Artificial neural network˗multi˗objective genetic algorithm hybrid and Simulink simulation: Powerful candidates for designing synergistic biostimulants formulation to promote plant resilience against salt and drought stress

• Plant biostimulants (PBs) can mitigate salinity and drought effects upon germination. • Artificial neural network (ANN) and Simulink simulation were developed to predict PBs effect. • An optimized synergetic PB formulation was defined using a multi˗objective genetic algorithm. • AI tools will open wide perspectives for developing PBs complex matrix. Plant biostimulants (PBs) are substances or microorganisms that can improve crop physiology and agronomic performance. The development of effective product formulation and specific target PBs is a challenging task. AI technology could successfully mediate the acceleration of new compound discovery. In vitro germination of two Solanaceae species was conducted under salt (150 mM NaCl) and drought (22.37% PEG-6000) stresses with three commercial PBs (Alga max® ‘Ag’, Amino 80® ‘A80’, and Stimule 7® ‘S7’) to assess their stress˗alleviating effect. Afterward, two approaches, artificial neural network (ANN) and Simulink, were developed to predict the PBs' impact on the time taken for cumulative germination to reach 50% of its maximum (t 50 ) and the germination percentage (Gmax). A positive response was recorded in pepper with Ag, vs. an inhibitory effect with A80 and S7. For tomato, there is mainly a significant decrease in t 50 . An accurate prediction was achieved with both models. k-fold cross-validation proved the good generalization of the ANN model with 0.03 MSE and 0.91 R 2 . Sensitivity analysis highlighted the effect of PBs in Gmax, also species and stress type in t 50 . Ultimately, ANN was linked to multi˗objective genetic algorithm to propose an optimized formulation of 4.992 g/L Ag, 1.274 g/L A80, and 0.303 % S7, validated by 97.4% and 105 h for pepper and 96.3% and 65.2h for tomato. Therefore, plant physiology, computational processing, and evolutionary algorithms are highly endorsed as new intelligent tools in biostimulant research and industry to pilot effective product development.