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. 2023 Nov 15;12(22):3867.
doi: 10.3390/plants12223867.

Hierarchical Machine Learning-Based Growth Prediction Model of Panax ginseng Sprouts in a Hydroponic Environment

Tae Hyong Kim  1 , Seunghoon Baek  1 , Ki Hyun Kwon  1 , Seung Eel Oh  2
Affiliations

Hierarchical Machine Learning-Based Growth Prediction Model of Panax ginseng Sprouts in a Hydroponic Environment

Tae Hyong Kim et al. Plants (Basel). .

Abstract

Due to an increase in interest towards functional and health-related foods, Panax ginseng sprout has been in the spotlight since it contains a significant amount of saponins which have anti-cancer, -stress, and -diabetic effects. To increase the amount of production as well as decrease the cultivation period, sprouted ginseng is being studied to ascertain its optimal cultivation environment in hydroponics. Although there are studies on functional components, there is a lack of research on early disease prediction along with productivity improvement. In this study, the ginseng sprouts were cultivated in four different hydroponic conditions: control treatment, hydrogen-mineral treatment, Bioblock treatment, and highly concentrated nitrogen treatment. Physical properties were measured, and environmental data were acquired using sensors. Using three algorithms (artificial neural networks, support vector machines, random forest) for germination and rottenness classification, and leaf number and length of stem prediction models, we propose a hierarchical machine learning model that predicts the growth outcome of ginseng sprouts after a week. Based on the results, a regression model predicts the number of leaves and stem length during the growth process. The results of the classifier models showed an F1-score of germination classification of about 99% every week. The rottenness classification model showed an increase from an average of 83.5% to 98.9%. Predicted leaf numbers for week 1 showed an average nRMSE value of 0.27, which decreased by about 33% by week 3. The results for predicting stem length showed a higher performance compared to the regression model for predicting leaf number. These results showed that the proposed hierarchical machine learning algorithm can predict germination and rottenness in ginseng sprout using physical properties.

Keywords: ginsenosides; hydroponic cultivation; machine learning; plant factory; rottenness.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The representative confusion matrix for the germination classification model for Week 1, 2, and 3.
Figure 2
Figure 2
The representative confusion matrix for the rottenness classification model for Week 1, 2, and 3.
Figure 3
Figure 3
The regression result for number of leaves prediction from Week 1 to 3.
Figure 4
Figure 4
The regression result of predicting length of stem prediction from Week 1 to 3.
Figure 5
Figure 5
Representative image of Panax sprout ginseng 1-year-old seed ginseng.
Figure 6
Figure 6
Hydroponic environment for growth of Panax sprout ginseng seed bed configuration.
Figure 7
Figure 7
Various sensors applied to measure hydroponic environment condition, sensor monitoring system and LED.
Figure 8
Figure 8
Representative growth of Panax sprout ginseng in on a hydroponic environment for 3 weeks.
Figure 9
Figure 9
Overall flowchart of the proposed hierarchical machine learning-based Panax sprout ginseng growth classification and prediction model.
Figure 10
Figure 10
Detailed hierarchical machine learning-based classification and regression flowchart.
Figure 11
Figure 11
The Panax ginseng sprout rottenness classification using three different supervised machine learning algorithms.
See this image and copyright information in PMC

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