Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis

Abstract

The important feature of petunia in tissue culture is its unpredictable and genotype-dependent callogenesis, posing challenges for efficient regeneration and biotechnology applications. To address this issue, machine learning (ML) can be considered a powerful tool to analyze callogenesis data, extract key parameters, and predict optimal conditions for petunia callogenesis, facilitating more controlled and productive tissue culture processes. The study aimed to develop a predictive model for callogenesis in petunia using ML algorithms and to optimize the concentrations of phytohormones to enhance callus formation rate (CFR) and callus fresh weight (CFW). The inputs for the model were BAP, KIN, IBA, and NAA, while the outputs were CFR and CFW. Three ML algorithms, namely MLP, RBF, and GRNN, were compared, and the results revealed that GRNN (R2≥83) outperformed MLP and RBF in terms of accuracy. Furthermore, a sensitivity analysis was conducted to determine the relative importance of the four phytohormones. IBA exhibited the highest importance, followed by NAA, BAP, and KIN. Leveraging the superior performance of the GRNN model, a genetic algorithm (GA) was integrated to optimize the concentration of phytohormones for maximizing CFR and CFW. The genetic algorithm identified an optimized combination of phytohormones consisting of 1.31 mg/L BAP, 1.02 mg/L KIN, 1.44 mg/L NAA, and 1.70 mg/L IBA, resulting in 95.83% CFR. To validate the reliability of the predicted results, optimized combinations of phytohormones were tested in a laboratory experiment. The results of the validation experiment indicated no significant difference between the experimental and optimized results obtained through the GA. This study presents a novel approach combining ML, sensitivity analysis, and GA for modeling and predicting callogenesis in petunia. The findings offer valuable insights into the optimization of phytohormone concentrations, facilitating improved callus formation and potential applications in plant tissue culture and genetic engineering.

Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on Strawberry Cultivars

“…This demonstrates a consistent RF efficacy pattern across different plant species and characteristics. Aasim et al [38] and Rezaei et al [41] applied ML models to optimize plant tissue culture protocols. Aasim et al [54] utilized MLP to predict shoot regeneration in common beans, while Rezaei et al [55] employed a genetic algorithm (GA) in conjunction with ML models to optimize phytohormone concentrations in petunia callogenesis.…”

“…Recently, diverse machine learning models have proven effective in accurately forecasting and refining plant tissue culture procedures. These models have been applied in various investigations, including in vitro mutagenesis, micropropagation, regeneration studies, plant system biology, in vitro organogenesis, stress physiology, and salt stress [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Only a few studies have used machine learning models to examine drought stress responses.…”

Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation

“…Similarly, Hesami et al [ 48 ] leveraged ML models to predict off-target activities of sgRNA in Cannabis sativa , highlighting the precision of ML in genome editing applications. Moreover, Rezaei et al [ 44 ] employed ML techniques to enhance tissue culture efficiency in petunia, showcasing the effectiveness of ML in improving callogenesis outcomes. These studies underscore the versatility and effectiveness of ML techniques in optimizing various in vitro culture parameters.…”

“…In contrast, ML algorithms, such as artificial neural networks (ANNs), support vector machines (SVMs), and genetic algorithms (GAs), offer higher accuracy and efficiency. For instance, ML has been successfully applied to optimize sterilization protocols [ 38 , 39 ], seed germination conditions [ 31 , 40 ], and callus induction processes [ 41 – 44 ]. Additionally, ML has optimized somatic embryogenesis [ 45 , 46 ] and haploid production [ 47 , 48 ].…”

Saffron In Vitro Propagation: An Innovative Method by Temporary Immersion System (TIS), Integrated with Machine Learning Analysis

“…Machine learning algorithms emerge as potent and predictive tools for decision-making in the sector of in vitro plant propagation, due to their proficiency in clarifying and defining the complexity of processes that involve a multitude of factors. Currently, these models have been applied in various in vitro culture investigations, including micropropagation, regeneration and in vitro organogenesis, stress physiology, and salt stress [28][29][30][31][32][33].…”