Smart hydroponic systems have been introduced to allow farmers to monitor their hydroponic system conditions anywhere and anytime using Internet of Things (IoT) technology. Several sensors are installed on the system, such as Total Dissolved Solids (TDS), nutrient level, and temperature sensors. These sensors must be calibrated to ensure correct and accurate readings. Currently, calibration of a TDS sensor is only possible at one or a very small range of TDS values due to the very limited measurement range of the sensor. Because of this, we propose a TDS sensor calibration method called Sectioned-Polynomial Regression (Sec-PR). The main aim is to extend the measurement range of the TDS sensor and still provide a good accuracy of the sensor reading. Sec-PR computes the polynomial regression line that fits into the TDS sensor values. Then, it divides the regression line into several sections. Sec-PR calculates the average ratio between the polynomial regressed TDS sensor values and the TDS meter in each section. These average ratio values map the TDS sensor reading to the TDS meter. The performance of Sec-PR was determined using mathematical analysis and verified using experiments. The finding shows that Sec-PR provides a good calibration accuracy of about 91% when compared to the uncalibrated TDS sensor reading of just 78% with Mean Average Error (MAE) and Root Mean Square Error (RMSE) equal to 59.36 and 93.69 respectively. Sec-PR provides a comparable performance with Machine Learning and Multilayer Perception method.

• Alipio, M. I., Cruz, A. E. M. dela, Doria, J. D. A., & Fruto, R. M. S. (2019). On the design of Nutrient Film Technique hydroponics farm for smart agriculture. Engineering in Agriculture, Environment and Food, 12(3), 315-324. https://doi.org/10.1016/j.eaef.2019.02.008

• Daud, M., Handika, V., & Bintoro, A. (2018). Design and realization of fuzzy logic control for Ebb and flow hydroponic system. International Journal of Scientific and Technology Research, 7(9), 138-144.

• Domingues, D. S., Takahashi, H. W., Camara, C. A. P., & Nixdorf, S. L. (2012). Automated system developed to control pH and concentration of nutrient solution evaluated in hydroponic lettuce production. Computers and Electronics in Agriculture, 84, 53-61. https://doi.org/10.1016/j.compag.2012.02.006

• Dubey, N., & Nain, V. (2020). Hydroponic - The future of farming. International Journal of Environment, Agriculture and Biotechnology, 4(4), 857-864. https://doi.org/10.22161/ijeab.54.2

• Franchini, S., Charogiannis, A., Markides, C. N., Blunt, M. J., & Krevor, S. (2019). Calibration of astigmatic particle tracking velocimetry based on generalized Gaussian feature extraction. Advances in Water Resources, 124, 1-8. https://doi.org/10.1016/j.advwatres.2018.11.016

• Garg, K., Verma, S., & Solanki, H. A. (2021). A review on variety and variability of soil-less media for maximizing yield of greenhouse horticultural crops. Research and Reviews: Journal of Environmental Sciences, 3(1), 1-9. http://doi.org/10.5281/zenodo.4663964

• Goparaju, S. U. N., Vaddhiparthy, S. S. S., Pradeep, C., Vattem, A., & Gangadharan, D. (2021, Jun 14-July 31). Design of an IoT system for machine learning calibrated TDS measurement in smart campus. [Paper presentation]. 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), Los Angeles, USA. https://doi.org/10.1109/WF-IoT51360.2021.9595057

• Graves, C. J. (1983). The nutrient film technique. Horticultural Reviews, 5(1), 1-44.

• Hope, T. M. (2020). Machine learning. Elsevier.

• Hosseini, H., Mozafari, V., Roosta, H. R., Shirani, H., van de Vlasakker, P. C. H., & Farhangi, M. (2021). Nutrient use in vertical farming: Optimal electrical conductivity of nutrient solution for growth of lettuce and basil in hydroponic cultivation. Horticulturae, 7(9), Article 283. https://doi.org/10.3390/horticulturae7090283

• Iida, S., Shimizu, T., Shinohara, Y., Takeuchi, S., & Kumagai, T. (2020). The necessity of sensor calibration for the precise measurement of water fluxes in forest ecosystems. Forest-Water Interactions, 240, 29-54. https://doi.org/10.1007/978-3-030-26086-6_2

• Karunasingha, D. S. K. (2022). Root mean square error or mean absolute error. Information Sciences, 585, 609-629. https://doi.org/10.1016/j.ins.2021.11.036

