Synergistic application of artificial intelligence and response surface methodology for predicting and enhancing in vitro tuber production of potato (Solanum tuberosum).

Journal: PloS one
Published Date:

Abstract

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.

Authors

  • Rajermani Thinakaran
    Faculty of Data Science and Information Technology, INTI International University, Negeri Sembilan, Malaysia.
  • Ecenur Korkmaz
    Öztar Tohumculuk ve Tarım Ürünleri A.Ş. Izmir, Türkiye.
  • Başak Ünver
    Öztar Tohumculuk ve Tarım Ürünleri A.Ş. Izmir, Türkiye.
  • Seyid Amjad Ali
    Department of Information Systems and Technologies, Bilkent University, 06800 Ankara, Turkey.
  • Zeshan Iqbal
    Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
  • Muhammad Aasim
    Department of Plant Protection, Faculty of Agricultural Sciences and Technology, Sivas University of Science and Technology, 58000 Sivas, Turkey. Electronic address: mshazim@gmail.com.