IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i5d10.1007_s11269-024-03746-7.html
   My bibliography  Save this article

A Machine Learning-Based Probabilistic Approach for Irrigation Scheduling

Author

Listed:
  • Shivendra Srivastava

    (University of Nebraska)

  • Nishant Kumar

    (University of Nebraska)

  • Arindam Malakar

    (University of Nebraska)

  • Sruti Das Choudhury

    (University of Nebraska
    University of Nebraska)

  • Chittaranjan Ray

    (University of Nebraska
    University of Nebraska)

  • Tirthankar Roy

    (University of Nebraska)

Abstract

Accurate prediction of irrigation requirements ensures that water is applied only when necessary, reducing wastage and conserving this precious resource. This study provides a probabilistic framework for determining the irrigation requirements of crops, referred to as the Irrigation Factor (IF). IF was calculated based on three indicators - soil moisture (SM), leaf area index (LAI), and evapotranspiration (ET). Irrigation requirement is determined based on a three-step methodology. First, relevant variables for each indicator are identified using a Random Forest regressor, followed by the development of a Long Short-Term Memory (LSTM) model to predict the three indicators. Second, errors in the simulation are calculated for each indicator by comparing the predicted and actual values in the historical time period, which are then used to calculate the error weights (normalized) of the three indicators for each month to also capture the seasonal variations. Third, we calculate the lower and upper limits by adding the error values (5th and 95th percentiles) to a simulated value. Using these values, we determine the mean, minimum, and maximum levels of irrigation requirement based on the levels’ threshold values. To determine the final levels of irrigation requirement at a daily time scale, we multiply the calculated levels (mean, minimum, and maximum) for each indicator by their respective weights. The outcome derived from the test case indicated that while certain variables exhibited no demand for water, there was a necessity for irrigation in other cases, and vice versa. This holistic approach to irrigation scheduling helps to ensure that plants receive adequate water while minimizing water wastage and promoting sustainability. It is especially valuable for agricultural operations, where optimizing water usage is essential economically and environmentally.

