Prediction and Classification of Phenol Contents in <i>Cnidium officinale</i> Makino Using a Stacking Ensemble Model in Climate Change Scenarios
2024
Hyunjo Lee | Hyun Jung Koo | Kyeong Cheol Lee | Yoojin Song | Won-Kyun Joo | Cheol-Joo Chae
Recent studies have focused on using big-data-based machine learning to address the effects of climate change scenarios on the production and quality of medicinal plants. Challenges relating to data collection can hinder the analysis of key feature variables that affect the quality of medicinal plants. In the study presented herein, we analyzed feature variables that affect the phenolic content of Korean <i>Cnidium officinale</i> Makino (<i>C. officinale</i> Makino) under different climate change scenarios. We applied different climate change scenarios based on environmental information obtained from Yeongju city, Gyeongsangbuk-do, Republic of Korea, and cultivated <i>C. officinale</i> Makino to collect data. The collected data included 3237, 75, and 45 records, and data augmentation was performed to address this data imbalance. We designed a function based on the DPPH value to set the phenolic content grade in <i>C. officinale</i> Makino and proposed a stacking ensemble model for predicting the total phenol contents and classifying the phenolic content grades. The regression model in the performance evaluation presented an improvement of 6.23–7.72% in terms of the MAPE; in comparison, the classification model demonstrated a 2.48–3.34% better performance in terms of accuracy. The classification accuracy was >0.825 when classifying phenol content grades using the predicted total phenol content values from the regression model, and the area under the curve values of the model indicated high model fitness (0.987–0.981). We plan to identify the key feature variables for the optimal cultivation of <i>C. officinale</i> Makino and explore the relationships among these feature variables.
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