Abstract
Industrial hemp (Cannabis sativa L.) is a highly recalcitrant plant under in vitro conditions that can be overcome by employing external stimuli. Hemp seeds were primed with 2.0–3.0% hydrogen peroxide (H2O2) followed by culture under different Light Emitting Diodes (LEDs) sources. Priming seeds with 2.0% yielded relatively high germination rate, growth, and other biochemical and enzymatic activities. The LED lights exerted a variable impact on Cannabis germination and enzymatic activities. Similarly, variable responses were observed for H2O2 × Blue-LEDs combination. The results were also analyzed by multiple regression analysis, followed by an investigation of the impact of both factors by Pareto chart and normal plots. The results were optimized by contour and surface plots for all parameters. Response surface optimizer optimized 2.0% H2O2 × 918 LUX LEDs for maximum scores of all output parameters. The results were predicted by employing Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Moreover, the validity of these models was assessed by using six different performance metrics. MLP performed better than RF and XGBoost models, considering all six-performance metrics. Despite the differences in scores, the performance indicators for all examined models were quite close to each other. It can easily be concluded that all three models are capable of predicting and validating data for cannabis seeds primed with H2O2 and grown under different LED lights.
Graphical abstract
Key message
Chemical priming of H2O2 with LED lights regulates the cannabis plant growth. Use of Pareto chart and normal plots to rank the input factor, their impact, and efficiency in percentage. Use of optimizing tools like contour plots, surface plots, and response optimizers to optimize H2O2 and LED lights for cannabis. Data validation and prediction using AI/ML-based MLP, RF, and XGBoost models.
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Data availability
The datasets generated during and/or analysed during the current study are not publicly available and can be provided on reasonable request.
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Funding
The present study was derived from Master thesis of Miss Buşra Yıldırım and the study was financially supported by The Scientific Research Council (BAP) of Sivas University of Science and Technology, Sivas, Türkiye (Grant Number:2022-YLTB-TBT-0001).
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MA: Conceived idea, Supervision, Research designing, Data analysis, graphical work, Manuscript writing. BY: Conducted research work, data tabulation. AS: Biochemical and enzyme analysis. SAA: Tabulation of formulas, graphic figures, Review, Machine learning analysis. SA: Supervision, Article control, and editing. MAN: Article writing, Enzyme analysis.
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Aasim, M., Yıldırım, B., Say, A. et al. Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.). Plant Mol Biol 114, 33 (2024). https://doi.org/10.1007/s11103-024-01427-y
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DOI: https://doi.org/10.1007/s11103-024-01427-y