Suggested Citation

  • Shivendra Srivastava & Nishant Kumar & Arindam Malakar & Sruti Das Choudhury & Chittaranjan Ray & Tirthankar Roy, 2024. "A Machine Learning-Based Probabilistic Approach for Irrigation Scheduling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(5), pages 1639-1653, March.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:5:d:10.1007_s11269-024-03746-7
    DOI: 10.1007/s11269-024-03746-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03746-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-024-03746-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sofia O. Lopes & Fernando A. C. C. Fontes & Rui M. S. Pereira & MdR de Pinho & A. Manuela Gonçalves, 2016. "Optimal Control Applied to an Irrigation Planning Problem," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-10, June.
    2. Padilla-Díaz, C.M. & Rodriguez-Dominguez, C.M. & Hernandez-Santana, V. & Perez-Martin, A. & Fernández, J.E., 2016. "Scheduling regulated deficit irrigation in a hedgerow olive orchard from leaf turgor pressure related measurements," Agricultural Water Management, Elsevier, vol. 164(P1), pages 28-37.
    3. Khadijeh Alibabaei & Pedro D. Gaspar & Tânia M. Lima, 2021. "Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling," Energies, MDPI, vol. 14(11), pages 1-21, May.
    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. Padilla-Díaz, C.M. & Rodriguez-Dominguez, C.M. & Hernandez-Santana, V. & Perez-Martin, A. & Fernandes, R.D.M. & Montero, A. & García, J.M. & Fernández, J.E., 2018. "Water status, gas exchange and crop performance in a super high density olive orchard under deficit irrigation scheduled from leaf turgor measurements," Agricultural Water Management, Elsevier, vol. 202(C), pages 241-252.
    2. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdoulghafor & Samir Brahim Belhaouari & Normahira Mamat & Shamsul Faisal Mohd Hussein, 2022. "Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review," Agriculture, MDPI, vol. 12(7), pages 1-35, July.
    3. Fernández, J.E. & Alcon, F. & Diaz-Espejo, A. & Hernandez-Santana, V. & Cuevas, M.V., 2020. "Water use indicators and economic analysis for on-farm irrigation decision: A case study of a super high density olive tree orchard," Agricultural Water Management, Elsevier, vol. 237(C).
    4. Isakwisa Gaddy Tende & Kentaro Aburada & Hisaaki Yamaba & Tetsuro Katayama & Naonobu Okazaki, 2023. "Development and Evaluation of a Deep Learning Based System to Predict District-Level Maize Yields in Tanzania," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    5. Wu, Yinshan & Jiang, Jie & Zhang, Xiufeng & Zhang, Jiayi & Cao, Qiang & Tian, Yongchao & Zhu, Yan & Cao, Weixing & Liu, Xiaojun, 2023. "Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice," Agricultural Water Management, Elsevier, vol. 289(C).
    6. Alcaras, L. Martín Agüero & Rousseaux, M. Cecilia & Searles, Peter S., 2016. "Responses of several soil and plant indicators to post-harvest regulated deficit irrigation in olive trees and their potential for irrigation scheduling," Agricultural Water Management, Elsevier, vol. 171(C), pages 10-20.
    7. Cheng Miao & Jing Xie & Yingdong Li, 2024. "Evaluating the risk and its acceptability of crop yield reduction in Northwest China under the impact of wind hail disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(15), pages 14027-14048, December.
    8. Camoglu, Gokhan & Demirel, Kursad & Kahriman, Fatih & Akcal, Arda & Nar, Hakan & Boran, Ahmet & Eroglu, Ilker & Genc, Levent, 2021. "Discrimination of water stress in pepper using thermography and leaf turgor pressure probe techniques," Agricultural Water Management, Elsevier, vol. 254(C).
    9. Franco E. Calvo & María A. Calahorra & Eduardo R. Trentacoste, 2024. "Impact of El Niño–Southern Oscillation and Mechanical Pruning Strategies on the Productivity, Alternate Bearing, and Vegetative Growth of Olive Hedgerows," Agriculture, MDPI, vol. 14(12), pages 1-14, December.
    10. Alibabaei, Khadijeh & Gaspar, Pedro D. & Assunção, Eduardo & Alirezazadeh, Saeid & Lima, Tânia M., 2022. "Irrigation optimization with a deep reinforcement learning model: Case study on a site in Portugal," Agricultural Water Management, Elsevier, vol. 263(C).
    11. Zare Abyaneh, Hamid & Jovzi, Mehdi & Albaji, Mohammad, 2017. "Effect of regulated deficit irrigation, partial root drying and N-fertilizer levels on sugar beet crop (Beta vulgaris L.)," Agricultural Water Management, Elsevier, vol. 194(C), pages 13-23.
    12. Ahmed, Moiz Uddin & Hussain, Iqbal, 2022. "Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan," Telecommunications Policy, Elsevier, vol. 46(6).
    13. García-Tejero, I.F. & Hernández, A. & Padilla-Díaz, C.M. & Diaz-Espejo, A. & Fernández, J.E, 2017. "Assessing plant water status in a hedgerow olive orchard from thermography at plant level," Agricultural Water Management, Elsevier, vol. 188(C), pages 50-60.
    14. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2022. "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, Elsevier, vol. 196(C).
    15. Guilherme Jesus & Martim L. Aguiar & Pedro D. Gaspar, 2022. "Computational Tool to Support the Decision in the Selection of Alternative and/or Sustainable Refrigerants," Energies, MDPI, vol. 15(22), pages 1-20, November.
    16. Galioto, Francesco & Battilani, Adriano, 2021. "Agro-economic simulation for day by day irrigation scheduling optimisation," Agricultural Water Management, Elsevier, vol. 248(C).
    17. Juan Botero-Valencia & Vanessa García-Pineda & Alejandro Valencia-Arias & Jackeline Valencia & Erick Reyes-Vera & Mateo Mejia-Herrera & Ruber Hernández-García, 2025. "Machine Learning in Sustainable Agriculture: Systematic Review and Research Perspectives," Agriculture, MDPI, vol. 15(4), pages 1-37, February.
    18. Eduardo Assunção & Pedro D. Gaspar & Khadijeh Alibabaei & Maria P. Simões & Hugo Proença & Vasco N. G. J. Soares & João M. L. P. Caldeira, 2022. "Real-Time Image Detection for Edge Devices: A Peach Fruit Detection Application," Future Internet, MDPI, vol. 14(11), pages 1-12, November.
    19. Fernandes, R.D.M. & Egea, G. & Hernandez-Santana, V. & Diaz-Espejo, A. & Fernández, J.E. & Perez-Martin, A. & Cuevas, M.V., 2021. "Response of vegetative and fruit growth to the soil volume wetted by irrigation in a super-high-density olive orchard," Agricultural Water Management, Elsevier, vol. 258(C).
    20. Egea, Gregorio & Fernández, José E. & Alcon, Francisco, 2017. "Financial assessment of adopting irrigation technology for plant-based regulated deficit irrigation scheduling in super high-density olive orchards," Agricultural Water Management, Elsevier, vol. 187(C), pages 47-56.

    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:spr:waterr:v:38:y:2024:i:5:d:10.1007_s11269-024-03746-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.