Machine Learning Insights into Ascorbic Acid-Enhanced Germination of Black Cumin (Nigella sativa L.) under Cadmium Stress

Abstract The objective of the present work is to study the impact of seed priming with varying concentrations of ascorbic acid (vitamin C) on the germination process of black cumin ( Nigella sativa ) under cadmium (Cd) stress. As expected, Cd had a great effect on germination rates and seedling growth. However, the application of ascorbic acid during seed priming effectively alleviated Cd stress and significantly increased seed vigor. Primed seeds exhibited markedly elevated final germination percentage, germination index, mean germination time, seedling length, seedling vigor index, and reduced abnormal seedling percentage. Additionally, vitamin priming reduced membrane lipid peroxidation, in treated seeds. Moreover, seed priming elicited a considerable increase in peroxidase and catalase activity, thus mitigating stress effects and augmenting seed vitality. Our experimental data allowed us to establish 100–150 mg/L as the optimal concentration range for ascorbic acid in seed priming of black cumin. These insights were further corroborated through modeling techniques based on supervised machine learning. Notably, XGBoost emerged as a proficient tool for predicting final germination percentage, mean germination time, seedling vigor index, abnormal seedling percentage, and peroxidase activity, while SVR demonstrated aptitude in forecasting catalase activity and germination index. The Gaussian method exhibited superior performance in predicting malondialdehyde content. These comprehensive findings substantiate the premise that vitamin priming with ascorbic acid serves as a promising strategy to ameliorate germination outcomes under Cd-induced stress conditions.