Machine learning and artificial neural networks-based approach to model and optimize ethyl methanesulfonate and sodium azide induced in vitro regeneration and morphogenic traits of water hyssops (Bacopa monnieri L.)

Abstract

Application of chemical mutagens is used for artificially induced in vitro mutation to develop new cultivars with elite characteristics. However, the optimization of selecting proper mutagen, its concentration, and exposure time is of utmost importance, especially for plants containing noteworthy secondary metabolites. In this study, the effect of sodium azide (NaN3) and ethyl methanesulfonate (EMS) in different concentrations (0.025, 0.05, 0.1, and 0.2 mg l−1), and treatment time (30, 60, and 120 min) was investigated on Bacopa monnieri; an important medicinal plant. The maximum shoot counts (57.0) were achieved from the combination of 0.10 mg l−1 EMS × 60 min. Whereas, maximum shoot length (4.07 cm), node numbers (4.97) and leaf numbers (12,23) were achieved from the combination of 0.20 mg l−1 EMS × 120 min, respectively. Combination of 0.025 mg l−1 NaN3 × 120 mg/l yielded maximum shoot counts (52.30), shoot length (3.23 cm), node numbers (6.07) and leaf numbers (12.13). The trained model to predict the outputs were designed and calibrated with machine learning (ML) algorithms. Support Vector Classifier (SVC), Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), Random Forest (RF) models, and Multilayer Perceptron (MLP) neural network algorithms were used to discover the best models and their hyperparameters. The RF model gave exceptional results in the prediction of the outputs. F1 scores of the RF were acquired in the range of 0.98–1.00 for different outputs. The other models’ F1 scores varied in the range of 0.65 and 0.85. The present work opens the new era of applying ML and artificial neural network (ANN) models in plant tissue culture with the possibility of application for other economic crops.