Design and Analysis of Electrical Conductivity Sensor for Water Quality Management in Sustainable Agriculture

Water quality management is essential for sustainable agriculture, environmental protection, and public health. Among various water quality parameters, electrical conductivity (EC) is a key indicator of dissolved ions and nutrient concentration, making it indispensable for irrigation management, fertigation, hydroponics, and water resource monitoring. This study presents the design and development of a low-cost Internet of Things (IoT)-enabled EC sensor for modern fertigation systems. Unlike many existing low-cost EC sensors that often suffer from poor accuracy and inconsistent performance, the proposed system is designed to provide accurate, reliable, and real-time water quality monitoring. The developed system integrates a total dissolved solids (TDS) and EC sensor probe, a DS18B20 temperature sensor for temperature compensation, an ESP32 microcontroller, electronic circuits and an IoT cloud platform for remote monitoring and data visualization. The sensor was experimentally validated using standard conductivity solutions and benchmarked against a commercial-grade Hanna Instruments EC meter. Experimental results demonstrate excellent agreement with the existing TDS product and reference instrument, producing accurate EC measurements and a strong correlation between TDS and EC values. The proposed IoT-enabled EC sensor provides a practical and cost-effective solution for continuous water quality monitoring in precision agriculture and environmental applications. Furthermore, this work supports the United Nations Sustainable Development Goals (SDGs) by contributing to SDG 6 (Clean Water and Sanitation) through improved water quality monitoring, SDG 9 (Industry, Innovation and Infrastructure) through the use of IoT platform, and SDG 12 (Responsible Consumption and Production) by providing potential efficient water and nutrient management for more sustainable agricultural practices.

Electrical Conductivity, Internet of Things, Fertigation, Sensor Calibration, Precision Agriculture, Water Monitoring

1. Paul, E., Joyeeta, G., & Piere, B. (2019). Global Environment Outlook Geo-6 Healthy Planet, Healthy People. Cambridge University Press. [Google Scholar] [Crossref]

2. Sanusi, I.O., Bakare, M.S. Predicting electrical conductivity in groundwater and surface water using heavy metals and physicochemical indicators with ANFIS model. Discov Appl Sci 8, 617 (2026). [Google Scholar] [Crossref]

3. Fakrulradzi, I., Anas, A.L., Muhammad, A., B, Yogeswaran, L, Zulkarami, B. IoT-based fertigation system for agriculture. (2024). Bulletin of Electrical Engineering and Informatics. vol. 13, No. 3, pp. 1574-1581. [Google Scholar] [Crossref]

4. Razman, N. A., Wan Ismail, W. Z., Abd Razak, M. H., Ismail, I., & Jamaludin, J. (2022). Design and analysis of water quality monitoring and filtration systems for different types of water in Malaysia. International Journal of Environmental Science and Technology, 20(4), 3789–3800. [Google Scholar] [Crossref]

5. Bembnowicz, P., Brom-Verheijden, G., Boonen, T., & Philips, N. (2023). Water quality sensors—from transducer technology to environmental application. IEEE Transactions on Instrumentation and Measurement, 72, 1–10. [Google Scholar] [Crossref]

6. Visco, G., Dell’Aglio, E., Tomassetti, M., Fontanella, L. U., & Sammartino, M. P. (2023). An open-source, low-cost apparatus for conductivity measurements based on Arduino coupled to a handmade cell. Analytica, 4(2), 217–230 [Google Scholar] [Crossref]

7. Fulton SG, Stegen JC, Kaufman MH, Dowd J, Thompson A (2023) Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare? PLoS ONE 18(5). [Google Scholar] [Crossref]

8. Incrocci, L., Massa, D., & Pardossi, A. (2017). New Trends in the Fertigation Management of Irrigated Vegetable Crops. Horticulturae, 3(2), 37. [Google Scholar] [Crossref]

9. Lin, Y.-B., & Lin, Y.-W. (2022). SensorTalk: Extending the life of redundant electrical conductivity sensors. IEEE Internet of Things Journal, 9(17), 16619–16630. [Google Scholar] [Crossref]

10. Wu, Y., Li, L., Li, S., Wang, H., Zhang, M., Sun, H., Sygrimis, N., & Li, M. (2019). Optimal Control Algorithm of Fertigation system in greenhouse based on EC model. International Journal of Agricultural and Biological Engineering, 12(3), 118–125. [Google Scholar] [Crossref]

11. Lin, Y.-B., & Tseng, H.-C. (2019). FishTalk: An IOT-based Mini Aquarium System. IEEE Access, 7, 35457–35469. [Google Scholar] [Crossref]

12. Sodini, M., Cacini, S., Navarro, A., Traversari, S., & Massa, D. (2023). Estimating Pore-Water Electrical Conductivity in Soilless Tomatoes Cultivation Using an Interpretable Machine Learning Model. https://doi.org/10.2139/ssrn.4511077 [Google Scholar] [Crossref]

13. Jenal, M., Nasirin, H. Z., Mohd Razali @ Kamaruddin, N. A., Sayed Mohd Albakir, S. A., Rosmadi, U. S., & Z. Ahmad Rosly, Z. A. (2021). Automated Irrigation and Fertigation System applying sensing technology. Journal of Electronic Voltage and Application, 2(2), 84-91. [Google Scholar] [Crossref]

14. Ardiansah, I., Calibra, R. G., Bafdal, N., Bono, A., Suryadi, E., & Nurhasanah, S. (2023). An IOT-enabled design for real-time water quality monitoring and control of Greenhouse Irrigation Systems. INMATEH Agricultural Engineering, 417–426. [Google Scholar] [Crossref]

15. McCleskey, R. Blaine, et al. “A New Method of Calculating Electrical Conductivity with Applications to Natural Waters.” Geochimica et Cosmochimica Acta, vol. 77, Jan. 2012, pp. 369–382, [Google Scholar] [Crossref]

16. Leon, F.A., et al. “Low-Cost Continuous Measurement System to Learn the Relationship between Electrical Conductivity and Temperature in Brackish Waters.” Desalination and Water Treatment, vol. 225, June 2021, pp. 356–363. [Google Scholar] [Crossref]

17. Adjovu, G. E., Stephen, H., James, D., & Ahmad, S. (2023). Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques. Remote Sensing, 15(14), 3534 [Google Scholar] [Crossref]

18. Siosemarde, M., Kave, F., Pazira, E., Sedghi, H., & Ghaderi, S. J. (2010). Determine constant coefficients to relate total dissolved solids to electrical conductivity. World Academy of Science, Engineering and Technology, 46, 258–260. [Google Scholar] [Crossref]

19. Thirumalini, S., & Joseph, K. (2009). Correlation between electrical conductivity and total dissolved solids in natural waters. Malaysian Journal of Science, 28(1), 55–61. [Google Scholar] [Crossref]

20. DFRobot. (n.d.). Gravity: Analog TDS Sensor/Meter for Arduino (SKU: SEN0244). Retrieved July 2, 2026. [Google Scholar] [Crossref]

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