Enhancing salt stress tolerance in sugar beet via TiO2 nanoparticles: a machine learning approach
Yazarlar (5)
Dr. Öğr. Üyesi Mustafa Alptekin Engin Bayburt Üniversitesi, Türkiye
Doç. Dr. Fırat SEFAOĞLU Kastamonu Üniversitesi, Türkiye
Prof. Dr. Volkan Gul Bayburt Üniversitesi, Türkiye
Dr. Öğr. Üyesi Selim Aras Ondokuz Mayis Üniversitesi, Türkiye
Dilara Kaynar Kastamonu Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | International Journal of Phytoremediation (Q3) | ||
| Dergi ISSN | 1522-6514 Dergi Bilgileri (2026) | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2026 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 03-08-2026 |
| Cilt / Sayı / Sayfa | 0 / 1 / – | DOI | 10.1080/15226514.2026.2711100 |
| Makale Linki | https://www.tandfonline.com/doi/full/10.1080/15226514.2026.2711100 | ||
| UAK Araştırma Alanları |
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| Özet |
| In 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 … |
| Anahtar Kelimeler |
| Machine learning | regression | salt stress | sugar beet | TiO2 nanoparticles |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Google Scholar | 1 |