Application of a Hybrid Model for Data Analysis in Hydroponic Systems
This study presents a hybrid data analysis approach to optimize the growing conditions for beetroot and tarragon microgreens cultivated in hydroponic systems. Maintaining precise microclimate control is essential, as even minor deviations can significantly affect the yield and product quality, but t...
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MDPI AG
2025-04-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/5/166 |
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| author | Kuanysh Bakirov Jamalbek Tussupov Akhmet Tussupov Ibraheem Shayea Aruzhan Shoman |
| author_facet | Kuanysh Bakirov Jamalbek Tussupov Akhmet Tussupov Ibraheem Shayea Aruzhan Shoman |
| author_sort | Kuanysh Bakirov |
| collection | DOAJ |
| description | This study presents a hybrid data analysis approach to optimize the growing conditions for beetroot and tarragon microgreens cultivated in hydroponic systems. Maintaining precise microclimate control is essential, as even minor deviations can significantly affect the yield and product quality, but traditional monitoring methods fail to adapt promptly to changing conditions. To overcome this limitation, an automated monitoring system integrating machine learning methods XGBoost 3.0.0, principal component analysis (PCA), and fuzzy logic was developed. The model continuously identifies the deviations in environmental parameters and recommends corrective actions to stabilize the growth conditions. Experimental evaluation demonstrated superior predictive performance by using XGBoost, achieving an accuracy and F1-score of 97.88%, ROC-AUC of 99.99%, and computational efficiency (training completed in 2.3 s), outperforming RandomForest and GradientBoosting algorithms. Real-time data collection was facilitated through IoT sensors transmitting readings via Wi-Fi every 5 s to a local server, accumulating approximately 17,280 records per day. The analysis highlighted air humidity, solution humidity, and temperature as critical influencing factors. This research confirms the developed system’s effectiveness in intelligent hydroponic monitoring, with future work aimed at integrating IoT and IIoT technologies for scalable management across diverse crops. |
| format | Article |
| id | doaj-art-152f2aba6d464f60ba611865dcc0aedd |
| institution | DOAJ |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-152f2aba6d464f60ba611865dcc0aedd2025-08-20T03:12:07ZengMDPI AGTechnologies2227-70802025-04-0113516610.3390/technologies13050166Application of a Hybrid Model for Data Analysis in Hydroponic SystemsKuanysh Bakirov0Jamalbek Tussupov1Akhmet Tussupov2Ibraheem Shayea3Aruzhan Shoman4Faculty of Information Technology, L. N. Gumilyov Eurasian National University, Astana 010000, KazakhstanFaculty of Information Technology, L. N. Gumilyov Eurasian National University, Astana 010000, KazakhstanResearch and Innovation Center “AgroTech”, Astana IT University, Astana 010000, KazakhstanElectronics and Communication Engineering Department, Faculty of Electrical and Electronics Engineering, Istanbul Technical University, Sarıyer 34467, TurkeyResearch and Innovation Center “AgroTech”, Astana IT University, Astana 010000, KazakhstanThis study presents a hybrid data analysis approach to optimize the growing conditions for beetroot and tarragon microgreens cultivated in hydroponic systems. Maintaining precise microclimate control is essential, as even minor deviations can significantly affect the yield and product quality, but traditional monitoring methods fail to adapt promptly to changing conditions. To overcome this limitation, an automated monitoring system integrating machine learning methods XGBoost 3.0.0, principal component analysis (PCA), and fuzzy logic was developed. The model continuously identifies the deviations in environmental parameters and recommends corrective actions to stabilize the growth conditions. Experimental evaluation demonstrated superior predictive performance by using XGBoost, achieving an accuracy and F1-score of 97.88%, ROC-AUC of 99.99%, and computational efficiency (training completed in 2.3 s), outperforming RandomForest and GradientBoosting algorithms. Real-time data collection was facilitated through IoT sensors transmitting readings via Wi-Fi every 5 s to a local server, accumulating approximately 17,280 records per day. The analysis highlighted air humidity, solution humidity, and temperature as critical influencing factors. This research confirms the developed system’s effectiveness in intelligent hydroponic monitoring, with future work aimed at integrating IoT and IIoT technologies for scalable management across diverse crops.https://www.mdpi.com/2227-7080/13/5/166hybrid modelhydroponic systemsmicrogreens growthmachine learningfuzzy logicenvironmental parameters |
| spellingShingle | Kuanysh Bakirov Jamalbek Tussupov Akhmet Tussupov Ibraheem Shayea Aruzhan Shoman Application of a Hybrid Model for Data Analysis in Hydroponic Systems Technologies hybrid model hydroponic systems microgreens growth machine learning fuzzy logic environmental parameters |
| title | Application of a Hybrid Model for Data Analysis in Hydroponic Systems |
| title_full | Application of a Hybrid Model for Data Analysis in Hydroponic Systems |
| title_fullStr | Application of a Hybrid Model for Data Analysis in Hydroponic Systems |
| title_full_unstemmed | Application of a Hybrid Model for Data Analysis in Hydroponic Systems |
| title_short | Application of a Hybrid Model for Data Analysis in Hydroponic Systems |
| title_sort | application of a hybrid model for data analysis in hydroponic systems |
| topic | hybrid model hydroponic systems microgreens growth machine learning fuzzy logic environmental parameters |
| url | https://www.mdpi.com/2227-7080/13/5/166 |
| work_keys_str_mv | AT kuanyshbakirov applicationofahybridmodelfordataanalysisinhydroponicsystems AT jamalbektussupov applicationofahybridmodelfordataanalysisinhydroponicsystems AT akhmettussupov applicationofahybridmodelfordataanalysisinhydroponicsystems AT ibraheemshayea applicationofahybridmodelfordataanalysisinhydroponicsystems AT aruzhanshoman applicationofahybridmodelfordataanalysisinhydroponicsystems |