Classification and prediction of drought and salinity stress tolerance in barley using GenPhenML.

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 2 . The results showed that GenPhenML with a neural network model predicted the salinity stress resistance score with MAE, RMSE and R 2 values of 0.1206, 0.0308 and 0.9995, respectively. Also, the NN model predicted drought stress scores with MAE, RMSE and R 2 values of 0.0727, 0.0105 and 0.9999, respectively. The GenPhenML approach was also used to classify barley genotypes as resistant and stress-sensitive. The results showed that the accuracy, accuracy and F1 score of the proposed approach for salinity and drought stress classification were higher than 97%.

Plant phenotyping relevance

GenPhenMLという機械学習手法を開発し、分子マーカーと表現型形質から大麦遺伝子型の乾燥・塩ストレス耐性という植物状態を予測・分類しており、表現型推定手法が中心である。

Code and data availability

The supplied article blocks contain no public phenotype/trait dataset, image/sensor inputs, author code repository, or trained model checkpoint. There is no data or code availability statement, no deposit identifier, and no authors' public URL for GenPhenML assets; only generic methodological references and the CC-BY-4

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