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

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 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.