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Insights into Drought Tolerance of Tetraploid Wheat Genotypes in the Germination Stage Using Machine Learning Algorithms

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dc.abstract.enThroughout germination, which represents the initial and crucial phase of the wheat life cycle, the plant is notably susceptible to the adverse effects of drought. The identification and selection of genotypes exhibiting heightened drought tolerance stand as pivotal strategies aimed at mitigating these effects. For the stated objective, this study sought to evaluate the responses of distinct wheat genotypes to diverse levels of drought stress encountered during the germination stage. The induction of drought stress was achieved using polyethylene glycol at varying concentrations, and the assessment was conducted through the application of multivariate analysis and machine learning algorithms. Statistical significance (p < 0.01) was observed in the differences among genotypes, stress levels, and their interaction. The ranking of genotypes based on tolerance indicators was evident through a principal component analysis and biplot graphs utilizing germination traits and stress tolerance indices. The drought responses of wheat genotypes were modeled using germination data. Predictions were then generated using four distinct machine learning techniques. An evaluation based on R-square, mean square error, and mean absolute deviation metrics indicated the superior performance of the elastic-net model in estimating germination speed, germination power, and water absorption capacity. Additionally, in assessing the criterion metrics, it was determined that the Gaussian processes classifier exhibited a better performance in estimating root length, while the extreme gradient boosting model demonstrated superior performance in estimating shoot length, fresh weight, and dry weight. The study’s findings underscore that drought tolerance, susceptibility levels, and parameter estimation for durum wheat and similar plants can be reliably and efficiently determined through the applied methods and analyses, offering a fast and cost-effective approach.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorBenlioğlu, Berk
dc.contributor.authorDemirel, Fatih
dc.contributor.authorTürkoğlu, Aras
dc.contributor.authorHaliloğlu, Kamil
dc.contributor.authorÖzaktan, Hamdi
dc.contributor.authorKujawa, Sebastian
dc.contributor.authorPiekutowska, Magdalena
dc.contributor.authorWojciechowski, Tomasz
dc.contributor.authorNiedbała, Gniewko
dc.date.access2025-04-11
dc.date.accessioned2025-06-26T10:25:30Z
dc.date.available2025-06-26T10:25:30Z
dc.date.copyright2024-01-27
dc.date.issued2024
dc.description.abstract<jats:p>Throughout germination, which represents the initial and crucial phase of the wheat life cycle, the plant is notably susceptible to the adverse effects of drought. The identification and selection of genotypes exhibiting heightened drought tolerance stand as pivotal strategies aimed at mitigating these effects. For the stated objective, this study sought to evaluate the responses of distinct wheat genotypes to diverse levels of drought stress encountered during the germination stage. The induction of drought stress was achieved using polyethylene glycol at varying concentrations, and the assessment was conducted through the application of multivariate analysis and machine learning algorithms. Statistical significance (p &lt; 0.01) was observed in the differences among genotypes, stress levels, and their interaction. The ranking of genotypes based on tolerance indicators was evident through a principal component analysis and biplot graphs utilizing germination traits and stress tolerance indices. The drought responses of wheat genotypes were modeled using germination data. Predictions were then generated using four distinct machine learning techniques. An evaluation based on R-square, mean square error, and mean absolute deviation metrics indicated the superior performance of the elastic-net model in estimating germination speed, germination power, and water absorption capacity. Additionally, in assessing the criterion metrics, it was determined that the Gaussian processes classifier exhibited a better performance in estimating root length, while the extreme gradient boosting model demonstrated superior performance in estimating shoot length, fresh weight, and dry weight. The study’s findings underscore that drought tolerance, susceptibility levels, and parameter estimation for durum wheat and similar plants can be reliably and efficiently determined through the applied methods and analyses, offering a fast and cost-effective approach.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,6
dc.description.number2
dc.description.points100
dc.description.versionfinal_published
dc.description.volume14
dc.identifier.doi10.3390/agriculture14020206
dc.identifier.issn2077-0472
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/2895
dc.identifier.weblinkhttps://www.mdpi.com/2077-0472/14/2/206
dc.languageen
dc.relation.ispartofAgriculture (Switzerland)
dc.relation.pagesart. 206
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.entetraploid wheat
dc.subject.endrought stress
dc.subject.engermination
dc.subject.enstress tolerance
dc.subject.enmodeling
dc.titleInsights into Drought Tolerance of Tetraploid Wheat Genotypes in the Germination Stage Using Machine Learning Algorithms
dc.title.volumeSpecial Issue Digital Innovations in Agriculture - Series II
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue2
oaire.citation.volume14