Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight.

Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight.

<p>Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight.</p>

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Bibliographic Details
Main Author:	Rajermani Thinakaran (19644730) (author)
Other Authors:	Ecenur Korkmaz (21594438) (author), Başak Ünver (21594441) (author), Seyid Amjad Ali (13301451) (author), Zeshan Iqbal (2836625) (author), Muhammad Aasim (3666946) (author)
Published:	2025
Subjects:	Microbiology Cell Biology Pharmacology Biotechnology Plant Biology Biological Sciences not elsewhere classified solanum tuberosum </ scale commercial production response surface methodology potentially improving large offers efficient propagation network plot analyses manipulating key variables improved feature engineering high error rates free seed production div >< p particularly tuber size vitro tuber production selective auxin application rsra analysis confirmed rsm ), followed maximum tuberization rate optimizing sucrose concentration 2 </ sup tuber formation synergistic application sucrose concentration auxin interactions 2 mg vitro tuberization vitro regeneration study concludes study aimed significant factor random forest promising strategy potato tubers potato (< modern agriculture medium supplemented machine learning linear effects l sucrose l indole influential variable including cultivar iba ). highly significant growth parameters genetic enhancement data validation butyric acid artificial intelligence ai presents 90 g
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