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Modeling Callus Induction and Regeneration in Hypocotyl Explant of Fodder Pea (Pisum sativum var. arvense L.) Using Machine Learning Algorithm Method

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cris.virtual.author-orcid0000-0002-2222-6866
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cris.virtual.author-orcid0000-0003-3721-6473
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dc.abstract.enA comprehensive understanding of genetic diversity and the categorization of germplasm is important to effectively identify appropriate parental candidates for the goal of breeding. It is necessary to have a technique of tissue culture that is both effective and reproducible to perform genetic engineering on fodder pea genotypes (Pisum sativum var. arvense L.). In this investigation, the genetic diversity of forty-two fodder pea genotypes was assessed based on their ability of callus induction (CI), the percentage of embryogenic callus by explant number (ECNEP), the percentage of responding embryogenic calluses by explant number (RECNEP), the number of somatic embryogenesis (NSE), the number of responding somatic embryogenesis (RSE), the regeneration efficiency (RE), and the number of regenerated plantlets (NRP). The findings of the ANOVA showed that there were significant differences (p < 0.001) between the genotypes for all in vitro parameters. The method of principal component analysis (PCA) was used to study the correlations that exist between the factors associated with tissue culture. While RE and NRP variables were most strongly associated with Doğruyol, Ovaçevirme-4, Doşeli-1, Yolgeçmez, and Incili-3 genotypes, RECNEP, NSE, RDE, and RECNEP variables were strongly associated with Avcılar, Ovaçevirme-3, and Ardahan Merkez-2 genotypes. The in vitro process is a complex multivariate process and more robust analyses are needed for linear and nonlinear parameters. Within the scope of this study, artificial neural network (ANN), random forest (RF), and multivariate adaptive regression spline (MARS) algorithms were used for RE estimation, and these algorithms were also compared. The results that we acquired from our research led us to the conclusion that the employed ANN-multilayer perceptron (ANN-MLP) model (R2 = 0.941) performs better than the RF model (R2 = 0.754) and the MARS model (R2 = 0.214). Despite this, it has been shown that the RF model is capable of accurately predicting RE in the early stages of the in vitro process. The current work is an inquiry regarding the use of RF, MARS, and ANN models in plant tissue culture, and it indicates the possibilities of application in a variety of economically important fodder peas.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorTürkoğlu, Aras
dc.contributor.authorBolouri, Parisa
dc.contributor.authorHaliloğlu, Kamil
dc.contributor.authorEren, Barış
dc.contributor.authorDemirel, Fatih
dc.contributor.authorIşık, Muhammet İslam
dc.contributor.authorPiekutowska, Magdalena
dc.contributor.authorWojciechowski, Tomasz
dc.contributor.authorNiedbała, Gniewko
dc.date.access2025-05-29
dc.date.accessioned2025-09-01T07:13:58Z
dc.date.available2025-09-01T07:13:58Z
dc.date.copyright2023-11-17
dc.date.issued2023
dc.description.abstract<jats:p>A comprehensive understanding of genetic diversity and the categorization of germplasm is important to effectively identify appropriate parental candidates for the goal of breeding. It is necessary to have a technique of tissue culture that is both effective and reproducible to perform genetic engineering on fodder pea genotypes (Pisum sativum var. arvense L.). In this investigation, the genetic diversity of forty-two fodder pea genotypes was assessed based on their ability of callus induction (CI), the percentage of embryogenic callus by explant number (ECNEP), the percentage of responding embryogenic calluses by explant number (RECNEP), the number of somatic embryogenesis (NSE), the number of responding somatic embryogenesis (RSE), the regeneration efficiency (RE), and the number of regenerated plantlets (NRP). The findings of the ANOVA showed that there were significant differences (p &lt; 0.001) between the genotypes for all in vitro parameters. The method of principal component analysis (PCA) was used to study the correlations that exist between the factors associated with tissue culture. While RE and NRP variables were most strongly associated with Doğruyol, Ovaçevirme-4, Doşeli-1, Yolgeçmez, and Incili-3 genotypes, RECNEP, NSE, RDE, and RECNEP variables were strongly associated with Avcılar, Ovaçevirme-3, and Ardahan Merkez-2 genotypes. The in vitro process is a complex multivariate process and more robust analyses are needed for linear and nonlinear parameters. Within the scope of this study, artificial neural network (ANN), random forest (RF), and multivariate adaptive regression spline (MARS) algorithms were used for RE estimation, and these algorithms were also compared. The results that we acquired from our research led us to the conclusion that the employed ANN-multilayer perceptron (ANN-MLP) model (R2 = 0.941) performs better than the RF model (R2 = 0.754) and the MARS model (R2 = 0.214). Despite this, it has been shown that the RF model is capable of accurately predicting RE in the early stages of the in vitro process. The current work is an inquiry regarding the use of RF, MARS, and ANN models in plant tissue culture, and it indicates the possibilities of application in a variety of economically important fodder peas.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,3
dc.description.number11
dc.description.points100
dc.description.versionfinal_published
dc.description.volume13
dc.identifier.doi10.3390/agronomy13112835
dc.identifier.issn2073-4395
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/4545
dc.identifier.weblinkhttp://www.mdpi.com/2073-4395/13/11/2835
dc.languageen
dc.relation.ispartofAgronomy
dc.relation.pagesart. 2835
dc.rightsCC-BY
dc.sciencecloudsend
dc.share.typeOPEN_JOURNAL
dc.subject.encluster analysis
dc.subject.engenetic diversity
dc.subject.enselection
dc.subject.enassociation
dc.subject.enprediction
dc.subject.enartificial intelligence
dc.titleModeling Callus Induction and Regeneration in Hypocotyl Explant of Fodder Pea (Pisum sativum var. arvense L.) Using Machine Learning Algorithm Method
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue11
oaire.citation.volume13