Light-emitting diodes induced in vitro regeneration of Alternanthera reineckii mini and validation via machine learning algorithms
نویسندگان
چکیده
Optimization of in vitro regeneration protocol using multiple input variables is highly significant, and can be achieved by validating the data machine learning algorithms. Shoot tip nodal segment explants Alternanthera reineckii mini were inoculated on Murashige Skoog (MS) medium enriched with different concentrations benzylaminopurine (BAP), cultured under five monochromic light-emitting diodes (LEDs). The attained results validated through application four supervised models (RF, XGBoost, KNN, GP). prediction regression coefficient (R2), mean squared error (MSE), absolute percentage (MAPE) performance metrics. Results revealed R2 values 0.61 0.59 for shoot counts length, respectively. MSE registered between 3.48–5.42 count 0.40–0.74 whereas, 28.9–35.1% 13.2–18.4% MAPE recorded both length. Among utilized models, RF model predicted more accurately, followed XGBoost output variables. confirm that ML used validation, opens a new era employing modeling plant tissue culture other economically important plants. Schematic structure presenting features outputs together validation metrics
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ژورنال
عنوان ژورنال: In Vitro Cellular & Developmental Biology – Plant
سال: 2022
ISSN: ['1054-5476', '1475-2689']
DOI: https://doi.org/10.1007/s11627-022-10312-6