• Koestoer, R., Pancasaputra, N., Roihan, I., & Harinaldi. (2019). A simple calibration methods of relative humidity sensor DHT22 for tropical climates based on Arduino data acquisition system. AIP Conference Proceedings, 2062(1), Article 020009. https://doi.org/10.1063/1.5086556

• Maucieri, C., Nicoletto, C., van Os, E., Anseeuw, D., van Havermaet, R., & Junge, R. (2019). Hydroponic technologies. In S. Goddek, A. Joyce, B. Kotzen & G. M. Burnell (Eds.), Aquaponics Food Production Systems (pp.77-110). Springer. https://doi.org/10.1007/978-3-030-15943-6_4

• Modu, F., Adam, A., Aliyu, F., Mabu, A., & Musa, M. (2020). A survey of smart hydroponic systems. Advances in Science, Technology and Engineering Systems Journal, 5(1), 233-248. https://doi.org/10.25046/aj050130

• Munandar, A., Fakhrurroja, H., Anto, I. F. A., Pratama, R. P., Wibowo, J. W., Salim, T. I., & Rizqyawan, M. I. (2018, November 21-22). Design and development of an IoT-based smart hydroponic system. [Paper presentation]. 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia. https://doi.org/10.1109/ISRITI.2018.8864340

• Nguyen, T. D., Nguyen, T. T. S., & Le, N. T. (2018, October 18-20). Calibration of conductivity sensor using combined algorithm selection and hyperparameter optimization. [Paper presentation]. 2018 International Conference on Advanced Technologies for Communications (ATC), Ho Chi Minh City, Vietnam. https://doi.org/10.1109/ATC.2018.8587559

• Olubanjo, O. O., Adaramola, O. D., Alade, A. E., Azubuike, C. J., & others. (2022). Development of drip flow technique hydroponic in growing cucumber. Sustainable Agriculture Research, 11(2), 1-67. https://doi.org/https://doi.org/10.5539/sar.v11n2p67

• Peršić, J., Petrović, L., Marković, I., & Petrović, I. (2021). Spatiotemporal multisensor calibration via gaussian processes moving target tracking. IEEE Transactions on Robotics, 37(5), 1401-1415. https://doi.org/10.1109/TRO.2021.3061364

• Pramono, S., Nuruddin, A., & Ibrahim, M. H. (2020). Design of a hydroponic monitoring system with deep flow technique (DFT). AIP Conference Proceedings, 2217(1), Article 030195. https://doi.org/10.1063/5.0000733

• Singh, H., & Dunn, B. (2016). Electrical conductivity and pH guide for hydroponics (HLA-6722). Division of Agriculture Science and Natural Resources Oklahoma State University. https://shareok.org/bitstream/handle/11244/331022/oksa_HLA-6722_2016-10.pdf

• Son, J. E., Kim, H. J., & Ahn, T. I. (2020). Hydroponic systems. In T. Kozai, G. Niu & M. Takagaki (Eds.), Plant Factory (pp.273-283). Academic Press. https://doi.org/10.1016/B978-0-12-816691-8.00020-0

• Suseno, J., Munandar, M., & Priyono, A. (2020). The control system for the nutrition concentration of hydroponic using web server. Journal of Physics: Conference Series, 1524(1), Article 012068. https://doi.org/10.1088/1742-6596/1524/1/012068

• Urban, S., Ludersdorfer, M., & van der Smagt, P. (2015). Sensor calibration and hysteresis compensation with heteroscedastic gaussian processes. IEEE Sensors Journal, 15(11), 6498-6506. https://doi.org/10.1109/JSEN.2015.2455814

• Wibowo, R. R. D. I., Ramdhani, M., Priramadhi, R. A., & Aprillia, B. S. (2019). IoT based automatic monitoring system for water nutrition on aquaponics system. Journal of Physics: Conference Series, 1367(1), Article 012071. https://doi.org/10.1088/1742-6596/1367/1/012071

• Zheng, Q., Weng, Q., & Wang, K. (2019). Developing a new cross-sensor calibration model for DMSP-OLS and Suomi-NPP VIIRS night-light imageries. ISPRS Journal of Photogrammetry and Remote Sensing, 153, 36-47. https://doi.org/10.1016/j.isprsjprs.2019.04.019