Short-Term Time-Series Forecasting of Hydroponic Water Quality Parameters Using XGBoost Based IoT Sensor Data
Abstract
Water quality monitoring is a critical factor in the success of hydroponic farming. Conventional reactive monitoring systems are often too slow to detect changes in water quality parameters such as Total Dissolved Solids (TDS), turbidity, and water level, which can lead to crop failure. This study aims to develop a short-term forecasting model using the Extreme Gradient Boosting (XGBoost) algorithm to support an early warning system. Time-series data was collected via IoT sensors over five months at 15-minute intervals. Model performance evaluation showed high accuracy across all parameters. The nutrient prediction model (TDS) achieved an R-squared value above 0.94 with MAPE below 2%, indicating excellent precision. For the turbidity parameter, the model obtained the highest R-squared value of 0.986 with MAE of only 0.72 NTU. Meanwhile, water level predictions were able to map water fluctuation dynamics with R-squared ranging from 0.85-0.92. These results prove that the XGBoost model is effective in providing accurate water condition estimates, enabling farmers to take preventive measures before a significant decline in water quality occurs within a relatively short period of time.
Downloads
References
C. R. Ramadhani, R. A. Hamzah, and A. A. Wahyudi, “Penerapan Teknologi Hidroponik Sebagai Solusi Pertanian Berkelanjutan di Lingkungan Perkotaan,” vol. 1, no. 1, 2025.
T. Alam, Z.-U.- Haq, M. A. Ahmed, and M. Ikram, “Hydroponics as an advanced vegetable production technique: an overview,” Zoo Bot., vol. 1, no. 1, pp. 29–42, Aug. 2023, doi: 10.55627/zoobotanica.001.01.0630.
W. H. Sugiharto, H. Susanto, and A. B. Prasetijo, “Real-Time Water Quality Assessment via IoT: Monitoring pH, TDS, Temperature, and Turbidity,” ISI, vol. 28, no. 4, pp. 823–831, Aug. 2023, doi: 10.18280/isi.280403.
R. Anjini, J. Jenifer, and Mrs. A. M. C. Blessy, “IoT Based Automated Hydroponics Greenhouse Monitoring,” IJARSCT, pp. 671–681, Apr. 2021, doi: 10.48175/IJARSCT-960.
A. Tzounis, N. Katsoulas, T. Bartzanas, and C. Kittas, “Internet of Things in agriculture, recent advances and future challenges,” Biosystems Engineering, vol. 164, pp. 31–48, Dec. 2017, doi: 10.1016/j.biosystemseng.2017.09.007.
Y. Hua, Z. Zhao, R. Li, X. Chen, Z. Liu, and H. Zhang, “Deep Learning with Long Short-Term Memory for Time Series Prediction,” Oct. 24, 2018, arXiv: arXiv:1810.10161. doi: 10.48550/arXiv.1810.10161.
N. Elsayed, Z. ElSayed, and A. S. Maida, “LiteLSTM Architecture for Deep Recurrent Neural Networks,” Oct. 25, 2022, arXiv: arXiv:2201.11624. doi: 10.48550/arXiv.2201.11624.
S. A. Frimpong, M. Han, W. Zheng, X. Li, E. Akpaku, and A. P. Obeng, “Machine and Deep Learning in Agricultural Engineering: A Comprehensive Survey and Meta-Analysis of Techniques, Applications, and Challenges,” Computers, vol. 14, no. 10, p. 438, Oct. 2025, doi: 10.3390/computers14100438.
M.Tech, Department of Computer Science & Engineering, SRM Institute of Science & Technology, Chennai, India., R. Ravi*, Dr. B. Baranidharan, and Associate Professor, Department of Computer Science & Engineering, SRM Institute of Science & Technology, Chennai, India., “Crop Yield Prediction using XG Boost Algorithm,” IJRTE, vol. 8, no. 5, pp. 3516–3520, Jan. 2020, doi: 10.35940/ijrte.D9547.018520.
M. Saritha, R. M. Irshath, S. Sanjay, A. V. Selvan, and M. S. Abdullah, “Real-Time Plant Disease Prediction Using XGBoost and IoT-Enabled Smart Agriculture System,” in Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025), vol. 124, J. K. Katiyar, D. S. R. Yellampalli, D. Chandra Mohan, K. K. Singh, B. Venkata Ramana, and N. Dinesh Kumar, Eds., in Advances in Computer Science Research, vol. 124. , Dordrecht: Atlantis Press International BV, 2025, pp. 120–131. doi: 10.2991/978-94-6463-858-5_12.
