
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Comparison of Nutrient and pH Control in NFT Hydroponic Plants for Coupled and Decoupled Methods
Corresponding Author(s) : Ina Rahmawati Putri
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 2, May 2026 (Article in Progress)
Abstract
PH and TDS were critical parameters in hydroponic systems that directly influenced nutrient absorption and plant growth. This study developed an automatic nutrient solution control system for NFT hydroponics using a Proportional-Integral-Derivative (PID) controller with coupled and decoupled approaches. The system employed a DFRobot Gravity: Analog TDS sensor to measure TDS, an Electrode Probe pH-4502C to monitor pH, and an Arduino Uno microcontroller to regulate peristaltic pumps in real time. Lettuce was used as the test crop, requiring 550 ppm TDS and pH 6.5. System performance was evaluated through MATLAB Simulink simulations and hardware implementation based on rise time, settling time, overshoot, and steady-state error. The simulation results showed that the coupled method had slightly faster rise time and settling time compared to the decoupled method, whereas the decoupled method had less overshoot than the coupled. The hardware test showed that the decoupled method performed better, with a pH rise time of 8.34 s, a settling time of 11 s, an overshoot of 10%, and a steady-state error of 0.90%, as well as a TDS rise time of 30.7 s, a settling time of 36 s, an overshoot of 4.36%, and a steady-state error of 0.60%. In contrast, the coupled method exhibited slower responses, longer settling times, and higher steady-state errors. Overall, the decoupled method proved more effective and reliable in maintaining pH and TDS stability, showing strong potential to enhance the efficiency and robustness of NFT hydroponic control systems.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- N. D. Rahayu, B. Sasmito, and N. Bashit, “Analisis Pengaruh Fenomena Indian Ocean Dipole (IOD) Terhadap Curah Hujan Di Pulau Jawa,” J. Geod. Undip, vol. 7, no. 1, pp. 57–67, 2018.
- B. P. Nasional, “Gugah Kesadaran Masyarakat Pentingnya Sayur dan Buah Demi Capai Pola Makan Bergizi Seimbang,” Badan Pangan Nasional. Accessed: Jun. 22, 2025. [Online]. Available: https://badanpangan.go.id/blog/post/gugah-kesadaran-masyarakat-pentingnya-sayur-dan-buah-demi-capai-pola-makan-bergizi-seimbang
- A. Aditianti, S. Prihatini, and H. Hermina, “Pengetahuan, Sikap dan Perilaku Individu Tentang Makanan Beraneka Ragam sebagai Salah Satu Indikator Keluarga Sadar Gizi (KADARZI),” Bul. Penelit. Kesehat., vol. 44, no. 2, pp. 117–126, 2016, doi: 10.22435/bpk.v44i2.5455.117-126.
- A. Ullah, S. Aktar, N. Sutar, R. Kabir, and A. Hossain, “Cost Effective Smart Hydroponic Monitoring and Controlling System Using IoT,” Intell. Control Autom., vol. 10, no. 04, pp. 142–154, 2019, doi: 10.4236/ica.2019.104010.
- S. Wahyuni, M. Wahyudi, and A. Rusidy, “Rekayasa Digitalisasi Pertanian Hidroponik NFT dengan Model Kendali Suhu, pH dan Electrical Conductivity (EC),” Rekayasa, vol. 14, no. 1, pp. 68–77, 2021, doi: 10.21107/rekayasa.v14i1.9217.
- S. M. Wirawati and S. N. Arthawati, “Meningkatan Pendapatan Masyarakat Melalui Budidaya Tanaman Sawi Dengan Metode Hidroponik Di Desa Pelawad Kecamatan Ciruas,” ABDIKARYA J. Pengabdi. dan Pemberdaya. Masy., vol. 3, no. 1, pp. 1–9, 2021, doi: 10.47080/abdikarya.v3i1.1151.
- R. Deshintia, A. Mufti, and F. Heltha, “Penerapan Logika Fuzzy Pada Pengontrolan Larutan Nutrisi Tanaman Selada Menggunakan Hidroponik Nutrient Film Technique,” vol. 9, no. 1, pp. 1–6, 2024.
- N. Surantha and V. Vincentdo, “NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference System,” 2022 2nd Int. Conf. Robot. Autom. Artif. Intell. RAAI 2022, pp. 118–123, 2022, doi: 10.1109/RAAI56146.2022.10092958.
- Fitriani, Z. Zainuddin, and Syafaruddin, “Nutrition Control System In Nutrient Film Technique (NFT) Hydroponics With Convolutional Neural Network (CNN) Method,” Proc. - ISMODE 2022 2nd Int. Semin. Mach. Learn. Optim. Data Sci., pp. 41–46, 2022, doi: 10.1109/ISMODE56940.2022.10180412.
