Precision <i>in vitro</i> propagation by integrating response surface methodology and machine learning for <i>Glossostigma elatinoides</i> (Benth) Hook. F

Optimization of in vitro organogenesis of Glossostigma elatinoides (Benth) Hook. f. was targeted in this study. The experiment was designed with the help of design of experiment (DOE) of response surface methodology (RSM) model. Murashige and Skoog (MS) basal salt, sucrose, and agar were used as input factors and a total of 15 runs were used for optimization. Results were analyzed by ANOVA and response surface regression analysis (RSRA) followed by prediction and validation via different machine learning (ML) models. Results of ANOVA revealed the impact of different combinations on output parameters. Results of RSRA illustrated the relationship between input and output parameters. Pareto chart analysis showed the significant impact of MS on clump diameter, fresh wt., and dry wt. Normal plot analysis illustrated the positive impact of MS on all output parameters and increased proportionally with MS concentration. Results of heatmap and network analysis also demonstrated the significance of MS on all output parameters. Comparison of ML models depicted the better performance of multilayer perceptron (MLP) model for rooting (R2 = 0.957), fresh wt (R2 = 0.806), and dry wt (R2 = 0.812). Conversely, the support vector regression (SVR) model demonstrated superior prediction for clump diameter (R2 = 0.809). Among the tested models, the SVR model showed the weakest performance, aside from clump diameter, while LightGBM achieved scores close to those of the RF and MLP models across all metrics. The findings clearly indicate that the adopted protocol is well-suited for the effective commercial propagation of the aquatic G. elatinoides plant.