Synergistic application of artificial intelligence and response surface methodology for predicting and enhancing in vitro tuber production of potato (Solanum tuberosum)
Rattachement africain : my, tr, ye. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Synergistic application of artificial intelligence and response surface methodology for predicting and enhancing in vitro tuber production of potato (Solanum tuberosum)
- Date Crossref
- 24/06/2025
- Éditeur
- Public Library of Science (PLoS)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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INTI International University pays non établi dans la noticeUniversité ou école supérieure
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İzmir Kavram Meslek Yüksekokulu pays non établi dans la noticeUniversité ou école supérieure
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Bilkent University Department of of Information Systems and Technologies pays non établi dans la noticeUniversité ou école supérieure
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University of Science and Technology Department of Computer Engineering pays non établi dans la noticeUniversité ou école supérieure
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Sivas State Hospital pays non établi dans la noticeÉtablissement de santé
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Sivas Bilim ve Teknoloji Üniversitesi pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Data Science and Information Technology pays non établi dans la noticeUniversité ou école supérieure
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Öztar Tohumculuk ve Tarım Ürünleri A.Ş. Izmir pays non établi dans la noticeInstitution
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Faculty of Agricultural Sciences and Technologies pays non établi dans la noticeUniversité ou école supérieure
INTI International University, İzmir Kavram Meslek Yüksekokulu et Department of of Information Systems and Technologies — Bilkent University, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.