Insights into Drought Tolerance of Tetraploid Wheat Genotypes in the Germination Stage Using Machine Learning Algorithms

Berk Benlioğlu, Fatih Demirel, Aras Türkoğlu (), Kamil Haliloğlu, Hamdi Özaktan, Sebastian Kujawa, Magdalena Piekutowska, Tomasz Wojciechowski and Gniewko Niedbała ()
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Berk Benlioğlu: Department of Field Crops, Faculty of Agriculture, Ankara University, 06110 Ankara, Türkiye
Fatih Demirel: Department of Agricultural Biotechnology, Faculty of Agriculture, Igdır University, 76000 Igdir, Türkiye
Aras Türkoğlu: Department of Field Crops, Faculty of Agriculture, Necmettin Erbakan University, 42310 Konya, Türkiye
Kamil Haliloğlu: Department of Field Crops, Faculty of Agriculture, Atatürk University, 25240 Erzurum, Türkiye
Hamdi Özaktan: Department of Field Crops, Faculty of Agriculture, Erciyes University, 38000 Kayseri, Türkiye
Sebastian Kujawa: Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Magdalena Piekutowska: Department of Botany and Nature Protection, Institute of Biology, Pomeranian University in Słupsk, 22b Arciszewskiego St., 76-200 Słupsk, Poland
Tomasz Wojciechowski: Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Gniewko Niedbała: Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland

Agriculture, 2024, vol. 14, issue 2, 1-18

Abstract: Throughout germination, which represents the initial and crucial phase of the wheat life cycle, the plant is notably susceptible to the adverse effects of drought. The identification and selection of genotypes exhibiting heightened drought tolerance stand as pivotal strategies aimed at mitigating these effects. For the stated objective, this study sought to evaluate the responses of distinct wheat genotypes to diverse levels of drought stress encountered during the germination stage. The induction of drought stress was achieved using polyethylene glycol at varying concentrations, and the assessment was conducted through the application of multivariate analysis and machine learning algorithms. Statistical significance ( p < 0.01) was observed in the differences among genotypes, stress levels, and their interaction. The ranking of genotypes based on tolerance indicators was evident through a principal component analysis and biplot graphs utilizing germination traits and stress tolerance indices. The drought responses of wheat genotypes were modeled using germination data. Predictions were then generated using four distinct machine learning techniques. An evaluation based on R-square, mean square error, and mean absolute deviation metrics indicated the superior performance of the elastic-net model in estimating germination speed, germination power, and water absorption capacity. Additionally, in assessing the criterion metrics, it was determined that the Gaussian processes classifier exhibited a better performance in estimating root length, while the extreme gradient boosting model demonstrated superior performance in estimating shoot length, fresh weight, and dry weight. The study’s findings underscore that drought tolerance, susceptibility levels, and parameter estimation for durum wheat and similar plants can be reliably and efficiently determined through the applied methods and analyses, offering a fast and cost-effective approach.

Keywords: tetraploid wheat; drought stress; germination; stress tolerance; modeling (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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