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Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate

Recently, γ-Aminobutyric acid (GABA) has been introduced as a treatment with high physiological activity induction to enhance the ability of plants against drought and salinity stress, which led to a decline in plant growth. Since changes in morphological traits to drought and salinity stress are in...

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Autores principales: Zarbakhsh, Saeedeh, Shahsavar, Ali Reza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534893/
https://www.ncbi.nlm.nih.gov/pubmed/36198905
http://dx.doi.org/10.1038/s41598-022-21129-z
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author Zarbakhsh, Saeedeh
Shahsavar, Ali Reza
author_facet Zarbakhsh, Saeedeh
Shahsavar, Ali Reza
author_sort Zarbakhsh, Saeedeh
collection PubMed
description Recently, γ-Aminobutyric acid (GABA) has been introduced as a treatment with high physiological activity induction to enhance the ability of plants against drought and salinity stress, which led to a decline in plant growth. Since changes in morphological traits to drought and salinity stress are influenced by multiple factors, advanced computational analysis has great potential for computing nonlinear and multivariate data. In this work, the effect of four input variables including GABA concentration, pomegranate cultivars, days of treatment, and drought and salinity stress evaluated to predict and modeling of morphological traits using artificial neural network (ANN) models including multilayer perceptron (MLP) and radial basis function (RBF). Image processing technique was used to measure the LLI, LWI, and LAI parameters. Among the ANNs applied, the MLP algorithm was chosen as the best model based on the highest accuracy. Furthermore, to predict and estimate the optimal values of input variables for achieving the best morphological parameters, the MLP algorithm was linked to a non-dominated sorting genetic algorithm-II (NSGA-II). Based on the results of MLP-NSGA-II, the best values of crown diameter (18.42 cm), plant height (151.82 cm), leaf length index (5.67 cm), leaf width index (1.76 cm), and leaf area index (13.82 cm) could be achieved with applying 10.57 mM GABA on ‘Atabaki’ cultivar under control (non-stress) condition after 20.8 days. The results of modeling and optimization can be helpful to predict the morphological responses to drought and salinity conditions.
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spelling pubmed-95348932022-10-07 Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate Zarbakhsh, Saeedeh Shahsavar, Ali Reza Sci Rep Article Recently, γ-Aminobutyric acid (GABA) has been introduced as a treatment with high physiological activity induction to enhance the ability of plants against drought and salinity stress, which led to a decline in plant growth. Since changes in morphological traits to drought and salinity stress are influenced by multiple factors, advanced computational analysis has great potential for computing nonlinear and multivariate data. In this work, the effect of four input variables including GABA concentration, pomegranate cultivars, days of treatment, and drought and salinity stress evaluated to predict and modeling of morphological traits using artificial neural network (ANN) models including multilayer perceptron (MLP) and radial basis function (RBF). Image processing technique was used to measure the LLI, LWI, and LAI parameters. Among the ANNs applied, the MLP algorithm was chosen as the best model based on the highest accuracy. Furthermore, to predict and estimate the optimal values of input variables for achieving the best morphological parameters, the MLP algorithm was linked to a non-dominated sorting genetic algorithm-II (NSGA-II). Based on the results of MLP-NSGA-II, the best values of crown diameter (18.42 cm), plant height (151.82 cm), leaf length index (5.67 cm), leaf width index (1.76 cm), and leaf area index (13.82 cm) could be achieved with applying 10.57 mM GABA on ‘Atabaki’ cultivar under control (non-stress) condition after 20.8 days. The results of modeling and optimization can be helpful to predict the morphological responses to drought and salinity conditions. Nature Publishing Group UK 2022-10-05 /pmc/articles/PMC9534893/ /pubmed/36198905 http://dx.doi.org/10.1038/s41598-022-21129-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zarbakhsh, Saeedeh
Shahsavar, Ali Reza
Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate
title Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate
title_full Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate
title_fullStr Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate
title_full_unstemmed Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate
title_short Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate
title_sort artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534893/
https://www.ncbi.nlm.nih.gov/pubmed/36198905
http://dx.doi.org/10.1038/s41598-022-21129-z
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