Responses of In Vitro Strawberry Plants to Drought Stress under the Influence of Nano-Silicon Dioxide

Şener, Sevinç; Sayğı, Hülya; Duran, Canan Nilay

doi:10.3390/su152115569

Cited by 6 publications

(3 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The increase in root count at the maximum PEG concentration for all cultivars is reminiscent of the findings of Zahedi et al [53], in whose study the accumulation of osmolytes and enhanced antioxidant enzyme activities were observed under severe drought stress. The decrease in root length at 6 mg/L PEG corresponds to Şener et al [51], in whose study nano-silicon dioxide application mitigated reduced vegetative growth under drought stress. Our data on root rates at different PEG concentrations align with a study by Josefi et al [52], emphasizing the influence of jasmonic acid (JA) on morphophysiological and biochemical characteristics under PEG-induced water stress.…”

Section: Discussionsupporting

confidence: 59%

“…Our experimental results reveal significant insights into the responses of three strawberry genotypes ("Festival", "Fortuna", and "Rubygem") to varying concentrations of polyethylene glycol (PEG) simulating drought stress. In comparing our findings with studies by Hussein et al [49], Mozafari et al [50], Şener et al [51], Josefi et al [52], and Zahedi et al [53], we can draw valuable comparisons to elucidate the implications of our results. The results indicate that PEG concentrations influence plant height, with the "Fortuna" cultivar reaching its maximum height in a PEG-free medium, while "Festival" showed the shortest height at 6 mg/L PEG.…”

Section: Discussionmentioning

confidence: 52%

“…The application of iron nano-particles and salicylic acid, as discussed by Mozafari et al [50], resonates with our findings on the influence of PEG on strawberry growth traits. Additionally, Şener et al [51] and Josefi et al [52] provide evidence of the positive effects of nano-silicon dioxide and jasmonic acid in enhancing drought stress tolerance. Zahedi et al [53] compare two strawberry cultivars under different drought conditions, supporting our emphasis on genotypic variations.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on Strawberry Cultivars

Şimşek

2024

Agriculture

View full text Add to dashboard Cite

This study aimed to assess the susceptibility of three strawberry cultivars (“Festival”, “Fortuna”, and “Rubygem”) to drought stress induced by varying polyethylene glycol (PEG) concentrations in the culture medium. Plantlets were cultivated on a solid medium supplemented with 1 mg/L BAP, and PEG concentrations (0, 2, 4, and 6 mg/L) were introduced to simulate drought stress. Morphological changes were observed, and morphometric analysis was conducted. Additionally, artificial neural network (ANN) analysis and machine learning approaches were integrated into this study. The results showed significant effects of PEG concentrations on plant height and multiplication coefficients, highlighting genotype-specific responses. This study employed various machine learning models, with random forest consistently demonstrating superior performance. Our findings revealed the random forest model outperformed others with a remarkable global diagnostic accuracy of 91.164%, indicating its superior capability in detecting and predicting water stress effects in strawberries. Specifically, the RF model excelled in predicting root length and the number of roots for “Festival” and “Fortuna” cultivars, demonstrating its reliability across different genetic backgrounds. Meanwhile, for the “Rubygem” cultivar, the multi-layer perceptron (MLP) and Gaussian process (GP) models showed particular strengths in predicting proliferation and plant height, respectively. These findings highlight the potential of ML models, particularly RF, to enhance agricultural breeding and cultivation strategies through accurate phenotypic predictions, suggesting a promising direction for future research to improve these predictions further. This research contributes to understanding strawberry responses to drought stress and emphasizes the potential of machine learning in predicting plant characteristics.

show abstract

Section: Discussionsupporting

confidence: 59%