An Intelligent Fuzzy Logic-Controlled IoT System for Efficient Hydroponic Plant Monitoring and Automation
DOI:
https://doi.org/10.31961/eltikom.v9i1.1475Keywords:
fuzzy logic control, Hydroponic automation, IoT system, Plant monitoring, Sustainable agricultureAbstract
This paper addresses the challenges of optimizing environmental conditions in hydroponic farming by integrating an Intelligent Fuzzy Logic-Controlled IoT System. The research problem lies in the inefficiency of traditional hydroponic monitoring systems, particularly in maintaining ideal conditions for plant growth while minimizing resource waste. This study aims to develop a system that leverages IoT technology and fuzzy logic to monitor and automate hydroponic processes more efficiently. Using sensors, the system continuously tracks key environmental parameters such as temperature, humidity, soil moisture, pH levels, and total dissolved solids (TDS). A fuzzy logic controller (FLC) triggers actions based on predefined rules. During testing, the system showed effective performance—for example, activating fans when temperature (31.2°C) and humidity (60%) indicated a need for cooling, and adjusting nutrient levels when pH (5.8) and TDS (450 ppm) were suboptimal. The system offers practical benefits through real-time adaptation using defuzzification and aggregation, ensuring precise resource control, improving efficiency, and reducing waste. This study highlights the system's potential to support sustainable agriculture by providing scalable solutions that enhance plant growth and optimize resource use, especially for small-scale farmers and urban farming initiatives.
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C. Y.K, S. Sukendah, and M. Makhziah, “Effects of Differences in Installation Slope on Growth and Yield of Rice (Oryza Sativa L.) Hydroponically,” J. Agrium, vol. 19, no. 3, p. 336, 2022, doi: 10.29103/agrium.v19i4.9733.
R. Lantarsih, “Marketing Strategy Analysis of Hydroponic Vegetables of Kebun Sehati,” Agric, vol. 35, no. 1, pp. 13–26, 2023, doi: 10.24246/agric.2023.v35.i1.p13-26.
S. Surateno, S. Kautsar, R. Wijaya, K. Husain, B. Widiawan, and C. Triwidiarto, “Portable Automatic Nutrient Mixing Based on Mi-crocontroller for Hydroponic Vegetable Cultivation,” Iop Conf. Ser. Earth Environ. Sci., vol. 1338, no. 1, p. 12057, 2024, doi: 10.1088/1755-1315/1338/1/012057.
W. S. C. Surbakti and S. Sabirin, “A Look at the Things That Affect Modern Farmers’ Income,” J. Magister Ilmu Ekon. Universtas Palangka Raya Growth, pp. 103–110, 2024, doi: 10.52300/grow.v9i2.12368.
V. Vincentdo and N. Surantha, “Nutrient Film Technique-Based Hydroponic Monitoring and Controlling System Using ANFIS,” Elec-tronics, vol. 12, no. 6, p. 1446, 2023, doi: 10.3390/electronics12061446.
A. Siddiq, M. Tariq, A. Zehra, and S. A. Malik, “ACHPA: A Sensor Based System for Automatic Environmental Control in Hydropon-ics,” Food Sci. Technol., vol. 40, no. 3, pp. 671–680, 2020, doi: 10.1590/fst.13319.
S. B. Sartika, “Education and Assistance in Hydroponic Plant Cultivation for Strengthening the Self-Reliant Economy,” Abdimas J. Pengabdi. Masy. Univ. Merdeka Malang, vol. 8, no. 2, pp. 243–251, 2023, doi: 10.26905/abdimas.v8i2.9588.
I. K. Wibowo et al., “SLiCE: Implementation of Automation Technology and Internet of Things in the Greenhouse,” Abdimas J. Pengabdi. Masy. Univ. Merdeka Malang, vol. 8, no. 2, pp. 315–325, 2023, doi: 10.26905/abdimas.v8i2.9816.
H. Y. Riskiawan et al., “Artificial Intelligence Enabled Smart Monitoring and Controlling of IoT-Green House,” Arab. J. Sci. Eng., vol. 49, no. 3, pp. 3043–3061, 2024, doi: 10.1007/s13369-023-07887-6.
D. Rahadiyan, S. Hartati, W. Wahyono, and A. P. Nugroho, “Design of an Intelligent Hydroponics System to Identify Macronutrient Deficiencies in Chili,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 1, 2022, doi: 10.14569/ijacsa.2022.0130117.
