Machine learning-driven modelling and optimization of callus induction and biomass accumulation in Lavandula × intermedia

Abstract

Abstract Callus formation in Lavandula × intermedia varies widely depending on explant type, plant growth regulator composition, and cultivation duration, yet their combined effects remain insufficiently characterized. Here, 57 growth regulator treatments were evaluated using root- and stem-derived explants over a 15-week culture period. Callus induction occurred on most media and was typically initiated within the first three weeks, while biomass accumulation followed a biphasic pattern with a pronounced increase after week six, reaching up to 35 g depending on treatment. A combination of 0.5 mg L⁻¹ 2,4-D and 0.5 mg L⁻¹ kinetin consistently produced the highest biomass. To model system behavior, five statistical and machine learning approaches were applied. XGBoost achieved the highest predictive accuracy on experimental data (R² ≈ 0.94), whereas Random Forest showed the most stable performance across independent validation datasets. Feature importance analysis identified culture duration as the dominant factor influencing biomass, while hormonal composition significantly affected both responses and explant type had only a minor contribution. Multi-objective optimization using NSGA-II revealed multiple high-performing solutions, while induction converged to a single near-optimal condition. These findings demonstrate that integrating experimental data with machine learning enables robust prediction and optimization of callus responses in Lavandula × intermedia .