H. I. Beloev, S. R. Saitov, A. A. Filimonova, N. D. Chichirova, O. E. Babikov, and I. K. Iliev, “Short-Term Electrical Load Forecasting Based on XGBoost Model,” Energies, vol. 18, no. 19, p. 5144, Sep. 2025, doi: 10.3390/en18195144.
Z. Fang, S. Yang, C. Lv, S. An, and W. Wu, “Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study,” BMJ Open, vol. 12, no. 7, p. e056685, Jul. 2022, doi: 10.1136/bmjopen-2021-056685.
L. Zhang, W. Bian, W. Qu, L. Tuo, and Y. Wang, “Time series forecast of sales volume based on XGBoost,” J. Phys.: Conf. Ser., vol. 1873, no. 1, p. 012067, Apr. 2021, doi: 10.1088/1742-6596/1873/1/012067.
V. Kramar and V. Alchakov, “Time-Series Forecasting of Seasonal Data Using Machine Learning Methods,” Algorithms, vol. 16, no. 5, p. 248, May 2023, doi: 10.3390/a16050248.
A. Tawakuli, B. Havers, V. Gulisano, D. Kaiser, and T. Engel, “Survey:Time-series data preprocessing: A survey and an empirical analysis,” Journal of Engineering Research, vol. 13, no. 2, pp. 674–711, Jun. 2025, doi: 10.1016/j.jer.2024.02.018.
B. L. Ortiz et al., “Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care,” JMIR Mhealth Uhealth, vol. 12, p. e59587, Sep. 2024, doi: 10.2196/59587.
F. H. Syahadah, R. T. Subagio, and P. Rizqiyah, “PENERAPAN XGBOOST DALAM PREDIKSI PENDAFTARAN SISWA BARU BIMBINGAN BELAJAR QSC DI KOTA CIREBON,” JITET, vol. 13, no. 3S1, Oct. 2025, doi: 10.23960/jitet.v13i3S1.7998.
T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2016, pp. 785–794. doi: 10.1145/2939672.2939785.
K. Maciejowska, A. Lipiecki, and B. Uniejewski, “Statistical and economic evaluation of forecasts in electricity markets: beyond RMSE and MAE,” 2025, arXiv. doi: 10.48550/ARXIV.2511.13616.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Computer Science, vol. 7, p. e623, Jul. 2021, doi: 10.7717/peerj-cs.623.
B. G. K. Yudistira, C. Hapsari, G. D. W. Adnyana, W. Nath, I. Putu, and R. M. Putra, “Smart Fisheries: Real-Time Water Quality Management and Automated Feeding System Design for Tilapia Farming using ESP32 Micro Controller,” vol. 5, no. 2.
Z. Qinghe, X. Wen, H. Boyan, W. Jong, and F. Junlong, “Optimised extreme gradient boosting model for short term electric load demand forecasting of regional grid system,” Sci Rep, vol. 12, no. 1, p. 19282, Nov. 2022, doi: 10.1038/s41598-022-22024-3.
S. Karmaker, “Feature Engineering in Time Series Forecasting”.
V. Cerqueira, N. Moniz, and C. Soares, “VEST: Automatic Feature Engineering for Forecasting,” Oct. 14, 2020, arXiv: arXiv:2010.07137. doi: 10.48550/arXiv.2010.07137.
C. S. Bojer, “Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities,” International Journal of Forecasting, vol. 38, no. 4, pp. 1555–1561, Oct. 2022, doi: 10.1016/j.ijforecast.2021.11.003.
A. De Myttenaere, B. Golden, B. Le Grand, and F. Rossi, “Mean Absolute Percentage Error for regression models,” Neurocomputing, vol. 192, pp. 38–48, Jun. 2016, doi: 10.1016/j.neucom.2015.12.114.
H. Y. Teh, A. W. Kempa-Liehr, and K. I.-K. Wang, “Sensor data quality: a systematic review,” J Big Data, vol. 7, no. 1, p. 11, Dec. 2020, doi: 10.1186/s40537-020-0285-1.
C. Tamilselvi, M. Yeasin, R. K. Paul, and A. K. Paul, “Can Denoising Enhance Prediction Accuracy of Learning Models? A Case of Wavelet Decomposition Approach,” Forecasting, vol. 6, no. 1, pp. 81–99, Jan. 2024, doi: 10.3390/forecast6010005.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
