IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i8p1622-d1219213.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/8/1622/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/8/1622/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
    2. 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.
    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. Sascha O. Becker, Sascha O & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," The Warwick Economics Research Paper Series (TWERPS) 1478, University of Warwick, Department of Economics.
    2. Deac Dan Stelian & Schebesch Klaus Bruno, 2018. "Market Forecasts and Client Behavioral Data: Towards Finding Adequate Model Complexity," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 28(3), pages 50-75, September.
    3. Wang, Feipeng & Wong, Wing-Keung & Wang, Zheng & Albasher, Gadah & Alsultan, Nouf & Fatemah, Ambreen, 2023. "Emerging pathways to sustainable economic development: An interdisciplinary exploration of resource efficiency, technological innovation, and ecosystem resilience in resource-rich regions," Resources Policy, Elsevier, vol. 85(PA).
    4. Ivan Miguel Pires & Faisal Hussain & Nuno M. Garcia & Petre Lameski & Eftim Zdravevski, 2020. "Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification," Future Internet, MDPI, vol. 12(11), pages 1-14, November.
    5. Sascha O. Becker & Hans-Joachim Voth, 2023. "From the Death of God to the Rise of Hitler," CESifo Working Paper Series 10730, CESifo.
    6. Ahmet Faruk Aysan & Bekir Sait Ciftler & Ibrahim Musa Unal, 2024. "Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking," JRFM, MDPI, vol. 17(3), pages 1-19, March.
    7. Fieberg, Christian & Günther, Steffen & Poddig, Thorsten & Zaremba, Adam, 2024. "Non-standard errors in the cryptocurrency world," International Review of Financial Analysis, Elsevier, vol. 92(C).
    8. Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    9. Özer Depren & Mustafa Tevfik Kartal & Serpil Kılıç Depren, 2021. "Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.
    10. Tomasz Rymarczyk & Konrad Niderla & Edward Kozłowski & Krzysztof Król & Joanna Maria Wyrwisz & Sylwia Skrzypek-Ahmed & Piotr Gołąbek, 2021. "Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control," Energies, MDPI, vol. 14(23), pages 1-21, December.
    11. Jialing Zhang & Zhanxu Chen & An Wang & Zhenzhang Li & Wei Wan, 2023. "Intelligent Personalized Lighting Control System for Residents," Sustainability, MDPI, vol. 15(21), pages 1-12, October.
    12. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    13. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    14. Junlong Zhang & Youbin He & Yuan Zhang & Weifeng Li & Junjie Zhang, 2022. "Well-Logging-Based Lithology Classification Using Machine Learning Methods for High-Quality Reservoir Identification: A Case Study of Baikouquan Formation in Mahu Area of Junggar Basin, NW China," Energies, MDPI, vol. 15(10), pages 1-15, May.
    15. Forbes, Kevin F., 2023. "Demand for grid-supplied electricity in the presence of distributed solar energy resources: Evidence from New York City," Utilities Policy, Elsevier, vol. 80(C).
    16. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
    17. Virginia Negri & Alessandro Mingotti & Roberto Tinarelli & Lorenzo Peretto, 2023. "Comparison of Algorithms for the AI-Based Fault Diagnostic of Cable Joints in MV Networks," Energies, MDPI, vol. 16(1), pages 1-20, January.
    18. Fabrizio De Caro & Amedeo Andreotti & Rodolfo Araneo & Massimo Panella & Antonello Rosato & Alfredo Vaccaro & Domenico Villacci, 2020. "A Review of the Enabling Methodologies for Knowledge Discovery from Smart Grids Data," Energies, MDPI, vol. 13(24), pages 1-25, December.
    19. Hillebrecht, Michael & Klonner, Stefan & Pacere, Noraogo A., 2020. "Dynamic Properties of Poverty Targeting," Working Papers 0696, University of Heidelberg, Department of Economics.
    20. Ivan Brandić & Alan Antonović & Lato Pezo & Božidar Matin & Tajana Krička & Vanja Jurišić & Karlo Špelić & Mislav Kontek & Juraj Kukuruzović & Mateja Grubor & Ana Matin, 2023. "Energy Potentials of Agricultural Biomass and the Possibility of Modelling Using RFR and SVM Models," Energies, MDPI, vol. 16(2), pages 1-10, January.

    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:gam:jagris:v:13:y:2023:i:8:p:1622-:d:1219213. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.