Application of Adaptive Neuro-Fuzzy Inference System-Non-dominated Sorting Genetic Algorithm-II (ANFIS-NSGAII) for Modeling and Optimizing Somatic Embryogenesis of Chrysanthemum.
artificial intelligence
carbohydrate
chrysanthemum
embryogenesis
in vitro culture
light quality
optimization algorithm
plant growth regulator
Journal
Frontiers in plant science
ISSN: 1664-462X
Titre abrégé: Front Plant Sci
Pays: Switzerland
ID NLM: 101568200
Informations de publication
Date de publication:
2019
2019
Historique:
received:
20
02
2019
accepted:
18
06
2019
entrez:
24
7
2019
pubmed:
25
7
2019
medline:
25
7
2019
Statut:
epublish
Résumé
A hybrid artificial intelligence model and optimization algorithm could be a powerful approach for modeling and optimizing plant tissue culture procedures. The aim of this study was introducing an Adaptive Neuro-Fuzzy Inference System- Non-dominated Sorting Genetic Algorithm-II (ANFIS-NSGAII) as a powerful computational methodology for somatic embryogenesis of chrysanthemum, as a case study. ANFIS was used for modeling three outputs including callogenesis frequency (CF), embryogenesis frequency (EF), and the number of somatic embryo (NSE) based on different variables including 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), sucrose, glucose, fructose, and light quality. Subsequently, models were linked to NSGAII for optimizing the process, and the importance of each input was evaluated by sensitivity analysis. Results showed that all of the R
Identifiants
pubmed: 31333705
doi: 10.3389/fpls.2019.00869
pmc: PMC6624437
doi:
Types de publication
Journal Article
Langues
eng
Pagination
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