Application of Smart Aquaponics Technology for Sustainable Household-Scale Food Production in Urban Jakarta
Article Sidebar
-
smart aquaponics, urban agriculture, food security, sustainability, IoT technology.
Abstract
Background. Rapid urbanization in Jakarta has intensified challenges related to food security, limited land availability, and environmental degradation, prompting the need for sustainable household-scale food production systems. This study explores the application of smart aquaponics technology as an integrated model combining aquaculture and hydroponics for efficient food production within urban households.
Purpose. The research aims to assess the technological feasibility, productivity outcomes, and environmental benefits of smart aquaponics as a sustainable alternative to conventional urban farming methods.
Method. A mixed-method design was employed, incorporating experimental trials, sensor-based monitoring, and household surveys conducted between January and August 2024 across five Jakarta districts..
Results. Results indicate that smart aquaponics systems increased vegetable yield by 38% and reduced water consumption by 42% compared to traditional hydroponic setups. The integration of IoT sensors enabled automated nutrient control, improving fish growth rates and system stability. Respondents reported enhanced household food self-sufficiency and reduced monthly food expenditure.
Conclusion. The study concludes that smart aquaponics offers a scalable and environmentally friendly solution for urban food resilience, aligning with Indonesia’s Sustainable Development Goals on food security and environmental sustainability.
Full text article
References
Abbasi, R., Martinez, P., & Ahmad, R. (2022). Data Acquisition and Monitoring Dashboard for IoT Enabled Aquaponics Facility. 168–172. https://doi.org/10.1109/ICCMA56665.2022.10011594
Amano, J. R., Punongbayan, N., Caacbay, J., Agustin, E., Dela Vega, K. A., Soriano, A., Andaya, F., Mandayo, E., & Beano, M. G. (2022). A Comparative Analysis of Machine Learning Algorithms for Bok Choy Leaf Disease Identification in Smart Aquaponics. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2022-November. https://doi.org/10.1109/TENCON55691.2022.9978159
Andrada, A. F., Yu, J. L. D., & Ostia, C. F. (2025). Design of a Triple-Input, Single-Output Powered Aquaponics System with Iot Battery Monitoring. 546–551. https://doi.org/10.1109/ICCAE64891.2025.10980501
Bin Zakaria, M. S., Bin Basar, M. R., Sajak, A. A. B., Mansor, Z., & Ali, A. S. (2024). Automated Aquaponics Systems to Support Sustainable Development Goals. In B. Alareeni & A. Hamdan (Eds.), Lecture Notes in Networks and Systems: Vol. 1081 LNNS (pp. 107–122). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-67437-2_11
Chakraborty, P., & Krishnani, K. K. (2022). Climate Smart Eco-management of Water and Soil Quality as a Tool for Fish Productivity Enhancement (Vol. 2, pp. 277–290). Springer International Publishing. https://doi.org/10.1007/978-3-030-93262-6_14
Chauhan, R., Gupta, V., Gupta, R., Bhatt, C., & Devliyal, S. (2023). An IoT based Smart Vertical Hydroponics System: Power of Computer in Farming. 619–624. https://doi.org/10.1109/ICCSAI59793.2023.10421529
Debroy, P., Majumder, P., Majumdar, P., Das, A., & Seban, L. (2025). Analysis of opportunities and challenges of smart aquaponic system: a summary of research trends and future research avenues. Sustainable Environment Research, 35(1). https://doi.org/10.1186/s42834-025-00255-z
Ekanayake, D., de Alwis, P., Harshana, P., Munasinghe, D., Jayakody, A., & Gamage, M. N. (2022). A Smart Aquaponic System for Enhancing The Revenue of Farmers in Sri Lanka. https://doi.org/10.1109/ICISS55894.2022.9915162
Ezzahoui, I., Ait Abdelouahid, R., & Marzak, A. (2024). Aquaponics Revolution: Reinforcing performance by means of Apache Spark and Apache Kafka. In E. E. Shakshuki (Ed.), Procedia Computer Science (Vol. 241, pp. 624–629). Elsevier B.V. https://doi.org/10.1016/j.procs.2024.08.091