- S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” Proc. 2017 Int. Conf. Eng. Technol. ICET 2017, vol. 2018-Janua, pp. 1–6, 2017, doi: 10.1109/ICEngTechnol.2017.8308186.
- A. F. Zrigan, A. J. Abougarair, M. K. Elmezughi, and A. M. Almaktoof, “Optimized PID Controller and Generalized Inverted Decoupling Design for MIMO System,” Proc. 2023 IEEE Int. Conf. Adv. Syst. Emergent Technol. IC_ASET 2023, pp. 1–6, 2023, doi: 10.1109/IC_ASET58101.2023.10150957.
- L. Liu, S. Tian, D. Xue, T. Zhang, Y. Chen, and S. Zhang, “A Review of Industrial MIMO Decoupling Control, Automation and Systems,” Int. J. Control, vol. 17, no. X, pp. 1–9, 2019.
- R. Li, Q. Kong, J. Ma, and K. Liang, “Decoupling Control Method of Temperature and Humidity in Long Storage Environment Based on Particle Swarm Optimization PID Algorithm,” Proc. - 2023 Int. Conf. Adv. Electr. Eng. Comput. Appl. AEECA 2023, pp. 902–907, 2023, doi: 10.1109/AEECA59734.2023.00164.
- K. Liu, Y. Liu, F. Song, C. Zhang, W. Li, and Z. Wang, “Data Driven Dynamic Decoupling Control for MIMO Precision Mechatronic Systems,” Proc. 2024 IEEE 13th Data Driven Control Learn. Syst. Conf. DDCLS 2024, pp. 1305–1310, 2024, doi: 10.1109/DDCLS61622.2024.10606678.
- C. G. Proudfoot, “Principles and practice of automatic process control. Carlos A. Smith and Armando B. Corripio,” Automatica, vol. 23, no. 3, p. 414, 1987, doi: 10.1016/0005-1098(87)90018-5.
- S. A. Aessa, S. W. Shneen, and M. K. Oudah, “Optimizing PID Controller for Large-Scale MIMO Systems Using Flower Pollination Algorithm,” J. Robot. Control, vol. 6, no. 2, pp. 553–559, 2025, doi: 10.18196/jrc.v6i2.24409.
- Ogata, Teknik Kontrol Automatik. Jakarta: Erlangga, 1996.
- D. S. Bhandare, N. R. Kulkarni, and M. V. Bakshi, “Linearization of a Coupled tank MIMO system and its validation using MATLAB,” 2021 6th Int. Conf. Converg. Technol. I2CT 2021, pp. 1–5, 2021, doi: 10.1109/I2CT51068.2021.9417875.
- K. Jangala, M. Rathaiah, R. Kiranmayi, P. Bharat Kumar, K. Nagabhushanam, and N. Swathi, “Improved Fractional Filter IMC-PID Controller Design for SISO and MIMO Processes,” 2023 5th Int. Conf. Electr. Comput. Commun. Technol. ICECCT 2023, pp. 1–5, 2023, doi: 10.1109/ICECCT56650.2023.10179712.
- J. M. Daif-Alkhasraji, S. W. Shneen, and M. Q. Sulttan, “Reduction of Large Scale Linear Dynamic MIMO Systems Using ACO-PID Controller,” Ing. e Investig., vol. 44, no. 1, pp. 1–7, 2024, doi: 10.15446/ing.investig.106657.
- G. Sitaramu, L. Dutta, and D. Kumar Das, “To Design Sigmoid Based PID Controller for Twin Rotor MIMO System,” 2023 2nd IEEE Int. Conf. Meas. Instrumentation, Control Autom. ICMICA 2023, pp. 1–5, 2024, doi: 10.1109/ICMICA61068.2024.10732610.
- A. Dubravic, D. Demirovic, and A. Serifovic-Trbalic, “Optimization of PID Controller Using PSO Algorithm for a First Order Plus Dead Time (FOPDT) Process -A Simulation Study,” Int. Conf. Electr. Comput. Energy Technol. ICECET 2022, no. July, pp. 1–4, 2022, doi: 10.1109/ICECET55527.2022.9872631.
- P. Kumar, V. Kumar, and B. Tyagi, “Experimental Validation of PI Controllers and Modelling of DC Servo Motor by FOPDT Model,” PESGRE 2022 - IEEE Int. Conf. “Power Electron. Smart Grid, Renew. Energy,” pp. 1–5, 2022, doi: 10.1109/PESGRE52268.2022.9715815.
