Water Quality Monitoring for Smart Farming Using Machine Learning Approach

  • Yana Hendriana Informatics Department, Universitas Nahdlatul Ulama Yogyakarta
  • Restiadi Bayu Taruno Informatics Department, Universitas Nahdlatul Ulama Yogyakarta
  • Zulkhairi Zulkhairi Electrical Engineering Department, Universitas Nahdlatul Ulama Yogyakarta
  • Nur Azmi Ainul Bashir Computer Engineering Department, Universitas Nahdlatul Ulama Yogyakarta
  • Joang Ipmawati Computer Engineering Department, Universitas Nahdlatul Ulama Yogyakarta
  • Ilham Unggara Computer Engineering Department, Universitas Nahdlatul Ulama Yogyakarta
Abstract views: 193 , PDF downloads: 101
Keywords: Lintangsongo, Fish Pond, XGBoost, Recursive Feature eElimination (RFE), Closed Loop


Water quality in fish farming environments has been a topic of research investigation for numerous years. While most studies have concentrated on managing water quality in fish ponds, there is a lack of research on implementing these practices on a commercial scale. Maintaining good water quality helps prevent disease, stress, and death in fish, resulting in higher yields and profits in fish farming operations. In our study, we gathered weekly data from two fish ponds in the Lintangsongo smart farming area over six months. To deal with the limited dataset, we utilized methods for reducing dimensionality, like the pairwise comparison of correlation matrices to eliminate the highest correlated predictors. We used techniques of feature selection, including XGBoost classification, and apart from that, we used Recursive Feature Elimination (RFE) to determine the importance of features. This analysis identified ammonium and calcium as the top two predictors. These nutrients played a vital role in maintaining the paired cultivation system and promoting the robust development of Nile tilapia fish and water spinach. This process of detecting and distributing nutrients persists until the desired quantities of ammonium and calcium are reached. During each cycle, 0.7 g of ammonium sulfate and calcium nitrate are distributed, and the nutrient levels are assessed. Vernier sensors were employed for assessing nutrient values, and a system of actuators was integrated to supply the necessary nutrients to the smart farming environment using the closed-loop concept. This research investigates water quality management practices in fish farming, assesses their impact on fish health and profitability, identifies key water quality predictors, and implements a closed-loop system for nutrient delivery.


Download data is not yet available.

Author Biography

Restiadi Bayu Taruno, Informatics Department, Universitas Nahdlatul Ulama Yogyakarta

Informatics Department


R. B. Taruno, I. Unggara, J. Ipmawati, Y. Hendriana, N. A. A. Bashir, and Z. Zulkhairi, “Pemanfaatan Energi Baru Terbarukan Smart Farming System dalam Peningkatan Hasil Pertanian dan Perikanan,” Berdikari: Jurnal Inovasi dan Penerapan Ipteks, vol. 11, no. 1, 2023, doi: 10.18196/berdikari.v11i1.16972.

J. L. Portinho et al., "The pathways influence of agricultural expansion on water quality of fish farming in Ilha Solteira reservoir, São Paulo, Brazil," Aquaculture, vol. 536, 2021, doi: 10.1016/j.aquaculture.2021.736405.

C. Chen, Y. Song, and Y. Yuan, "The operating characteristics of partial nitrification by controlling Ph and alkalinity," Water (Switzerland), vol. 13, no. 3, 2021, doi: 10.3390/w13030286.

S. M. S. Nogueira, J. S. Junior, H. D. Maia, J. P. S. Saboya, and W. R. L. Farias, "Use of Spirulina platensis in treatment of fish farming wastewater," Revista Ciencia Agronomica, vol. 49, no. 4, 2018, doi: 10.5935/1806-6690.20180068.

R. R. Nair, B. Rangaswamy, B. S. I. Sarojini, and V. Joseph, "Anaerobic ammonia-oxidizing bacteria in tropical bioaugmented zero water exchange aquaculture ponds," Environmental Science and Pollution Research, vol. 27, no. 10, 2020, doi: 10.1007/s11356-020-07663-1.

S. K. Parker and G. Grote, "Automation, Algorithms, and Beyond: Why Work Design Matters More Than Ever in a Digital World," Applied Psychology, 2020, doi: 10.1111/apps.12241.

J. A. Johannessen and H. Sætersdal, Automation, Innovation and Work: The Impact of Technological, Economic, and Social Singularity. 2020. doi: 10.4324/9781003032854.

Y. Hendriana and R. Hardi, "Remote control system as serial communications mobile using a microcontroller," in 2016 International Conference on Information Technology Systems and Innovation, ICITSI 2016 - Proceedings, 2017. doi: 10.1109/ICITSI.2016.7858212.

M. Krastanova, I. Sirakov, S. Ivanova-Kirilova, D. Yarkov, and P. Orozova, "Aquaponic systems: biological and technological parameters," Biotechnology and Biotechnological Equipment, vol. 36, no. 1. 2022. doi: 10.1080/13102818.2022.2074892.

M. F. Taha et al., "Recent Advances of Smart Systems and Internet of Things (IoT) for Aquaponics Automation: A Comprehensive Overview," Chemosensors, vol. 10, no. 8. 2022. doi: 10.3390/chemosensors10080303.

