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Background <p>Tissue 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 <i>Phalaenopsis amabilis</i> were investigated under in vitro condition. Growth variables, pigment composition, carbohydrate metabolism, and maximum quantum efficiency of photosystem II (F<sub>v</sub>/F<sub>m</sub>) were quantified separately in leaves and aerial roots. The primary objective was to predict trait-specific optimal treatment regions using fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) models trained on organ-resolved datasets derived from 60 treatment combinations across 16 traits.</p> Results <p>ANFIS 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.</p> Conclusions <p>Machine 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.</p>

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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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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

摘要

Background

Tissue 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 (Fv/Fm) were quantified separately in leaves and aerial roots. The primary objective was to predict trait-specific optimal treatment regions using fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) models trained on organ-resolved datasets derived from 60 treatment combinations across 16 traits.

Results

ANFIS 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.

Conclusions

Machine 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.