Munir & Imran Sarwar Bajwa & M. Asif Naeem & Bushra Ramzan, 2018. "Design and Implementation of an IoT System for Smart Energy Consumption and Smart Irrigation in Tunnel Farming," Energies, MDPI, vol. 11(12), pages 1-18, December.
    5. 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.
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    Cited by:

    1. Wang, Shunke & Chang, Jingjing & Xue, Jie & Sun, Huaiwei & Zeng, Fanjiang & Liu, Lei & Liu, Xin & Li, Xinxin, 2024. "Coupling behavioral economics and water management policies for agricultural land-use planning in basin irrigation districts: Agent-based socio-hydrological modeling and application," Agricultural Water Management, Elsevier, vol. 298(C).

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