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

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.

Using Machine Learning Algorithms to Investigate the Impact of Temperature Treatment and Salt Stress on Four Forage Peas (Pisum sativum var. arvense L.)

“…For plants that are susceptible to heat limitations, germination may provide early research for determining the threshold tolerance of the plant. Çaçan et al [43] investigated the germination abilities of several fodder pea lines and kinds under varied temperature conditions (10,23,27, and 30 • C). According to their study, the rate of germination typically fell at a temperature of 30 • C, but the time it took for germination to occur was reduced at a temperature of 10 • C. Sivritepe [44] declared that the optimal temperature for conducting germination experiments on Pisum sativum seedlings is 20 • C. According to Brar et al [45], the maximum germination rate of Pisum arvense seeds was seen at a temperature of 20 • C. The results of our investigation were consistent with those of earlier research.…”

“…In the process of analyzing the prediction performances and R 2 of the models, it was found that the model performances were comparable to those of earlier research that has been published in the literature. Benlio glu et al [23] effectively used machine learning methods to clarify the germination traits of tetraploid wheat in the presence of drought-induced stress. In their investigation, they reported that the elastic net (ELNET) model showed more efficiency in predicting germination speed (R 2 = 0.762, RMSE = 4.873, MAD = 3.755), germination power (R 2 = 0.514, RMSE = 4.946, MAD = 3.581), and water absorption capacity (R 2 = 0.902, RMSE = 5.140, MAD = 2.492), the Gaussian processes classifier (GPC) model showed superior performance in predicting root length (R 2 = 0.981, RMSE = 0.552, MAD = 0.407), and the XGBoost model showed better prediction performance for shoot length (R 2 = 0.962, RMSE = 0.761, MAD = 0.457), fresh weight (R 2 = 0.962, RMSE = 0.077, MAD = 0.056), and dry weight (R 2 = 0.944, RMSE = 0.018, MAD = 0.014).…”

“…Since there is little research on this topic, forage pea varieties' germination responses to abiotic stressors must be examined. Furthermore, optimization and forecasting are particularly desired in agricultural research, and solid findings may be produced by using modern computer techniques such as machine learning algorithms that employ fewer inputs [22,23]. These approaches are advantageous because they are easily accessible and can be implemented without requiring extensive resources or specialized equipment.…”