Thinakaran Rajermani, Korkmaz Ecenur, Ünver Başak, Ali Seyid Amjad, Iqbal Zeshan, Aasim Muhammad
Faculty of Data Science and Information Technology, INTI International University, Negeri Sembilan, Malaysia.
Öztar Tohumculuk ve Tarım Ürünleri A.Ş. Izmir, Türkiye.
PLoS One. 2025 Jun 24;20(6):e0325754. doi: 10.1371/journal.pone.0325754. eCollection 2025.
In vitro regeneration of potato tubers is highly significant in modern agriculture as it offers efficient propagation, genetic enhancement, and pathogen-free seed production. This study aimed to optimize in vitro tuberization by manipulating key variables, including cultivar, sucrose concentration, and cytokinin-auxin interactions. Results were analyzed by response surface regression analysis (RSRA) of Response Surface Methodology (RSM), followed by data validation and prediction with machine learning (ML) models. Fontana cultivar exhibited superior tuberization performance, with a maximum tuberization rate of 75.6% from Murashige and Skoog (MS) medium supplemented with 90 g/L sucrose, 2 mg/L BAP, and 1 mg/L Indole-3-butyric acid (IBA). Sucrose concentration was the most significant factor for all growth parameters, particularly tuber size and weight. RSRA analysis confirmed the significance of the linear effects of sucrose and BAP on tuberization, while auxins primarily regulated tuber size and weight. Pareto chart analysis highlighted sucrose as the most influential variable for both cultivars. Heatmap and network plot analyses further illustrated strong positive correlations between sucrose, BAP, and tuber formation, whereas auxins exhibited comparatively weaker effects. Results analyzed by Machine learning (ML) models revealed maximum predictive accuracy for tuberization by Random Forest (RF) model with an R2 of 0.379. However, all other models also faced challenges with high error rates, indicating the need for improved feature engineering. This study concludes that optimizing sucrose concentration and BAP levels, combined with selective auxin application, and integration of RSM and AI presents a promising strategy for optimization and potentially improving large-scale commercial production of disease-free potato tubers.
马铃薯块茎的离体再生在现代农业中具有重要意义，因为它能实现高效繁殖、遗传改良以及无病原体种子生产。本研究旨在通过控制关键变量（包括品种、蔗糖浓度和细胞分裂素 - 生长素相互作用）来优化离体块茎形成。通过响应面法（RSM）的响应面回归分析（RSRA）对结果进行分析，随后使用机器学习（ML）模型进行数据验证和预测。丰塔纳品种表现出优异的块茎形成性能，在添加90 g/L蔗糖、2 mg/L 6 - 苄基腺嘌呤（BAP）和1 mg/L吲哚 - 3 - 丁酸（IBA）的Murashige和Skoog（MS）培养基上，最大块茎形成率为75.6%。蔗糖浓度是所有生长参数中最显著的因素，尤其是块茎大小和重量。RSRA分析证实了蔗糖和BAP对块茎形成的线性效应的重要性，而生长素主要调节块茎大小和重量。帕累托图分析突出了蔗糖是两个品种中最具影响力的变量。热图和网络图分析进一步表明蔗糖、BAP与块茎形成之间存在强正相关，而生长素的影响相对较弱。通过机器学习（ML）模型分析的结果显示，随机森林（RF）模型对块茎形成的预测准确率最高，R²为0.379。然而，所有其他模型也面临高错误率的挑战，这表明需要改进特征工程。本研究得出结论，优化蔗糖浓度和BAP水平，结合选择性应用生长素，以及将RSM和人工智能相结合，为优化和潜在改善无病马铃薯块茎的大规模商业生产提供了一种有前景的策略。
