Optimizing Micropropagation of Tanacetum balsamita L.: A Machine Learning Approach to Compare Semisolid Media and Temporary Immersion System

Tanacetum balsamita L. is a medicinal and aromatic plant of high economic value, yet its tissue culture and micropropagation protocols remain poorly developed. This study evaluated and compared two in vitro culture systems, semisolid medium (SS) and Temporary Immersion System (TIS), for enhancing biomass production and growth performance, in terms of relative growth rate (RGR), photosynthetic activity, chlorophyll content, antiradical capacity, and anatomical development. The results demonstrated that the TIS significantly improved RGR, photosynthetic performance, and antiradical activity, and promoted the anatomical development that facilitated greenhouse acclimatization. Machine learning (ML) models, including Multilayer Perceptron (MLP) and Random Forest (RF), were employed to predict morphological and biochemical traits. MLP achieved the highest predictive accuracy (R2 > 0.95) and lowest error metrics for complex, nonlinear traits such as chlorophyll content and antiradical activity, whereas RF excelled in predicting morphological traits with more uniform variance, such as leaf number and shoot length. Overall, this study demonstrates that the TIS provides a high-yield, economically crucial strategy for the micropropagation of T. balsamita, and that integrating ML-based predictive modeling can enhance parameter optimization and phenotyping precision. This combined approach offers a valuable framework for advancing tissue culture research in medicinal and aromatic plants through both production efficiency and data-driven decision-making.