- H. Sukri, A. B. Putra, A. F. Ibadillah, M. Ulum, D. N. Purnamasari, and M. Hardiwansyah, “Automatic Charcoal Briquette Making Machine Tool with PID Control Approach Using Ziegler-Nichols Tuning Method for Energy Efficiency and Productivity,” Proceeding - IEEE 10th Inf. Technol. Int. Semin. ITIS 2024, pp. 89–95, 2024, doi: 10.1109/ITIS64716.2024.10845718.
- S. R. Mahapatro and B. Subudhi, “A New H∞Weighted Sensitive Function-Based Robust Multi-Loop PID Controller for a Multi-Variable System,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 71, no. 3, pp. 1256–1260, 2024, doi: 10.1109/TCSII.2023.3319388.
References
N. D. Rahayu, B. Sasmito, and N. Bashit, “Analisis Pengaruh Fenomena Indian Ocean Dipole (IOD) Terhadap Curah Hujan Di Pulau Jawa,” J. Geod. Undip, vol. 7, no. 1, pp. 57–67, 2018.
B. P. Nasional, “Gugah Kesadaran Masyarakat Pentingnya Sayur dan Buah Demi Capai Pola Makan Bergizi Seimbang,” Badan Pangan Nasional. Accessed: Jun. 22, 2025. [Online]. Available: https://badanpangan.go.id/blog/post/gugah-kesadaran-masyarakat-pentingnya-sayur-dan-buah-demi-capai-pola-makan-bergizi-seimbang
A. Aditianti, S. Prihatini, and H. Hermina, “Pengetahuan, Sikap dan Perilaku Individu Tentang Makanan Beraneka Ragam sebagai Salah Satu Indikator Keluarga Sadar Gizi (KADARZI),” Bul. Penelit. Kesehat., vol. 44, no. 2, pp. 117–126, 2016, doi: 10.22435/bpk.v44i2.5455.117-126.
A. Ullah, S. Aktar, N. Sutar, R. Kabir, and A. Hossain, “Cost Effective Smart Hydroponic Monitoring and Controlling System Using IoT,” Intell. Control Autom., vol. 10, no. 04, pp. 142–154, 2019, doi: 10.4236/ica.2019.104010.
S. Wahyuni, M. Wahyudi, and A. Rusidy, “Rekayasa Digitalisasi Pertanian Hidroponik NFT dengan Model Kendali Suhu, pH dan Electrical Conductivity (EC),” Rekayasa, vol. 14, no. 1, pp. 68–77, 2021, doi: 10.21107/rekayasa.v14i1.9217.
S. M. Wirawati and S. N. Arthawati, “Meningkatan Pendapatan Masyarakat Melalui Budidaya Tanaman Sawi Dengan Metode Hidroponik Di Desa Pelawad Kecamatan Ciruas,” ABDIKARYA J. Pengabdi. dan Pemberdaya. Masy., vol. 3, no. 1, pp. 1–9, 2021, doi: 10.47080/abdikarya.v3i1.1151.
R. Deshintia, A. Mufti, and F. Heltha, “Penerapan Logika Fuzzy Pada Pengontrolan Larutan Nutrisi Tanaman Selada Menggunakan Hidroponik Nutrient Film Technique,” vol. 9, no. 1, pp. 1–6, 2024.
N. Surantha and V. Vincentdo, “NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference System,” 2022 2nd Int. Conf. Robot. Autom. Artif. Intell. RAAI 2022, pp. 118–123, 2022, doi: 10.1109/RAAI56146.2022.10092958.
Fitriani, Z. Zainuddin, and Syafaruddin, “Nutrition Control System In Nutrient Film Technique (NFT) Hydroponics With Convolutional Neural Network (CNN) Method,” Proc. - ISMODE 2022 2nd Int. Semin. Mach. Learn. Optim. Data Sci., pp. 41–46, 2022, doi: 10.1109/ISMODE56940.2022.10180412.
S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” Proc. 2017 Int. Conf. Eng. Technol. ICET 2017, vol. 2018-Janua, pp. 1–6, 2017, doi: 10.1109/ICEngTechnol.2017.8308186.
A. F. Zrigan, A. J. Abougarair, M. K. Elmezughi, and A. M. Almaktoof, “Optimized PID Controller and Generalized Inverted Decoupling Design for MIMO System,” Proc. 2023 IEEE Int. Conf. Adv. Syst. Emergent Technol. IC_ASET 2023, pp. 1–6, 2023, doi: 10.1109/IC_ASET58101.2023.10150957.
