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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationComputational Science – ICCS 2021 - 21st International Conference, Proceedings
EditorsMaciej Paszynski, Dieter Kranzlmüller, Dieter Kranzlmüller, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M.A. Sloot, Peter M.A. Sloot, Peter M.A. Sloot
PublisherSpringer Science and Business Media Deutschland GmbH
Pages75-88
Number of pages14
ISBN (Print)9783030779696
DOIs
Publication statusPublished - 2021
Event21st International Conference on Computational Science, ICCS 2021 - Virtual, Online
Duration: 16 Jun 202118 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12745 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Computational Science, ICCS 2021
CityVirtual, Online
Period16/06/2118/06/21

Keywords

  • Machine learning
  • Multi-objective optimization
  • Sustainable agriculture
  • Water use efficiency

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