Scholarlink

<p>Callus formation in <i>Lavandula × intermedia</i> varies widely depending on explant type, plant growth regulator composition, and cultivation duration, yet their combined effects remain insufficiently characterized. Here, 57 growth regulator treatments were evaluated using root- and stem-derived explants over a 15-week culture period. Callus induction occurred on most media and was typically initiated within the first three weeks, while biomass accumulation followed a biphasic pattern with a pronounced increase after week six, reaching up to 35&#xa0;g depending on treatment. A combination of 0.5&#xa0;mg L⁻¹ 2,4-D and 0.5&#xa0;mg L⁻¹ kinetin consistently produced the highest biomass. To model system behavior, five statistical and machine learning approaches were applied. XGBoost achieved the highest predictive accuracy on experimental data (R² ≈ 0.94), whereas Random Forest showed the most stable performance across independent validation datasets. Feature importance analysis identified culture duration as the dominant factor influencing biomass, while hormonal composition significantly affected both responses and explant type had only a minor contribution. Multi-objective optimization using NSGA-II revealed multiple high-performing solutions, while induction converged to a single near-optimal condition. These findings demonstrate that integrating experimental data with machine learning enables robust prediction and optimization of callus responses in <i>Lavandula × intermedia</i>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning-driven modelling and optimization of callus induction and biomass accumulation in Lavandula × intermedia

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月24日

查看全文
  • Šarlota Kaňuková,
  • Lea Veničáková,
  • Seyid Amjad Ali,
  • Katarína Ondreičková,
  • Sofia Mikulášková,
  • Jozef Gubiš,
  • Marcela Gubišová

摘要

Callus formation in Lavandula × intermedia varies widely depending on explant type, plant growth regulator composition, and cultivation duration, yet their combined effects remain insufficiently characterized. Here, 57 growth regulator treatments were evaluated using root- and stem-derived explants over a 15-week culture period. Callus induction occurred on most media and was typically initiated within the first three weeks, while biomass accumulation followed a biphasic pattern with a pronounced increase after week six, reaching up to 35 g depending on treatment. A combination of 0.5 mg L⁻¹ 2,4-D and 0.5 mg L⁻¹ kinetin consistently produced the highest biomass. To model system behavior, five statistical and machine learning approaches were applied. XGBoost achieved the highest predictive accuracy on experimental data (R² ≈ 0.94), whereas Random Forest showed the most stable performance across independent validation datasets. Feature importance analysis identified culture duration as the dominant factor influencing biomass, while hormonal composition significantly affected both responses and explant type had only a minor contribution. Multi-objective optimization using NSGA-II revealed multiple high-performing solutions, while induction converged to a single near-optimal condition. These findings demonstrate that integrating experimental data with machine learning enables robust prediction and optimization of callus responses in Lavandula × intermedia.