Assessing Cadmium Stress Resilience in Myrtle Genotypes Using Machine Learning Predictive Models: A Comparative In Vitro Analysis

Abstract

This study investigated the effects of cadmium (Cd) stress on the micropropagation and rooting dynamics of two myrtle (Myrtus communis L.) genotypes with different fruit colors under controlled in vitro conditions. We evaluated the response of these genotypes to varying concentrations of Cd (0, 100, 200, 300, 400, and 500 µM) to determine dose-dependent effects on plantlet multiplication and root formation. Our results demonstrate that the white-fruited (WF) genotype exhibits greater resilience than the black-fruited (BF) genotype across all concentrations, maintaining higher multiplication rates and shoot heights. For instance, the multiplication rate at 100 µM Cd was highest for WF at 6.73, whereas BF showed the lowest rate of 1.94 at 500 µM. Similarly, increasing Cd levels significantly impaired root length and the number of roots for both genotypes, illustrating the detrimental impact of Cd on root system development. Additionally, this study incorporated machine learning (ML) models to predict growth outcomes. The multilayer perceptron (MLP) model, including random forest (RF) and XGBoost, was used to analyze the data. The MLP model performed notably well, demonstrating the potential of advanced computational tools in accurately predicting plant responses to environmental stress. For example, the MLP model accurately predicted shoot height with an R2 value of 0.87 and root length with an R2 of 0.99, indicating high predictive accuracy. Overall, our findings provide significant insights into the genotypic differences in Cd tolerance and the utility of ML models in plant science. These results underscore the importance of developing targeted strategies to enhance plant resilience in contaminated environments.

Keywords

• heavy metal tolerance
• plant stress
• genotypic variation
• toxic metal accumulation
• artificial intelligence in horticulture