Enhancing salt stress tolerance in sugar beet via TiO2 nanoparticles: a machine learning approach

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dc.contributor.authorEngin, Mustafa Alptekin
dc.contributor.authorSefaoglu, Firat
dc.contributor.authorGul, Volkan
dc.contributor.authorAras, Selim
dc.contributor.authorKaynar, Dilara
dc.date.accessioned2026-09-01T15:52:37Z
dc.date.available2026-09-01T15:52:37Z
dc.date.issued2026
dc.departmentBayburt Üniversitesi
dc.description.abstractIn this study, the biostimulant potential of titanium dioxide (TiO2) nanoparticles was investigated to enhance sugar beet (Beta vulgaris L.) seed response to salt stress. Controlled applications were conducted at different NaCl (0-200 mM) and TiO2 (0-1,800 ppm) levels, and eight key morphological and physiological parameters were evaluated. In the first stage, the individual and interactive effects of the factors were examined using a two-way analysis of variance. Then, various machine learning-based regression models, primarily the Gradient Boosting algorithm, were used to numerically model plant responses. Based on the high-accuracy models, optimal application combinations were determined for each parameter and visualized using graphical surface analyses. Additionally, the most suitable overall TiO2 dosage for each salt level was calculated using a multi-criteria scoring system that assigned equal weight to all parameters. The results revealed that TiO2 exerts a regulatory and protective effect on plants against salt stress, with this effect varying with both dosage level and environmental stress severity. The study demonstrates that nanotechnological initiatives can be integrated into data-driven agricultural strategies to optimize stress management.
dc.identifier.doi10.1080/15226514.2026.2711100
dc.identifier.issn1522-6514
dc.identifier.issn1549-7879
dc.identifier.orcid0000-0002-8946-3217
dc.identifier.pmid42545040
dc.identifier.scopus2-s2.0-105046708371
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1080/15226514.2026.2711100
dc.identifier.urihttps://hdl.handle.net/20.500.12403/8505
dc.identifier.wosWOS:001840976600001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofInternational Journal of Phytoremediation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260820
dc.subjectMachine Learning
dc.subjectRegression
dc.subjectSalt Stress
dc.subjectSugar Beet
dc.subjectTio2 Nanoparticles
dc.titleEnhancing salt stress tolerance in sugar beet via TiO2 nanoparticles: a machine learning approach
dc.typeArticle

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