Machine learning (ML) algorithms and artificial neural network for optimizing in vitro germination and growth indices of industrial hemp (Cannabis sativa L.)

Abstract

In vitro germination of hemp is challenging due to low germination and high contamination rates. Successful establishment of in vitro sterilization is the prerequisite of plant tissue culture studies. Recent advancements in the field of artificial neural network (ANN) and machine learning (ML) algorithms open new horizons for sustainable and precision agriculture. ANN and ML algorithms are powerful tools to evaluate the results and make more precise and high accuracy predictions in the field of plant tissue culture, especially for industrial purposes. Keeping in view, the study was designed to investigate the possible response of variable concentrations of hydrogen peroxide (H2O2) on germination and morphological traits of in vitro-grown hemp seedlings by using ML algorithms. Five different ML algorithms used in this study to evaluate the prediction of the output variables were: Support Vector Classifier (SVC), Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), Random Forest (RF) models, and Multilayer Perceptron (MLP) neural network utilizing accuracy, F1 score, precision, and recall values. Among the tested models, the RF model exhibited better prediction of output variables with a high F1 score in the range of 0.98–1.00. The F1 scores of the other models ranged between 0.69 and 0.86. Response surface methodology (RSM) used to compute the optimum concentration of H2O2 revealed the statistically significant effect of H2O2 on in vitro germination and seedling growth. The optimum value of H2O2 for the maximum germination and seedling was optimized to about ~2.2% by using RSM. The present work is a case study about the application of different ML and ANN models in plant tissue culture and reveals the possibility of application in many other economic crops.