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 (R ² = 0.957), fresh wt (R ² = 0.806), and dry wt (R ² = 0.812). Conversely, the support vector regression (SVR) model demonstrated superior prediction for clump diameter (R ² = 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.

Nano-phytoremediation is a novel green technique to remove toxic pollutants from the environment. In vitro regenerated Ceratophyllum demersum (L.) plants were exposed to different concentrations of chromium (Cr) and exposure times in the presence of titania nanoparticles (TiO₂NPs). Response surface methodology was used for multiple statistical analyses like regression analysis and optimizing plots. The supplementation of NPs significantly impacted Cr in water and Cr removal (%), whereas NP × exposure time (T) statistically regulated all output parameters. The Firefly metaheuristic algorithm and the random forest (Firefly-RF) machine learning algorithms were coalesced to optimize hyperparameters, aiming to achieve the highest level of accuracy in predicted models. The R² scores were recorded as 0.956 for Cr in water, 0.987 for Cr in the plant, 0.992 for bioconcentration factor (BCF), and 0.957 for Cr removal through the Firefly-RF model. The findings illustrated superior prediction performance from the random forest models when compared to the response surface methodology. The conclusion is drawn that metal-based nanoparticles (NPs) can effectively be utilized for nano-phytoremediation of heavy metals. This study has uncovered a promising outlook for the utilization of nanoparticles in nano-phytoremediation. This study is expected to pave the way for future research on the topic, facilitating further exploration of various nanoparticles and a thorough evaluation of their potential in aquatic ecosystems. Graphical Abstract