A. V Hartanto, F. S. Kristiady, D. K. P. Aji, and U. Nurhasan, “Enhancing Hydroponic Systems with ESP32 : An IoT Approach to Real-Time Monitoring and Automation Enhancing Hydroponic Systems with ESP32 : An IoT Approach to Real-Time Monitoring and Au-tomation”, doi: 10.1088/1755-1315/1446/1/012010.
A. Rahman, S. Wahjuni, and K. Priandana, “The Development of Hydroponic Nutrient Solutions Control Using Fuzzy and BPNN for Celery Plant,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 1, p. 431, 2022, doi: 10.18517/ijaseit.12.1.13833.
S. A. Barai, N. Nurmahfuzhah, and D. Defina, “Cultivating Sustainability: Exploring the Relationship Between Homestead Gardening, Land Property, and Family Economic Pressure in Household With Stunting Children,” J. Fam. Sci., vol. 8, no. 2, pp. 190–203, 2023, doi: 10.29244/jfs.v8i2.51324.
S. Aisyah, S. Suhendra, K. Saharja, and F. Telaumbanua, “Development of an Automated Feeding System for Hydroponic Plant Nutrition Using Arduino Uno,” Kne Eng., 2024, doi: 10.18502/keg.v6i1.15365.
A. F. Amalia et al., “Artificial Intelligence for Small Hydroponics Farms Employing Fuzzy Logic Systems and Economic Analysis,” Rev. Bras. Eng. Agrícola E Ambient., vol. 27, no. 9, pp. 690–697, 2023, doi: 10.1590/1807-1929/agriambi.v27n9p690-697.
M. Rathedi, O. Matsebe, and N. M. J. Ditshego, “Performance Evaluation of Hydroponics Control Systems for pH, Temperature, and Water Level Control,” Int. J. Eng. Res. Africa, vol. 65, pp. 105–116, 2023, doi: 10.4028/p-rbt3yu.
H. Helmy, M. G. Mahaidayu, A. Nursyahid, T. A. Setyawan, and A. S. M. J. Hasan, “Nutrient Film Technique (NFT) Hydroponic Mon-itoring System Based on Wireless Sensor Network,” pp. 81–84, 2017, doi: 10.1109/comnetsat.2017.8263577.
E. Ilten, H. Calgan, and M. Demirtas, “Fuzzy Logic Level Control of a Coupled Tank System with Raspberry Pi Application,” no. November, 2023, doi: 10.59287/as-proceedings.125.
I. S. Nasution et al., “Embedded Fuzzy Logic for Controlling pH and Nutrition in Hydroponic Cultivation,” Iop Conf. Ser. Earth Envi-ron. Sci., vol. 1183, no. 1, p. 12113, 2023, doi: 10.1088/1755-1315/1183/1/012113.
A. A. Kurniasari, T. D. Puspitasari, R. Chandra, K. Jurusan, K. Politeknik, and N. Jember, “Sistem Informasi Diagnosis Ikterus Neona-torum Menggunakan Metode Fuzzy Tsukamoto,” 2022. [Online]. Available: https://doi.org/10/25047/jtit.v9i2.274
Y.-F. Yang, H.-Y. Chen, Y.-H. Chen, S. P. Ho, C.-S. Wang, and C.-F. Lin, “Refining Environmental Sustainability Governance Reports Through Fuzzy Systems Evaluation and Scoring,” Sustainability, vol. 16, no. 16, p. 7227, 2024, doi: 10.3390/su16167227.
W. Wawan, M. Zuniati, and A. Setiawan, “Optimization of National Rice Production With Fuzzy Logic Using Mamdani Method,” J. Multidiscip. Appl. Nat. Sci., vol. 1, no. 1, pp. 36–43, 2021, doi: 10.47352/jmans.v1i1.3.
A. H. Prihamayu, “Prediction of Closing Price Combined Stock Index (Ihsg) Using the Fuzzy Mamdani Method,” vol. 1, no. 2, pp. 74–79, 2022, doi: 10.37567/sajgibe.v1i2.1862.
T. K. Huseynov, N. A. Abdulova, T. T. Huseymov, A. R. Hashimova, and A. Y. Shirinova, “Fuzzy System of Automatic Tuning of Electromagnetic Excitation System of Vibration-Frequency Liquid Density Meter,” J. Phys. Conf. Ser., vol. 2697, no. 1, p. 12006, 2024, doi: 10.1088/1742-6596/2697/1/012006.
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