L. Liu, S. Tian, D. Xue, T. Zhang, Y. Chen, and S. Zhang, “A Review of Industrial MIMO Decoupling Control, Automation and Systems,” Int. J. Control, vol. 17, no. X, pp. 1–9, 2019.
R. Li, Q. Kong, J. Ma, and K. Liang, “Decoupling Control Method of Temperature and Humidity in Long Storage Environment Based on Particle Swarm Optimization PID Algorithm,” Proc. - 2023 Int. Conf. Adv. Electr. Eng. Comput. Appl. AEECA 2023, pp. 902–907, 2023, doi: 10.1109/AEECA59734.2023.00164.
K. Liu, Y. Liu, F. Song, C. Zhang, W. Li, and Z. Wang, “Data Driven Dynamic Decoupling Control for MIMO Precision Mechatronic Systems,” Proc. 2024 IEEE 13th Data Driven Control Learn. Syst. Conf. DDCLS 2024, pp. 1305–1310, 2024, doi: 10.1109/DDCLS61622.2024.10606678.
C. G. Proudfoot, “Principles and practice of automatic process control. Carlos A. Smith and Armando B. Corripio,” Automatica, vol. 23, no. 3, p. 414, 1987, doi: 10.1016/0005-1098(87)90018-5.
S. A. Aessa, S. W. Shneen, and M. K. Oudah, “Optimizing PID Controller for Large-Scale MIMO Systems Using Flower Pollination Algorithm,” J. Robot. Control, vol. 6, no. 2, pp. 553–559, 2025, doi: 10.18196/jrc.v6i2.24409.
Ogata, Teknik Kontrol Automatik. Jakarta: Erlangga, 1996.
D. S. Bhandare, N. R. Kulkarni, and M. V. Bakshi, “Linearization of a Coupled tank MIMO system and its validation using MATLAB,” 2021 6th Int. Conf. Converg. Technol. I2CT 2021, pp. 1–5, 2021, doi: 10.1109/I2CT51068.2021.9417875.
K. Jangala, M. Rathaiah, R. Kiranmayi, P. Bharat Kumar, K. Nagabhushanam, and N. Swathi, “Improved Fractional Filter IMC-PID Controller Design for SISO and MIMO Processes,” 2023 5th Int. Conf. Electr. Comput. Commun. Technol. ICECCT 2023, pp. 1–5, 2023, doi: 10.1109/ICECCT56650.2023.10179712.
J. M. Daif-Alkhasraji, S. W. Shneen, and M. Q. Sulttan, “Reduction of Large Scale Linear Dynamic MIMO Systems Using ACO-PID Controller,” Ing. e Investig., vol. 44, no. 1, pp. 1–7, 2024, doi: 10.15446/ing.investig.106657.
G. Sitaramu, L. Dutta, and D. Kumar Das, “To Design Sigmoid Based PID Controller for Twin Rotor MIMO System,” 2023 2nd IEEE Int. Conf. Meas. Instrumentation, Control Autom. ICMICA 2023, pp. 1–5, 2024, doi: 10.1109/ICMICA61068.2024.10732610.
A. Dubravic, D. Demirovic, and A. Serifovic-Trbalic, “Optimization of PID Controller Using PSO Algorithm for a First Order Plus Dead Time (FOPDT) Process -A Simulation Study,” Int. Conf. Electr. Comput. Energy Technol. ICECET 2022, no. July, pp. 1–4, 2022, doi: 10.1109/ICECET55527.2022.9872631.
P. Kumar, V. Kumar, and B. Tyagi, “Experimental Validation of PI Controllers and Modelling of DC Servo Motor by FOPDT Model,” PESGRE 2022 - IEEE Int. Conf. “Power Electron. Smart Grid, Renew. Energy,” pp. 1–5, 2022, doi: 10.1109/PESGRE52268.2022.9715815.
H. Sukri, A. B. Putra, A. F. Ibadillah, M. Ulum, D. N. Purnamasari, and M. Hardiwansyah, “Automatic Charcoal Briquette Making Machine Tool with PID Control Approach Using Ziegler-Nichols Tuning Method for Energy Efficiency and Productivity,” Proceeding - IEEE 10th Inf. Technol. Int. Semin. ITIS 2024, pp. 89–95, 2024, doi: 10.1109/ITIS64716.2024.10845718.
S. R. Mahapatro and B. Subudhi, “A New H∞Weighted Sensitive Function-Based Robust Multi-Loop PID Controller for a Multi-Variable System,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 71, no. 3, pp. 1256–1260, 2024, doi: 10.1109/TCSII.2023.3319388.