Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation.

Autor: Isak MA; Department of Agricultural Science and Technology, Graduate School of Natural and Applied Sciences Erciyes University, Kayseri, Türkiye., Bozkurt T; Tekfen Agricultural Research Production and Marketing Inc., Adana, Türkiye., Tütüncü M; Department of Horticulture, Faculty of Agriculture, Ondokuz Mayıs University, Samsun, Türkiye., Dönmez D; Biotechnology Research and Application Center, Çukurova University, Adana, Türkiye., İzgü T; Institute of BioEconomy, National Research Council of Italy (CNR), Florence, Italy., Şimşek Ö; Department of Agricultural Science and Technology, Graduate School of Natural and Applied Sciences Erciyes University, Kayseri, Türkiye.; Department of Horticulture, Faculty of Agriculture, Erciyes University, Kayseri, Türkiye.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Jun 13; Vol. 19 (6), pp. e0305111. Date of Electronic Publication: 2024 Jun 13 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0305111
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Isak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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