ArticlePloS one2014
Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- ADAM: advanced design and AI-driven modeling for plant tissue culture media optimization.Plant methods · 2026Article
- Integrated media and plant growth regulators comparative evaluation for enhanced in vitro propagation and acclimatization of Selenicereus Costaricensis.BMC plant biology · 2026Article
- Inoculum of EndophyticPlants (Basel, Switzerland) · 2025Article
- Identification of genes involved in the tomato root response to Globodera rostochiensis parasitism under varied light conditions.Journal of applied genetics · 2025Article
- Optimizing cannabis cultivation: an efficient in vitro system for flowering induction.Plant methods · 2024Article
- Plant Growth Regulation in Cell and Tissue Culture In Vitro.Plants (Basel, Switzerland) · 2024Review
- Dynamic changes of endogenous phytohormones and carbohydrates during spontaneous morphogenesis ofFrontiers in plant science · 2024Article
- Article
- Growth, photosynthetic function, and stomatal characteristics of Persian walnut explantsFrontiers in plant science · 2023Article
- Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron-multi-objective genetic algorithm.BMC plant biology · 2022Article
- Article
- Effect of Sucrose on Growth and Stress Status ofPlants (Basel, Switzerland) · 2022Article
- Light and Plant Growth Regulators on In Vitro Proliferation.Plants (Basel, Switzerland) · 2022Review
- Tissue culture coupled with a gas exchange system offers new perspectives on phenotyping the developmental biology ofFrontiers in plant science · 2022Article
- Comparative Analysis of Machine Learning and Evolutionary Optimization Algorithms for Precision Micropropagation ofFrontiers in plant science · 2021Article
- From Ethnomedicine to Plant Biotechnology and Machine Learning: The Valorization of the Medicinal PlantPharmaceuticals (Basel, Switzerland) · 2020Review
- Introducing a hybrid artificial intelligence method for high-throughput modeling and optimizing plant tissue culture processes: the establishment of a new embryogenesis medium for chrysanthemum, as a case study.Applied microbiology and biotechnology · 2020Article
- Recent Advances in Adventitious Root Formation in Chestnut.Plants (Basel, Switzerland) · 2020Review
- Shoot tip necrosis of in vitro plant cultures: a reappraisal of possible causes and solutions.Planta · 2020Review
- Machine Learning Unmasked Nutritional Imbalances on the Medicinal PlantFrontiers in plant science · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPlant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. METHODOLOGY AND PRINCIPAL
findingsIn this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2) s(-1).
conclusionsOur results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.
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