S. L. Ullo and G. R. Sinha, "Advances in smart environment monitoring systems using iot and sensors," Sensors (Switzerland), vol. 20, no. 11. 2020. doi: 10.3390/s20113113.

R. P. Defa, M. Ramdhani, R. A. Priramadhi, and B. S. Aprillia, "Automatic controlling system and IoT based monitoring for pH rate on the aquaponics system," in Journal of Physics: Conference Series, 2019. doi: 10.1088/1742-6596/1367/1/012072.

F. Jan, N. Min-Allah, and D. Düştegör, "Iot based smart water quality monitoring: Recent techniques, trends and challenges for domestic applications," Water (Switzerland), vol. 13, no. 13. 2021. doi: 10.3390/w13131729.

S. Pawar, S. Tembe, and S. Khan, "Design of an affordable pH module for IoT Based pH level control in hydroponics applications," in 2020 International Conference on Convergence to Digital World - Quo Vadis, ICCDW 2020, 2020. doi: 10.1109/ICCDW45521.2020.9318677.

B. Suárez-Puerto, M. Delgadillo-Díaz, M. J. Sánchez-Solís, and M. Gullian-Klanian, "Analysis of the cost-effectiveness and growth of Nile tilapia (Oreochromis niloticus) in biofloc and green water technologies during two seasons," Aquaculture, vol. 538, 2021, doi: 10.1016/j.aquaculture.2021.736534.

D. Deswati, S. Safni, K. Khairiyah, E. Yani, Y. Yusuf, and H. Pardi, "Biofloc technology: water quality (pH, temperature, DO, COD, BOD) in a flood & drain aquaponic system," Int J Environ Anal Chem, vol. 102, no. 18, 2022, doi: 10.1080/03067319.2020.1817428.

G. Soltana, M. Sabetzadeh, and L. C. Briand, "Synthetic data generation for statistical testing," in ASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, 2017. doi: 10.1109/ASE.2017.8115698.

M. Hernandez et al., "Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Well-being Domain," Electronics (Switzerland), vol. 11, no. 5, 2022, doi: 10.3390/electronics11050812.

A. Figueira and B. Vaz, "Survey on Synthetic Data Generation, Evaluation Methods and GANs," Mathematics, vol. 10, no. 15. 2022. doi: 10.3390/math10152733.

M. Alzantot, S. Chakraborty, and M. Srivastava, "SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation Project 6, Task 2 (Deep Learning for Multi-Layer Situational Understanding)," in IEEE International Conference on Pervasive Computing and Communications Workshops, 2017.

M. Gregorich, S. Strohmaier, D. Dunkler, and G. Heinze, "Regression with highly correlated predictors: Variable omission is not the solution," Int J Environ Res Public Health, vol. 18, no. 8, 2021, doi: 10.3390/ijerph18084259.

W. Zhu, C. Lévy-Leduc, and N. Ternès, "A variable selection approach for highly correlated predictors in high-dimensional genomic data," Bioinformatics, vol. 37, no. 16, 2021, doi: 10.1093/bioinformatics/btab114.

S. K. Punia, M. Kumar, T. Stephan, G. G. Deverajan, and R. Patan, "Performance analysis of machine learning algorithms for big data classification: Ml and ai-based algorithms for big data analysis," International Journal of E-Health and Medical Communications, vol. 12, no. 4, 2021, doi: 10.4018/IJEHMC.20210701.oa4.

K. Obaideen et al., "An overview of smart irrigation systems using IoT," Energy Nexus, vol. 7, 2022, doi: 10.1016/j.nexus.2022.100124.

K. E. Lakshmiprabha and C. Govindaraju, "Hydroponic-based smart irrigation system using Internet of Things," International Journal of Communication Systems, vol. 36, no. 12, 2023, doi: 10.1002/dac.4071.

Y. A. Sihombing and S. Listiari, "Detection of air temperature, humidity and soil pH by using DHT22 and pH sensor based Arduino nano microcontroller," in AIP Conference Proceedings, 2020. doi: 10.1063/5.0003115.

F. A. Purnomo, N. M. Yoeseph, S. A. T. Bawono, and R. Hartono, "Development of air temperature and soil moisture monitoring systems with LoRA technology," in Journal of Physics: Conference Series, 2021. doi: 10.1088/1742-6596/1825/1/012029.

G. Kaur, P. Upadhyaya, and P. Chawla, "Comparative analysis of IoT-based controlled environment and uncontrolled environment plant growth monitoring system for hydroponic indoor vertical farm," Environ Res, vol. 222, 2023, doi: 10.1016/j.envres.2023.115313.

R. Mittal and M. P. S. Bhatia, "Wireless sensor networks for monitoring the environmental activities," in 2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010, 2010. doi: 10.1109/ICCIC.2010.5705791.

L. Belhaj Salah and F. Fourati, "A greenhouse modeling and control using deep neural networks," Applied Artificial Intelligence, vol. 35, no. 15, 2021, doi: 10.1080/08839514.2021.1995232.

How to Cite
Hendriana, Y., Taruno, R. B., Zulkhairi, Z., Bashir, N. A. A., Ipmawati, J., & Unggara, I. (2023). Water Quality Monitoring for Smart Farming Using Machine Learning Approach. International Journal of Artificial Intelligence & Robotics (IJAIR), 5(2), 81-90. https://doi.org/10.25139/ijair.v5i2.7499