PDF(Pubmed)
Abstract：
This study investigates the influence of cadmium (Cd) stress on the micropropagation of Goji Berry (Lycium barbarum L.) across three distinct genotypes (ERU, NQ1, NQ7), employing an array of machine learning (ML) algorithms, including Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Gaussian Process (GP), and Extreme Gradient Boosting (XGBoost). The primary motivation is to elucidate genotype-specific responses to Cd stress, which poses significant challenges to agricultural productivity and food safety due to its toxicity. By analyzing the impacts of varying Cd concentrations on plant growth parameters such as proliferation, shoot and root lengths, and root numbers, we aim to develop predictive models that can optimize plant growth under adverse conditions. The ML models revealed complex relationships between Cd exposure and plant physiological changes, with MLP and RF models showing remarkable prediction accuracy (R2 values up to 0.98). Our findings contribute to understanding plant responses to heavy metal stress and offer practical applications in mitigating such stress in plants, demonstrating the potential of ML approaches in advancing plant tissue culture research and sustainable agricultural practices.
摘要：
这项研究调查了镉（Cd）胁迫对三种不同基因型（ERU，NQ1，NQ7），采用一系列机器学习(ML)算法，包括多层感知器（MLP），支持向量机(SVM)随机森林(RF),高斯过程(GP)和极端梯度提升（XGBoost）。主要动机是阐明基因型对Cd胁迫的特异性反应，由于其毒性，这对农业生产力和食品安全构成了重大挑战。通过分析不同Cd浓度对植物生长参数如增殖的影响,枝条和根的长度，和根号，我们的目标是开发可以在不利条件下优化植物生长的预测模型。ML模型揭示了Cd暴露与植物生理变化之间的复杂关系，MLP和RF模型显示出显着的预测精度（R2值高达0.98）。我们的发现有助于理解植物对重金属胁迫的反应，并为减轻植物的这种胁迫提供实际应用。展示ML方法在推进植物组织培养研究和可持续农业实践方面的潜力。
