Evidence map›Paper›PMID 42432517›Full record

ArticleBMC plant biology2026

ANFIS-derived response surfaces reveal organ-specific optimal conditions for nutrient supply, light quality, plant developmental status, and culture configuration in Phalaenopsis amabilis in vitro propagation.

Aylar Mohammadpour Barough, Shirin Dianati Daylami, Keyvan Asefpour Vakilian, Sasan Aliniaeifard, Kourosh Vahdati

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Article in BMC plant biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Aylar Mohammadpour BaroughDepartment of Horticulture, College of Aburaihan, University of Tehran, Tehran, 33916-53755, Iran.ORCID http://orcid.org/0009-0007-4606-8288
Shirin Dianati DaylamiDepartment of Horticulture, College of Aburaihan, University of Tehran, Tehran, 33916-53755, Iran. dianati@ut.ac.ir.ORCID http://orcid.org/0000-0002-1736-0745
Keyvan Asefpour VakilianDepartment of Biosystems Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, 4913815739, Iran.ORCID http://orcid.org/0000-0001-5035-7727
Sasan AliniaeifardDepartment of Horticulture, College of Aburaihan, University of Tehran, Tehran, 33916-53755, Iran.ORCID http://orcid.org/0000-0001-9572-2839
Kourosh VahdatiDepartment of Horticulture, College of Aburaihan, University of Tehran, Tehran, 33916-53755, Iran.ORCID http://orcid.org/0000-0001-8458-8697

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTissue culture optimization in orchids is constrained by the impracticality of experimentally testing all possible combinations of environmental and nutritional factors. In this study, the interactive effects of culture media composition, light quality, plant developmental status (rooted vs. rootless), and culture configuration (single-phase vs. two-phase) on organ-specific morpho-physiological traits of Phalaenopsis amabilis were investigated under in vitro condition. Growth variables, pigment composition, carbohydrate metabolism, and maximum quantum efficiency of photosystem II (F

resultsANFIS exhibited superior capability compared with FIS in predicting nonlinear response surfaces and identifying trait-specific optimal regions. For leaf growth traits, ANFIS predicted optimal regions under orange-red light combined with moderate nitrogen (N) availability, whereas FIS produced narrower and more localized optima. For root biomass and carbohydrate accumulation traits, ANFIS predicted red light dominated optimal regions with elevated phosphorus (P) and reduced N, indicating stronger sensitivity of sink-related traits to light nutrient interactions. For leaf pigment accumulation traits, ANFIS identified broader optimal regions under blue light with lower N levels, while carbohydrate related traits showed distinct optima under red and green spectral conditions depending on nutrient balance. Overall, ANFIS generated more distinct and physiologically consistent response surfaces than FIS across all traits.

conclusionsMachine learning assisted modeling effectively identified organ-specific optimal treatment regions and revealed complex nonlinear interactions among experimental factors that are not directly observable from mean treatment comparisons. The ANFIS framework provides a robust predictive tool for optimizing orchid tissue culture systems and for generating testable hypotheses for future in vitro propagation studies.

Indexed as

OrchidaceaeCulture MediaFuzzy LogicLightNutrientsPlant LeavesPlant RootsSoft ComputingTissue Culture TechniquesCulture MediaANFISEpiphytic orchidFISLight spectraNutrient regulationOrgan specializationPhalaenopsisPhotosynthesisPlant tissue cultureSource-sink relations

Identifiers

PMID42432517
PMCPMC13640281

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.