Hierarchical Machine Learning-Based Growth Prediction Model of Panax ginseng Sprouts in a Hydroponic Environment
Abstract
:1. Introduction
2. Results
2.1. Classification Result for Germination and Rottenness of Panax Ginseng
2.2. Regression Result for Predicting the Number of Leaf and Length of Stem for Panax Ginseng
3. Discussion
4. Materials and Methods
4.1. Data Sample
4.1.1. Sample Material
4.1.2. Configuration of Growth Environment
4.2. Data Processing
4.2.1. Data Sampling
4.2.2. Data Preprocessing
4.2.3. Input and Output Feature Formation for Machine Learning Algorithms
4.3. The Process of Hierarchical Machine Learning Algorithm for Classifying and Predicting Growth of Ginseng
4.3.1. 1st Stage of Germination and Rottenness Classification
4.3.2. Prediction of Number of Leaves and Length of Stem
4.4. Optimization
4.5. Performance Evaluation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Lee, S.M.; Bae, B.S.; Park, H.W.; Ahn, N.G.; Cho, B.G.; Cho, Y.L.; Kwak, Y.S. Characterization of Korean Red Ginseng (Panax ginseng Meyer): History, preparation method, and chemical composition. J. Ginseng Res. 2015, 39, 384–391. [Google Scholar] [CrossRef] [PubMed]
- Kim, D.H.; Moon, Y.S.; Lee, T.H.; Jung, J.S.; Suh, H.W.; Song, D.K. The inhibitory effect of ginseng saponins on the stress-induced plasma interleukin-6 level in mice. Neurosci. Lett. 2003, 353, 13–16. [Google Scholar] [CrossRef] [PubMed]
- López, M.V.N.; Cuadrado, M.P.G.S.; Ruiz-Poveda, O.M.P.; Del Fresno, A.M.V.; Accame, M.E.C. Neuroprotective effect of individual ginsenosides on astrocytes primary culture. Biochim. Biophys. Acta (BBA)-Gen. Subj. 2007, 1770, 1308–1316. [Google Scholar] [CrossRef] [PubMed]
- Kim, Y.J.; Nguyen, T.K.L.; Oh, M.M. Growth and ginsenosides content of ginseng sprouts according to LED-based light quality changes. Agronomy 2020, 10, 1979. [Google Scholar] [CrossRef]
- Lee, Y.R.; Seo, J.H.; Hong, C.Y.; Kim, K.H.; Lee, J.; Jeong, H.S. Antioxidant activities of hydropoic-cultured ginseng roots and leaves. Korean J. Food Nutr. 2020, 33, 58–63. [Google Scholar]
- Jun, S.Y.; Kim, T.H.; Hwang, S.H. The consumption status and preference for sprouts and leafy vegetables. Korean J. Food Preserv. 2012, 19, 783–791. [Google Scholar] [CrossRef]
- Lee, T.K.; Lee, J.Y.; Cho, Y.J.; Kim, J.E.; Kim, S.Y.; Park, J.H.Y.; Lee, K.W. Optimization of the extraction process of high levels of chlorogenic acid and ginsenosides from short-term hydroponic-cultured ginseng and evaluation of the extract for the prevention of atopic dermatitis. J. Ginseng Res. 2022, 46, 367–375. [Google Scholar] [CrossRef] [PubMed]
- Kim, G.; Hyun, D.; Kim, Y.; Lee, S.; Kwon, H.; Cha, S.; Kim, Y. Investigation of ginsenosides in different parts of Panax ginseng cultured by hydroponics. Korean J. Hortic. Sci. Technol. 2010, 28, 216–226. [Google Scholar]
- Harkey, M.R.; Henderson, G.L.; Gershwin, M.E.; Stern, J.S.; Hackman, R.M. Variability in commercial ginseng products: An analysis of 25 preparations. Am. J. Clin. Nutr. 2001, 73, 1101–1106. [Google Scholar] [CrossRef]
- Kozai, T. Resource use efficiency of closed plant production system with artificial light: Concept, estimation and application to plant factory. Proc. Jpn. Acad. Ser. B 2013, 89, 447–461. [Google Scholar] [CrossRef] [PubMed]
- Song, Y.N.; Hong, H.G.; Son, J.S.; Kwon, Y.O.; Lee, H.H.; Kim, H.J.; Yoon, M.H. Investigation of ginsenosides and antioxidant activities in the roots, leaves, and stems of hydroponic-cultured ginseng (Panax ginseng Meyer). Prev. Nutr. Food Sci. 2019, 24, 283. [Google Scholar] [CrossRef] [PubMed]
- Kim, G.S.; Lee, S.E.; Noh, H.J.; Kwon, H.; Lee, S.W.; Kim, S.Y.; Kim, Y.B. Effects of natural bioactive products on the growth and ginsenoside contents of Panax ginseng cultured in an aeroponic system. J. Ginseng Res. 2012, 36, 430. [Google Scholar] [CrossRef] [PubMed]
