ArticleScientific reports2024
Classification and prediction of drought and salinity stress tolerance in barley using GenPhenML.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Machine learning model provides stress biomarkers for the classification of abiotic stress in Micro-Tom.Scientific reports · 2026Article
- Combined effect of salt stress and high light in plants: from basic statistical approach to machine learning methods.BMC plant biology · 2025Article
- Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches.Scientific reports · 2025Article
- AdapTree: Data-Driven Approach to Assessing Plant Stress Through the AI-Sensor Synergy.Sensors (Basel, Switzerland) · 2025Article
- Genome-Wide Identification, Molecular Evolution, and Expression Divergence ofFood science & nutrition · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
Genetic and agronomic advances consistently lead to an annual increase in global barley yield. Since abiotic stresses (physical environmental factors that negatively affect plant growth) reduce barley yield, it is necessary to predict barley resistance. Artificial intelligence and machine learning (ML) models are new and powerful tools for predicting product resilience. Considering the research gap in the use of molecular markers in predicting abiotic stresses, this paper introduces a new approach called GenPhenML that combines molecular markers and phenotypic traits to predict the resistance of barley genotypes to drought and salinity stresses by ML models. GenPhenML uses feature selection algorithms to determine the most important molecular markers. It then identifies the best model that predicts atmospheric resistance with lower MAE, RMSE, and higher R
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