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Machine Learning Approaches for Forecasting the Best Microbial Strains to Alleviate Drought Impact in Agriculture

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  • Tymoteusz Miller

    (Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
    Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland)

  • Grzegorz Mikiciuk

    (Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland)

  • Anna Kisiel

    (Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
    Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland)

  • Małgorzata Mikiciuk

    (Department of Bioengineering, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology, Słowackiego 17, 71-434 Szczecin, Poland)

  • Dominika Paliwoda

    (Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland)

  • Lidia Sas-Paszt

    (Department of Microbiology and Rhizosphere, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland)

  • Danuta Cembrowska-Lech

    (Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland
    Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland)

  • Adrianna Krzemińska

    (Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland)

  • Agnieszka Kozioł

    (Institute of Technology and Life Sciences–National Research Institute, Falenty, Hrabska Avenue 3, 05-090 Raszyn, Poland)

  • Adam Brysiewicz

    (Institute of Technology and Life Sciences–National Research Institute, Falenty, Hrabska Avenue 3, 05-090 Raszyn, Poland)

Abstract

Drought conditions pose significant challenges to sustainable agriculture and food security. Identifying microbial strains that can mitigate drought effects is crucial to enhance crop resilience and productivity. This study presents a comprehensive comparison of several machine learning models, including Random Forest, Decision Tree, XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN), to predict optimal microbial strains for this purpose. Models were assessed on multiple metrics, such as accuracy, standard deviation of results, gains, total computation time, and training time per 1000 rows of data. Notably, the Gradient Boosted Trees model outperformed others in accuracy but required extensive computational resources. This underscores the balance between accuracy and computational efficiency in machine learning applications. Leveraging machine learning for selecting microbial strains signifies a leap beyond traditional methods, offering improved efficiency and efficacy. These insights hold profound implications for agriculture, especially concerning drought mitigation, thus furthering the cause of sustainable agriculture and ensuring food security.

Suggested Citation

  • Tymoteusz Miller & Grzegorz Mikiciuk & Anna Kisiel & Małgorzata Mikiciuk & Dominika Paliwoda & Lidia Sas-Paszt & Danuta Cembrowska-Lech & Adrianna Krzemińska & Agnieszka Kozioł & Adam Brysiewicz, 2023. "Machine Learning Approaches for Forecasting the Best Microbial Strains to Alleviate Drought Impact in Agriculture," Agriculture, MDPI, vol. 13(8), pages 1-16, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1622-:d:1219213
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    References listed on IDEAS

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    1. Bogumił Kamiński & Michał Jakubczyk & Przemysław Szufel, 2018. "A framework for sensitivity analysis of decision trees," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 135-159, March.
    2. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
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