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چکیده
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The important feature of petunia in tissue culture is its unpredictable and genotype-depen- dent callogenesis, posing challenges for efficient regeneration and biotechnology applica- tions. 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 petu- nia 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 deter- mine the relative importance of the four phytohormones. IBA exhibited the highest impor- tance, 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 phytohor- mones for maximizing CFR and CFW. The genetic algorithm identified an optimized combi- nation 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 experi- mental and optimized results obtained through the GA. This study presents a novel approach combining ML, sensitivity analysis, and GA for modeling and predicting callogen- esis in petunia. The findings offer valuable insights into the optimization of phytohormone concentrations, facilitating improved callus formation
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