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A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12745))

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Abstract

Optimization of water consumption in agriculture is necessary to preserve freshwater reserves and reduce the environment’s burden. Finding optimal irrigation and water resources for crops is necessary to increase the efficiency of water usage. Many optimization approaches maximize crop yield or profit but do not consider the impact on the environment. We propose a machine learning approach based on the crop simulation model WOFOST to assess the crop yield and water use efficiency. In our research, we use weather history to evaluate various weather scenarios. The application of multi-criteria optimization based on the non-dominated sorting genetic algorithm-II (NSGA-II) allows users to find the dates and volume of water for irrigation, maximizing the yield and reducing the total water consumption. In the study case, we compared the effectiveness of NSGA-II with Monte Carlo search and a real farmer’s strategy. We showed a decrease in water consumption simultaneously with increased sugar-beet yield using the NSGA-II algorithm. Our approach yielded a higher potato crop than a farmer with a similar level of water consumption. The NSGA-II algorithm received an increase in yield for potato crops, but water use efficiency remained at the farmer’s level. NSGA-II used water resources more efficiently than the Monte Carlo search and reduced water losses to the lower soil horizons.

M. Gasanov and D. Merkulov—These authors contributed equally to the work.

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Notes

  1. 1.

    https://github.com/ajwdewit/pcse.

  2. 2.

    https://github.com/EDSEL-skoltech/multi_objective_irrigation

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Acknowledgement

This work is supported by the Russian Science Foundation (project No. 20-74-10102). Vectors used in plots and graphical abstract from vecteezy project.

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Authors and Affiliations

  1. Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, 121205, Moscow, Russia

    Mikhail Gasanov, Daniil Merkulov, Artyom Nikitin, Nikita Stasenko, Anna Petrovskaia, Mariia Pukalchik & Ivan Oseledets

  2. Marchuk Institute of Numerical Mathematics, RAS, Gubkin st 8, 119333, Moscow, Russia

    Sergey Matveev & Ivan Oseledets

  3. Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Leninskiye Gory 1, 119991, Moscow, Russia

    Sergey Matveev

Authors
  1. Mikhail Gasanov
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Correspondence to Mikhail Gasanov .

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Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Gasanov, M. et al. (2021). A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-77970-2_7

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