Optimizing the In Vitro Propagation of Tea Plants: A Comparative Analysis of Machine Learning Models

Optimizing the In Vitro Propagation of Tea Plants: A Comparative Analysis of Machine Learning Models

In this study, we refine in vitro propagation techniques for <i>Camellia sinensis</i> using a machine learning approach to ascertain the influence of different shooting and rooting conditions on key growth metrics. This was achieved by applying random forest (RF), XGBoost, and multilayer...

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Bibliographic Details
Main Authors: Taner Bozkurt, Sezen İnan, İjlal Dündar, Musab A. Isak, Özhan Şimşek
Format: Article
Language:English
Published: MDPI AG 2024-07-01
Series:Horticulturae
Subjects:
tea plant micropropagation
machine learning in horticulture
in vitro propagation optimization
growth parameter prediction
random forest and MLP models
Online Access:https://www.mdpi.com/2311-7524/10/7/721

Internet

https://www.mdpi.com/2311-7524/10/7/721

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