Application of artificial neural network and machine learning algorithms for modeling the in vitro regeneration of chickpea (Cicer arietinum L.)

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dc.contributor.authorKirtis, Arife
dc.contributor.authorAasim, Muhammad
dc.contributor.authorKatirci, Ramazan
dc.date.accessioned2024-02-23T13:59:26Z
dc.date.available2024-02-23T13:59:26Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractIn vitro whole plant regeneration protocol of desi chickpea was established followed by generating the model prediction using different machine learning algorithms. Surface sterilized seeds with 5% bleach (NaOCl) were cultured on Murashige and Skoog medium with six different concentrations (0.25, 0.50, 1.00, 1.50, 2.00 and 3.00 mg/L) of Benzylaminopurine (BAP), Thidiazuron or Kinetin (KIN). BAP and KIN enriched medium produced normal shoots and relatively high shoot induction frequency (%) was recorded 98.14-100%. Application of TDZ induced medium generated five and sevenfold more shoot counts than BAP and KIN respectively. Maximum shoot length was recorded as 10.67 cm and 9.90 cm on medium containing 0.25 mg/L BAP or 0.50 mg/L KIN respectively. Regenerated shoots were rooted on medium containing IBA. The establishment of plantlets were done in growth chamber adjusted to 24 +/- 1 degrees C, 60% relative humidity and 16 h light photoperiod where plant established flowering and set seeds. Machine learning algorithms of support vector regression, gaussian process regression, XGBoost, random forest (RF) models and multilayer perceptron neural network were used to predict the shoot count and length. It was found that the RF model indicated the highest performance to predict the outputs. To confirm the validity of the models, Leave-One-Out cross validation was used. The evaluation was performed using the parameters of R-2 (coefficient of determination and MSE (mean squared error) scores. In our study, The R-2 and MSE scores of RF model were 0.99 and 2.86 for shoot count, 0.98 and 0.29 for shoot length respectively.en_US
dc.identifier.doi10.1007/s11240-022-02255-y
dc.identifier.endpage152en_US
dc.identifier.issn0167-6857
dc.identifier.issn1573-5044
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85125546694en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage141en_US
dc.identifier.urihttps://doi.org/10.1007/s11240-022-02255-y
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11192
dc.identifier.volume150en_US
dc.identifier.wosWOS:000763197800002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofPlant Cell Tissue And Organ Cultureen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networken_US
dc.subjectDesi Chickpeaen_US
dc.subjectIn Vitroen_US
dc.subjectMachine Learningen_US
dc.subjectRootingen_US
dc.subjectSeeden_US
dc.titleApplication of artificial neural network and machine learning algorithms for modeling the in vitro regeneration of chickpea (Cicer arietinum L.)en_US
dc.typeArticleen_US

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