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<p>Growing <i>Phalaenopsis</i> orchids in vitro presents challenges for growers due to their prolonged growth period and extended juvenile stage. This study employed machine learning to optimize macronutrient concentrations during the cultivation of <i>Phalaenopsis</i> orchids. A dataset was generated by investigating the effects of various macronutrient levels on the morphological and biochemical traits of plantlets. An innovative iterative prediction scheme based on artificial neural networks (ANNs) was utilized to identify the optimal macronutrient levels necessary for enhancing plant morphology and biochemistry. The modeling results indicated that doubling the nitrate concentration, combined with a 2.2% increase in potassium and a 5.3% increase in phosphorus compared to the half-strength MS medium, yielded the most favorable morphological traits. Additionally, the model demonstrated that a twofold increase in nitrate, along with a 2.7% increase in potassium and a 4.7% increase in phosphorus, significantly improved biochemical characteristics, including chlorophyll <i>a</i>, chlorophyll <i>b</i>, total chlorophyll, and carotenoids. To investigate the possibility of introducing a single optimal point for the improvement of both morphological and biochemical traits, new plants were cultivated by doubling the nitrate content, a 2.5% increase in potassium, and a 5.0% increase in phosphorus, all compared with the control, i.e., half-strength MS medium. Remarkable enhancements were observed in plant physiology and biochemistry, achieving over a 50% increase in morphological traits and a 200% increase in biochemical traits. The proposed approach that effectively reduces the growth period of the orchid plantlets, can revolutionize the cultivation of plants with prolonged growth periods.</p>

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Revolutionizing in vitro cultivation: machine learning-based optimization of nutrients for superior morphology and biochemistry of Phalaenopsis orchids

  • 期刊论文

  • 发表日期：

    2025年4月22日

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

摘要

Growing Phalaenopsis orchids in vitro presents challenges for growers due to their prolonged growth period and extended juvenile stage. This study employed machine learning to optimize macronutrient concentrations during the cultivation of Phalaenopsis orchids. A dataset was generated by investigating the effects of various macronutrient levels on the morphological and biochemical traits of plantlets. An innovative iterative prediction scheme based on artificial neural networks (ANNs) was utilized to identify the optimal macronutrient levels necessary for enhancing plant morphology and biochemistry. The modeling results indicated that doubling the nitrate concentration, combined with a 2.2% increase in potassium and a 5.3% increase in phosphorus compared to the half-strength MS medium, yielded the most favorable morphological traits. Additionally, the model demonstrated that a twofold increase in nitrate, along with a 2.7% increase in potassium and a 4.7% increase in phosphorus, significantly improved biochemical characteristics, including chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids. To investigate the possibility of introducing a single optimal point for the improvement of both morphological and biochemical traits, new plants were cultivated by doubling the nitrate content, a 2.5% increase in potassium, and a 5.0% increase in phosphorus, all compared with the control, i.e., half-strength MS medium. Remarkable enhancements were observed in plant physiology and biochemistry, achieving over a 50% increase in morphological traits and a 200% increase in biochemical traits. The proposed approach that effectively reduces the growth period of the orchid plantlets, can revolutionize the cultivation of plants with prolonged growth periods.