RANCANG BANGUN SISTEM KONTROL DAN MONITORING PH, SUHU AIR DAN TDS PADA SISTEM AKUAPONIK BERBASIS INTERNET OF THINGS (IOT)

Ubaidillah Umar

Abstract


As time goes by, agricultural land in urban areas has decreased. Aquaponics can be an option for agricultural development in urban areas. However, in the field, there are obstacles where the condition of the fishpond water is uncertain in providing nutrients for the plants. To respond to this condition, this research developed a system by implementing an IoT system that is integrated with a rule-based control system to control and monitor nutritional quality by the needs of aquaponic-based plants. This technology includes controlling pH levels, water temperature, and TDS (total dissolved solids) produced by fish waste in the aquaponic system to be used as nutrition for plants. This system requires several sensors and main components, including a ph meter sensor, TDS, and temperature sensor, which are integrated and processed via the ESP32 microcontroller. The system measurement results are controlled and sent to the IoT Cloud database connected by an internet connection. This IoT-based aquaponics system can be monitored in real-time. The test results on the system show that the sensors used have a small reading error, namely the temperature sensor showing an error value of 1.5%, the PH sensor 0.7%, and the TDS sensor 0.9%. In this way, the level of accuracy when using sensors can be guaranteed. The research results show that the control and monitoring system design works according to needs, where the system that is controlled and integrated with the website system can run with a 100% success rate, which can facilitate the care and maintenance of plants and fish by farmers.

Full Text:

PDF

References


Abbasi, Rabiya, Pablo Martinez, and Rafiq Ahmad. 2021. “An Ontology Model to Support the Automated Design of Aquaponic Grow Beds.” Procedia CIRP 100: 55–60.

Chukkapalli, Sai Sree Laya et al. 2020. “Ontologies and Artificial Intelligence Systems for the Cooperative Smart Farming Ecosystem.” IEEE Access 8: 164045–64.

Dhal, Sambandh Bhusan, Muthukumar Bagavathiannan, Ulisses Braga-Neto, and Stavros Kalafatis. 2022. “Nutrient Optimization for Plant Growth in Aquaponic Irrigation Using Machine Learning for Small Training Datasets.” Artificial Intelligence in Agriculture 6: 68–76.

Garrido-Momparler, Víctor, and Miguel Peris. 2022. “Smart Sensors in Environmental/Water Quality Monitoring Using IoT and Cloud Services.” Trends in Environmental Analytical Chemistry 35: e00173.

Haryanto et al. 2019. “Smart Aquaponic System Based Internet of Things (IoT).” In Journal of Physics: Conference Series, Institute of Physics Publishing.

Khaoula, Taji, Rachida Ait Abdelouahid, Ibtissame Ezzahoui, and Abdelaziz Marzak. 2021a. “Architecture Design of Monitoring and Controlling of IoT-Based Aquaponics System Powered by Solar Energy.” In Procedia Computer Science, Elsevier B.V., 493–98.

———. 2021b. “Architecture Design of Monitoring and Controlling of IoT-Based Aquaponics System Powered by Solar Energy.” Procedia Computer Science 191: 493–98.

Kralik, Brittany et al. 2022. “From Water to Table: A Multidisciplinary Approach Comparing Fish from Aquaponics with Traditional Production Methods.” Aquaculture 552: 737953.

Kumar Pothula, Anand, Zhao Zhang, and Renfu Lu. 2023. “Evaluation of a New Apple In-Field Sorting System for Fruit Singulation, Rotation and Imaging.” Computers and Electronics in Agriculture 208.

Kyaw, Thu Ya, and Andrew Keong Ng. 2017. “Smart Aquaponics System for Urban Farming.” Energy Procedia 143: 342–47.

Megawati, Dini et al. 2020. “Rancang Bangun Sistem Monitoring PH Dan Suhu Air Pada Akuaponik Berbasis Internet of Thing (IoT).” TELKA - Jurnal Telekomunikasi, Elektronika, Komputasi dan Kontrol 6(2): 124–37. https://telka.ee.uinsgd.ac.id/index.php/TELKA/article/view/telka.v6n2.124-137 (August 1, 2023).

Nie, Peng, Michele Roccotelli, Maria Pia Fanti, and Zhiwu Li. 2022. “Fuzzy Rule-Based Models for Home Energy Consumption Prediction.” Energy Reports 8: 9279–89.

Pasha, Adrian K. et al. 2018. “System Design of Controlling and Monitoring on Aquaponic Based on Internet of Things.” Proceeding of 2018 4th International Conference on Wireless and Telematics, ICWT 2018.

Paul, Sanjoy Kumar et al. 2022. “An Advanced Decision-Making Model for Evaluating Manufacturing Plant Locations Using Fuzzy Inference System.” Expert Systems with Applications 191.

Podder, Amit Kumer et al. 2021. “IoT Based Smart Agrotech System for Verification of Urban Farming Parameters.” Microprocessors and Microsystems 82.

Trevathan, Jarrod et al. 2021. “An IoT General-Purpose Sensor Board for Enabling Remote Aquatic Environmental Monitoring.” Internet of Things 16: 100429.

Umar, Ubaidillah, Dimas Adiputra, and Helmy Widyantara. 2020. “Pengembangan Sistem Kendali Kuantitas Air Pada Tanaman Hidroponik Berbasis Internet of Thing (IoT).” MULTINETICS 6(2): 110–16. https://jurnal.pnj.ac.id/index.php/multinetics/article/view/3447 (July 31, 2023).

Umar, Ubaidillah, Tri Arief Sardjono, and Hendra Kusuma. 2023. “The Ontology Model for Selecting Quality Melons Uses Hidden Semantic Data Based on Melon Knowledge Domains.” 13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023: 95–100.

Vernandhes, Wanda, N. S. Salahuddin, A. Kowanda, and Sri Poernomo Sari. 2018a. “Smart Aquaponic with Monitoring and Control System Based on IoT.” Proceedings of the 2nd International Conference on Informatics and Computing, ICIC 2017 2018-January: 1–6.

———. 2018b. “Smart Aquaponic with Monitoring and Control System Based on IoT.” Proceedings of the 2nd International Conference on Informatics and Computing, ICIC 2017 2018-January: 1–6.

Wang, Liping, and Emmanuel Iddio. 2022. “Energy Performance Evaluation and Modeling for an Indoor Farming Facility.” Sustainable Energy Technologies and Assessments 52: 102240.

Xiang, Zhongming et al. 2022. “Rule Base Construction Method of Section out of Limit Disposal Strategy for Thermal Power Unit Equipment Fault.” Energy Reports 8: 13220–25.

Zhao, Fang, Gang Li, Hongyue Guo, and Lidong Wang. 2022. “Rule-Based Models via the Axiomatic Fuzzy Set Clustering and Their Granular Aggregation.” Applied Soft Computing 130: 109692.




DOI: https://doi.org/10.31884/jtt.v10i1.557

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JTT (Jurnal Teknologi Terapan)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

View Stats

 

 Creative Common Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)