- Jeon, S.Y.; Sung, J.M.; Roh, J.H.; Kwon, K.H. Effects of LED treatment and plasma-activated water on the growth and quality of Panax ginseng sprouts during hydroponic cultivation. Korean J. Food Preserv. 2021, 28, 890–899. [Google Scholar] [CrossRef]
- Nájera, C.; Gallegos-Cedillo, V.M.; Ros, M.; Pascual, J.A. Role of Spectrum-Light on Productivity, and Plant Quality over Vertical Farming Systems: Bibliometric Analysis. Horticulturae 2023, 9, 63. [Google Scholar] [CrossRef]
- Lee, B.; Pham, M.D.; Hwang, H.; Jang, I.; Chun, C. Growth and morphology of ginseng seedlings cultivated in an ebb-and-flow subirrigation system as affected by cell dimension. Hortic. Sci. Technol. 2021, 39, 224–231. [Google Scholar] [CrossRef]
- Kawakatsu, T.; Fukuda, N. Dense planting and environmental control (temperature, light intensity, and concentration of nutrient solution) can increase the yield of ginseng (Panax ginseng CA Meyer) seedlings in indoor cultivation with artificial light. Hortic. Environ. Biotechnol. 2023, 64, 571–582. [Google Scholar] [CrossRef]
- Choi, J.; Kim, J.; Yoon, H.I.; Son, J.E. Effect of far-red and UV-B light on the growth and ginsenoside content of ginseng (Panax ginseng CA Meyer) sprouts aeroponically grown in plant factories. Hortic. Environ. Biotechnol. 2022, 63, 77–87. [Google Scholar] [CrossRef]
- Liao, B.; Newmark, H.; Zhou, R. Neuroprotective effects of ginseng total saponin and ginsenosides Rb1 and Rg1 on spinal cord neurons in vitro. Exp. Neurol. 2002, 173, 224–234. [Google Scholar] [CrossRef]
- Lee, J.Y.; Yang, H.; Lee, T.K.; Lee, C.H.; Seo, J.W.; Kim, J.E.; Lee, K.W. A short-term, hydroponic-culture of ginseng results in a significant increase in the anti-oxidative activity and bioactive components. Food Sci. Biotechnol. 2020, 29, 1007–1012. [Google Scholar] [CrossRef]
- Shaikh, T.A.; Rasool, T.; Lone, F.R. Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Comput. Electron. Agric. 2022, 198, 107119. [Google Scholar] [CrossRef]
- Buja, I.; Sabella, E.; Monteduro, A.G.; Chiriacò, M.S.; De Bellis, L.; Luvisi, A.; Maruccio, G. Advances in plant disease detection and monitoring: From traditional assays to in-field diagnostics. Sensors 2021, 21, 2129. [Google Scholar] [CrossRef] [PubMed]
- Cho, K.M.; Lee, H.Y.; Cho, D.Y.; Jung, J.G.; Kim, M.J.; Jeong, J.B.; Son, K.H. Comprehensive Comparison of Chemical Composition and Antioxidant Activity of Panax ginseng Sprouts by Different Cultivation Systems in a Plant Factory. Plants 2022, 11, 1818. [Google Scholar] [CrossRef] [PubMed]
- Suthaparan, A.; Torre, S.; Stensvand, A.; Herrero, M.L.; Pettersen, R.I.; Gadoury, D.M.; Gislerød, H.R. Specific light-emitting diodes can suppress sporulation of Podosphaera pannosa on greenhouse roses. Plant Dis. 2010, 94, 1105–1110. [Google Scholar] [CrossRef] [PubMed]
- Singh, S.K.; Vidyarthi, S.K.; Tiwari, R. Machine learnt image processing to predict weight and size of rice kernels. J. Food Eng. 2020, 274, 109828. [Google Scholar] [CrossRef]
- Jayapal, P.K.; Park, E.; Faqeerzada, M.A.; Kim, Y.S.; Kim, H.; Baek, I.; Cho, B.K. Analysis of RGB plant images to identify root rot disease in Korean Ginseng plants using deep learning. Appl. Sci. 2022, 12, 2489. [Google Scholar] [CrossRef]
- Hu, M.H.; Ao, Y.S.; Yang, X.E.; Li, T.Q. Treating eutrophic water for nutrient reduction using an aquatic macrophyte (Ipomoea aquatica Forsskal) in a deep flow technique system. Agric. Water Manag. 2008, 95, 607–615. [Google Scholar] [CrossRef]
- Rural Development Administration (RDA). Agricultural Science and Technology Research Standard; RDA Press: Suwon, Republic of Korea, 2012; pp. 1–1135. ISBN 978-89-480-1649-9 93520. [Google Scholar]
- Nurhasan, U.; Prasetyo, A.; Lazuardi, G.; Rohadi, E.; Pradibta, H. Implementation IoT in system monitoring hydroponic plant water circulation and control. Int. J. Eng. Technol 2018, 7, 122. [Google Scholar] [CrossRef]
