Machine Learning-Assisted In Vitro Rooting Optimization in Passiflora caerulea

Abstract

In vitro rooting as one of the most critical steps of micropropagation is affected by various extrinsic (e.g., medium composition, auxins) and intrinsic factors (e.g., species, explant). In Passiflora species, in vitro adventitious rooting is a difficult, complex, and non-linear process. Since in vitro rooting is a multivariable complex biological process, efficient and reliable computational approaches such as machine learning (ML) are required to model, predict, and optimize this non-linear biological process. Therefore, in the current study, a hybrid of generalized regression neural network (GRNN) and genetic algorithm (GA) was employed to predict in vitro rooting responses (rooting percentage, number of roots, and root length) of Passiflora caerulea based on the optimization of the level of auxins (indole-3-acetic acid (IAA), indolebutyric acid (IBA), and 1-naphthaleneacetic acid (NAA)) and the type of explant (microshoots derived from leaf, node, and internode). Based on the results, the GRNN model was accurate in predicting all in vitro rooting responses of P. caerulea (R2 > 0.92) in either training or testing sets. The result of the validation experiment also showed that there was a negligible difference between the predicted-optimized values and the validated results demonstrating the reliability of the developed GRNN-GA model. Generally, the results of the current study showed that GRNN-GA is a reliable and accurate model to predict and optimize in vitro rooting of P. caerulea.