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<p>Optimizing nutrient regimes is essential for enhancing growth and nutrient uptake in orchid tissue culture. This study applied artificial neural networks (ANNs) optimized with the genetic algorithm (GA) to model and predict the effects of varying ammonium (NH₄⁺), nitrate (NO₃⁻), phosphorus (P), and potassium (K) concentrations on <i>Phalaenopsis</i> plantlets. The approach identified two distinct optimal nutrient combinations: a 300% increase in NH₄⁺ with 20% P and 3% K maximized total nitrogen (N) content in plants, whereas a 100% increase in NO₃⁻ with 7.4% P and 2.1% K maximized leaf area. Considering the fact that the highest total N content in plants was inadequate to represent the optimal conditions for overall plant growth, experimental validation was performed to maximize leaf area. The validation based on newly-grown plantlets with optimized culture media (i.e., with a 100, 7.4, and 2.1% increase in NO₃⁻, P, and K, respectively) confirmed the predictions, showing a 387% increase in leaf area and a 5% increase in plant total N content compared to the control. These findings demonstrate the application of machine learning for precision nutrient optimization in orchid tissue culture, offering a valuable tool to enhance growth efficiency and nutrient use in <i>Phalaenopsis</i> propagation.</p>

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Machine learning-based macronutrients optimization enhances nitrogen uptake and growth in Phalaenopsis plantlets

  • 期刊论文

  • 发表日期：

    2025年8月11日

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  • Zahra Mahdavi,
  • Shirin Dianati Daylami,
  • Keyvan Asefpour Vakilian,
  • Kourosh Vahdati

摘要

Optimizing nutrient regimes is essential for enhancing growth and nutrient uptake in orchid tissue culture. This study applied artificial neural networks (ANNs) optimized with the genetic algorithm (GA) to model and predict the effects of varying ammonium (NH₄⁺), nitrate (NO₃⁻), phosphorus (P), and potassium (K) concentrations on Phalaenopsis plantlets. The approach identified two distinct optimal nutrient combinations: a 300% increase in NH₄⁺ with 20% P and 3% K maximized total nitrogen (N) content in plants, whereas a 100% increase in NO₃⁻ with 7.4% P and 2.1% K maximized leaf area. Considering the fact that the highest total N content in plants was inadequate to represent the optimal conditions for overall plant growth, experimental validation was performed to maximize leaf area. The validation based on newly-grown plantlets with optimized culture media (i.e., with a 100, 7.4, and 2.1% increase in NO₃⁻, P, and K, respectively) confirmed the predictions, showing a 387% increase in leaf area and a 5% increase in plant total N content compared to the control. These findings demonstrate the application of machine learning for precision nutrient optimization in orchid tissue culture, offering a valuable tool to enhance growth efficiency and nutrient use in Phalaenopsis propagation.