优化茶树的体外繁殖:机器学习模型的比较分析

Taner Bozkurt, S. Inan, İ. Dündar, Musab A. Isak, Ö. Şimşek
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摘要

在这项研究中,我们利用机器学习方法改进了山茶的体外繁殖技术,以确定不同的种植和生根条件对关键生长指标的影响。这是通过应用随机森林(RF)、XGBoost 和多层感知器(MLP)模型来剖析微繁殖和生根过程的复杂性来实现的。研究揭示了不同培养基条件下生长指标的显著差异,强调了培养基成分对植物发育的深远影响。采用方差分析法进行的细致统计分析凸显了生长指标在统计学上的显著差异,表明了培养基成分在优化生长条件中的关键作用。在方法上,该研究利用了 2-3 年生茶树的外植体,这些外植体在微繁殖和生根阶段被引入两种不同的培养基之前都经过了灭菌处理。研究人员进行了统计分析,以评估不同培养基之间的生长结果差异,同时采用机器学习模型,根据各种生长调节剂预测微繁殖和生根的功效。这种方法利用 R2、RMSE 和 MAE 等指标,对模型在不同条件下模拟植物生长的性能进行了全面评估。这项研究的结果极大地促进了对茶树微繁殖的理解,凸显了机器学习模型在农业优化中的实用性。这项研究有助于改进茶树的微繁殖策略,体现了将机器学习融入植物科学的变革潜力,为改进农业和园艺实践铺平了道路。这种跨学科方法为优化体外繁殖过程提供了新的视角,为植物组织培养和生物技术做出了重大贡献。
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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 Camellia sinensis 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 perceptron (MLP) models to dissect the complexities of micropropagation and rooting processes. The research unveiled significant disparities in growth metrics under varying media conditions, underscoring the profound impact of media composition on plant development. The meticulous statistical analysis, employing ANOVA, highlighted statistically significant differences in growth metrics, indicating the critical role of media composition in optimizing growth conditions. Methodologically, the study utilized explants from 2–3-year-old tea plants, which underwent sterilization before being introduced to two distinct culture media for their micropropagation and rooting phases. Statistical analyses were conducted to evaluate the differences in growth outcomes between media, while machine learning models were employed to predict the efficacy of micropropagation and rooting based on various growth regulators. This approach allowed for a comprehensive evaluation of the model’s performance in simulating plant growth under different conditions, leveraging metrics like R2, RMSE, and MAE. The findings from this study significantly advance the understanding of tea plant micropropagation, highlighting the utility of machine learning models in agricultural optimization. This research contributes to enhancing micropropagation strategies for the tea plant and exemplifies the transformative potential of integrating machine learning into plant science, paving the way for improved agricultural and horticultural practices. This interdisciplinary approach offers a novel perspective on optimizing in vitro propagation processes, contributing substantially to plant tissue culture and biotechnology.
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