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

Author

Listed:
  • Musab A Isak
  • Taner Bozkurt
  • Mehmet Tütüncü
  • Dicle Dönmez
  • Tolga İzgü
  • Özhan Şimşek

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.

Suggested Citation

  • Musab A Isak & Taner Bozkurt & Mehmet Tütüncü & Dicle Dönmez & Tolga İzgü & Özhan Şimşek, 2024. "Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-20, June.
  • Handle: RePEc:plo:pone00:0305111
    DOI: 10.1371/journal.pone.0305111
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    References listed on IDEAS

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    1. Hamed Rezaei & Asghar Mirzaie-asl & Mohammad Reza Abdollahi & Masoud Tohidfar, 2023. "Comparative analysis of different artificial neural networks for predicting and optimizing in vitro seed germination and sterilization of petunia," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-24, May.
    2. Siamak Farhadi & Mina Salehi & Ahmad Moieni & Naser Safaie & Mohammad Sadegh Sabet, 2020. "Modeling of paclitaxel biosynthesis elicitation in Corylus avellana cell culture using adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) and multiple regression methods," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
    3. Mohsen Hesami & Milad Alizadeh & Roohangiz Naderi & Masoud Tohidfar, 2020. "Forecasting and optimizing Agrobacterium-mediated genetic transformation via ensemble model- fruit fly optimization algorithm: A data mining approach using chrysanthemum databases," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
    4. Marziyeh Jafari & Mohammad Hosein Daneshvar, 2024. "Machine learning-mediated Passiflora caerulea callogenesis optimization," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-16, January.
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