Machine learning models for optimization, validation, and prediction of light emitting diodes with kinetin based basal medium for in vitro regeneration of upland cotton (Gossypium hirsutum L.)
Citation
Özkat, G. Y., Aasim, M., Bakhsh, A., Ali, S. A., & Özcan, S. (2025). Machine learning models for optimization, validation, and prediction of light emitting diodes with kinetin based basal medium for in vitro regeneration of upland cotton (Gossypium hirsutum L.). Journal of Cotton Research, 8(1), 19. https://doi.org/10.1186/s42397-025-00222-4Abstract
Background: Plant tissue culture has emerged as a tool for improving cotton propagation and genetics, but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration. Cotton's recalcitrance is influenced by genotype, explant type, and environmental conditions. To overcome these issues, this study uses different machine learning-based predictive models by employing multiple input factors. Cotyledonary node explants of two commercial cotton cultivars (STN-468 and GSN-12) were isolated from 7–8 days old seedlings, preconditioned with 5, 10, and 20 mg·L−1 kinetin (KIN) for 10 days. Thereafter, explants were postconditioned on full Murashige and Skoog (MS), ½ MS, ¼ MS, and full MS + 0.05 mg·L−1 KIN, cultured in growth room enlightened with red and blue light-emitting diodes (LED) combination. Statistical analysis (analysis of variance, regression analysis) was employed to assess the impact of different treatments on shoot regeneration, with artificial intelligence (AI) models used for confirming the findings. Results: GSN-12 exhibited superior shoot regeneration potential compared with STN-468, with an average of 4.99 shoots per explant versus 3.97. Optimal results were achieved with 5 mg·L−1 KIN preconditioning, ¼ MS postconditioning, and 80% red LED, with maximum of 7.75 shoot count for GSN-12 under these conditions; while STN-468 reached 6.00 shoots under the conditions of 10 mg·L−1 KIN preconditioning, MS with 0.05 mg·L−1 KIN (postconditioning) and 75.0% red LED. Rooting was successfully achieved with naphthalene acetic acid and activated charcoal. Additionally, three different powerful AI-based models, namely, extreme gradient boost (XGBoost), random forest (RF), and the artificial neural network-based multilayer perceptron (MLP) regression models validated the findings. Conclusion: GSN-12 outperformed STN-468 with optimal results from 5 mg·L−1 KIN + ¼ MS + 80% red LED. Application of machine learning-based prediction models to optimize cotton tissue culture protocols for shoot regeneration is helpful to improve cotton regeneration efficiency.