- Azarmdel, H.; Jahanbakhshi, A.; Mohtasebi, S.S.; Muñoz, A.R. Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM). Postharvest Biol. Technol. 2020, 166, 111201. [Google Scholar] [CrossRef]
- Lalabadi, H.M.; Sadeghi, M.; Mireei, S.A. Fish freshness categorization from eyes and gills color features using multi-class artificial neural network and support vector machines. Aquac. Eng. 2020, 90, 102076. [Google Scholar] [CrossRef]
- Farid, D.M.; Zhang, L.; Rahman, C.M.; Hossain, M.A.; Strachan, R. Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Syst. Appl. 2014, 41, 1937–1946. [Google Scholar] [CrossRef]
- Jhansi, G.; Sujatha, K. HRFSVM: Identification of fish disease using hybrid Random Forest and Support Vector Machine. Environ. Monit. Assess. 2023, 195, 918. [Google Scholar] [CrossRef]
- Ullah, I.; Liu, K.; Yamamoto, T.; Shafiullah, M.; Jamal, A. Grey wolf optimizer-based machine learning algorithm to predict electric vehicle charging duration time. Transp. Lett. 2022, 15, 889–906. [Google Scholar] [CrossRef]
- Chand, A.A.; Prasad, K.A.; Mar, E.; Dakai, S.; Mamun, K.A.; Islam, F.R.; Kumar, N.M. Design and analysis of photovoltaic powered battery-operated computer vision-based multi-purpose smart farming robot. Agronomy 2021, 11, 530. [Google Scholar] [CrossRef]
Germination Classification | ||||
---|---|---|---|---|
Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) | |
Week 1 | 99.13 ± 0.28 | 99.77 ± 0.27 | 99.08 ± 0.61 | 99.42 ± 0.20 |
Week 2 | 99.82 ± 0.35 | 99.89 ± 0.20 | 99.89 ± 0.20 | 99.90 ± 0.20 |
Week 3 | 99.65 ± 0.51 | 99.69 ± 0.41 | 99.89 ± 0.21 | 99.79 ± 0.31 |
Rottenness Classification | ||||
---|---|---|---|---|
Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) | |
Week 1 | 98.96 ± 0.52 | 87.16 ± 11.07 | 81.5 ± 19.21 | 83.47 ± 14.04 |
Week 2 | 98.78 ± 0.75 | 94.50 ± 3.56 | 98.31 ± 2.32 | 96.32 ± 2.10 |
Week 3 | 99.39 ± 0.58 | 99.12 ± 0.72 | 98.83 ± 1.43 | 98.97 ± 1.00 |
Number of Leaf Prediction | |||
---|---|---|---|
nRMSE | MAE | R | |
Week 1 | 0.27 ± 0.03 | 0.79 ± 0.07 | 0.96 ± 0.01 |
Week 2 | 0.20 ± 0.04 | 0.76 ± 0.16 | 0.98 ± 0.01 |
Week 3 | 0.18 ± 0.05 | 0.78 ± 0.23 | 0.98 ± 0.01 |
Length of Stem Prediction | |||
---|---|---|---|
nRMSE | MAE | R | |
Week 1 | 0.006 ± 0.004 | 0.014 ± 0.009 | 0.99 ± 0.00 |
Week 2 | 0.03 ± 0.002 | 0.05 ± 0.03 | 0.99 ± 0.00 |
Week 3 | 0.02 ± 0.01 | 0.04 ± 0.01 | 0.99 ± 0.00 |
Sensor-Based Input Feature List | Growth Measured Input Feature List | ||
---|---|---|---|
1 | Average temperature | 1 | TGS nutrient solution type |
2 | Maximum temperature | 2 | Ginseng sprout year |
3 | Minimum temperature | 3 | Total length |
4 | Average water temperature | 4 | Ginseng sprout root length |
5 | Maximum water temperature | 5 | Ginseng sprout root diameter |
6 | Minimum water temperature | 6 | Ginseng sprout growth week |
7 | Average humidity | 1st stage ML result and 2nd stage ML input feature | |
8 | Maximum humidity | 1 | Germination result |
9 | Minimum humidity | 2 | Rottenness result |
10 | Average pH | 2nd stage ML input feature | |
11 | Maximum pH | 1 | Number of leaves |
12 | Minimum pH | 2 | Length of stem |
13 | Average EC | ||
14 | Maximum EC | ||
15 | Minimum EC | ||
16 | Average DO | ||
17 | Maximum DO | ||
18 | Minimum DO |
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Kim, T.H.; Baek, S.; Kwon, K.H.; Oh, S.E. Hierarchical Machine Learning-Based Growth Prediction Model of Panax ginseng Sprouts in a Hydroponic Environment. Plants 2023, 12, 3867. https://doi.org/10.3390/plants12223867
Kim TH, Baek S, Kwon KH, Oh SE. Hierarchical Machine Learning-Based Growth Prediction Model of Panax ginseng Sprouts in a Hydroponic Environment. Plants. 2023; 12(22):3867. https://doi.org/10.3390/plants12223867
Chicago/Turabian StyleKim, Tae Hyong, Seunghoon Baek, Ki Hyun Kwon, and Seung Eel Oh. 2023. "Hierarchical Machine Learning-Based Growth Prediction Model of Panax ginseng Sprouts in a Hydroponic Environment" Plants 12, no. 22: 3867. https://doi.org/10.3390/plants12223867