Fandiño-Pelayo, J. S., Mendoza Castellanos, L. S., Cazes Ortega, R., & Hernández-Rojas, L. G. (2025). AI-Driven Monitoring for Fish Welfare in Aquaponics: A Predictive Approach. Sensors, 25(19). https://doi.org/10.3390/s25196107
Gordon-Smith, H. (2024). CONNECTING ARCHITECTURE AND AGRICULTURE FOR A CLIMATE-SMART FUTURE (pp. 435–445). Taylor and Francis. https://doi.org/10.4324/9781003384113-50
Hadiyoso, S., Alfaruq, A., Wijayanto, I., Ramadan, D. N., Senthil Kumar, A. V. S., & Irawati, I. D. (2024). A Smart Aquaponics System: IoT-Driven Water Quality Control for Lettuce Cultivation. 279–283. https://doi.org/10.1109/ICICYTA64807.2024.10913414
Kodali, R. K., & Sabu, A. C. (2022). Aqua Monitoring System using AWS. https://doi.org/10.1109/ICCCI54379.2022.9740798
López-Erazo, O. S., Delle Ville, J., Maltempo, G., Gómez, A., Ortega Erazo, J. C., Muñoz, L. F., Hurtado Alegría, J. A., & Antonelli, L. (2025). Application of SemIoTica to the Development of a Prototype of an Intelligent System with IoT in Single-Family Aquaponics at the Tecno Academia Popayán. In V. Agredo-Delgado, V. Agredo-Delgado, P. H. Ruiz, & C. A. Meneses Escobar (Eds.), Communications in Computer and Information Science: Vol. 2369 CCIS (pp. 42–57). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-91690-8_4
Murdan, A. P., & Joyram, A. (2021). An IoT based solar powered aquaponics system. https://doi.org/10.1109/ECAI52376.2021.9515023
Nemade, B., & Shah, D. (2023). An IoT-Based Efficient Water Quality Prediction System for Aquaponics Farming. In A. Shukla, N. Hasteer, B. K. Murthy, & J.-P. VanBelle (Eds.), Lecture Notes in Electrical Engineering (Vol. 968, pp. 311–323). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-7346-8_27
Nishanth, D., Alshamsi, M. H. S., Alkaabi, A. M. K. A., AlKaabi, A. H. A., Alnuaimi, S. H. K., Nair, C. S., & Jaleel, A. (2024). Aquaponics as a climate-smart technology for sustainable food production: A comparison with conventional production system in United Arab Emirates. Journal of the World Aquaculture Society, 55(2). https://doi.org/10.1111/jwas.13049
Ramchiary, D., Nirmala, B., Baishya, S., & Raibaruah, A. K. (2022). Aquaponics-A Smart Automated Feedback Control Process for Cultivation. https://doi.org/10.1109/IPRECON55716.2022.10059575
Reyes-Yanes, A., Abbasi, R., Martinez, P., & Ahmad, R. (2022). Digital Twinning of Hydroponic Grow Beds in Intelligent Aquaponic Systems. Sensors, 22(19). https://doi.org/10.3390/s22197393
Shareef, U., Rehman, A. U., & Ahmad, R. (2024). A Systematic Literature Review on Parameters Optimization for Smart Hydroponic Systems. AI (Switzerland), 5(3), 1517–1533. https://doi.org/10.3390/ai5030073
Silalahi, A. O., Sinambela, A., Pardosi, J. T. N., & Panggabean, H. M. (2022). Automated Water Quality Monitoring System for Aquaponic Pond using LoRa TTGO SX1276 and Cayenne Platform. https://doi.org/10.1109/ICOSNIKOM56551.2022.10034916
Taji, K., Sohail, A., Ghanimi, F., Ghanimi, I., Ilyas, S., & Ahmad, Y. T. (2023). A Systematic Literature Review of Computational Studies in Aquaponic System Literature Review of Computational Studies in Aquaponic System. International Journal of Advanced Computer Science and Applications, 14(9), 333–343. https://doi.org/10.14569/IJACSA.2023.0140936
Venkatraman, M., & Surendran, R. (2023). Aquaponics and Smart Hydroponics Systems Water Recirculation Using Machine Learning. 998–1004. https://doi.org/10.1109/ICOSEC58147.2023.10276310
Yadav, A., Trivedi, Y., & Sharma, N. (2024). Design Consideration of Table-top Model of an AI-Enabled Aquaponics System. https://doi.org/10.1109/PuneCon63413.2024.10895349
Authors
Copyright (c) 2025 Prastika Suwandi Tjeng, Rashid Rahman, Rina Nopianti, Ravi Dara
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
