Integrated Fuzzy Logic Model for Smart Water Quality Monitoring and Floating Net Cage Optimization in Barramundi Aquaculture

Rozeff  Pramana; M Hasbi Sidqi  Alajuri

doi:10.52436/1.jutif.2025.6.5.5346

Authors

Rozeff Pramana Department Electrical Engineering, Faculty of Engineering and Maritime Technology, Raja Ali Haji Maritime University, Indonesia
M Hasbi Sidqi Alajuri Department Electrical Engineering, Faculty of Engineering and Maritime Technology, Raja Ali Haji Maritime University, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.5.5346

Keywords:

Aquaculture, Barramundi, Fuzzy Logic, Monitoring, Floating Net Cage

Abstract

Water quality and aquatic conditions are critical factors in the success of fish farming with Floating Net Cages (FNCs). However, manual monitoring is often delayed due to limited human resources, irregular measurement schedules, and dependence on manual sampling, which can result in late detection of deteriorating water quality and ultimately increase the risk of fish stress, disease outbreaks, and mortality. This study aims to develop an Internet-based water quality monitoring system, integrated with smartphones and PCs, to support rapid decision-making for FNC relocation when water conditions deteriorate. The system is equipped with sensors for temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), total dissolved solids (TDS), turbidity, anemometer, and wind direction, and was field-tested for 36 days in sea-based Barramundi aquaculture. Decision-making was implemented using a Fuzzy Inference System (FIS) with input variables: temperature, DO, pH, and anemometer data, while the output variable was the FNC status: “Relocate” or “Remain.” Results indicated that water quality changes occurred across both short-term and long-term intervals, and during a 56-hour fuzzy simulation, 10 data points suggested “Relocate” while 46 data points indicated “Remain.” The novelty of this research lies in the integration of real-time IoT monitoring with fuzzy logic specifically for FNC relocation decision-making, bridging environmental sensing and intelligent decision support. These findings demonstrate that the proposed system is more effective and efficient than conventional methods, contributing to the advancement of intelligent aquaculture technologies.

Downloads

Download data is not yet available.

References

H. Ul Hassan et al., “Assessment of growth characteristics, the survival rate and body composition of Asian sea bass Lates calcarifer (Bloch, 1790) under different feeding rates in closed aquaculture system,” Saudi J. Biol. Sci., vol. 28, no. 2, hal. 1324–1330, 2021, doi: 10.1016/j.sjbs.2020.11.056.

S. Nayak et al., “From fish excretions to high-protein dietary ingredient: Feeding intensively cultured barramundi (Lates calcarifer) a diet containing microbial biomass (biofloc) from effluent of an aquaculture system,” Aquaculture, vol. 562, hal. 738780, Jan 2023, doi: 10.1016/j.aquaculture.2022.738780.

G. H. Yue et al., “Genomic resources and their applications in aquaculture of Asian seabass,” Rev. Aquac., vol. 15, no. 2, hal. 853–871, Mar 2023, doi: 10.1111/raq.12764.

G. A. Sallam, H. A. Aly, W. M. Fayed, W. B. Elhefnawy, A. E. Khalid, dan E. A. Omar, “Stress correlated factors with water quality under different rearing conditions of the european sea bass (Dicentrarchus labrax) juveniles,” Egypt. J. Aquat. Biol. Fish., vol. 24, no. 1, hal. 453–470, 2020, doi: 10.21608/EJABF.2020.72712.

C. Orduna, L. Encina, A. Rodríguez-Ruiz, dan V. Rodríguez-Sánchez, “Testing of new sampling methods and estimation of size structure of sea bass (Dicentrarchus labrax) in aquaculture farms using horizontal hydroacoustics,” Aquaculture, vol. 545, hal. 737242, Des 2021, doi: 10.1016/j.aquaculture.2021.737242.

M. Q. Nguyen, D. M. Nguyen, T. T. T. Toan, dan A. Q. Dao, “Nanotechnology in Aquaculture: Applications and Challenges,” J. Electrochem. Soc., vol. 171, no. 5, hal. 057507, Mei 2024, doi: 10.1149/1945-7111/ad48c2.

K. Zhang et al., “Water Quality Impact on Fish Behavior: A Review From an Aquaculture Perspective,” Rev. Aquac., vol. 17, no. 1, Jan 2025, doi: 10.1111/raq.12985.

M. Burke, J. Grant, R. Filgueira, dan T. Stone, “Oceanographic processes control dissolved oxygen variability at a commercial Atlantic salmon farm: Application of a real-time sensor network,” Aquaculture, vol. 533, hal. 736143, Feb 2021, doi: 10.1016/j.aquaculture.2020.736143.

T. M. Szewczyk et al., “Interactive effects of multiple stressors with significant wave height exposure on farmed Atlantic salmon (Salmo salar) welfare along an inshore-offshore gradient,” Aquaculture, vol. 579, hal. 740184, Jan 2024, doi: 10.1016/j.aquaculture.2023.740184.

J. Jamaludin, A. Mustofa, dan L. Mudiarti, “Analisis Kesesuain Perairan untuk Budidaya Ikan Kerapu Macan (Epinephelus fuscoguttatus) di Perairan Menjagan Besar, Taman Nasinal Karimunjawa, Jepara,” J. Mar. Res., vol. 13, no. 4, hal. 784–790, Nov 2024, doi: 10.14710/jmr.v13i4.48068.

B. Su et al., “Towards a holistic digital twin solution for real-time monitoring of aquaculture net cage systems,” Mar. Struct., vol. 91, hal. 103469, Sep 2023, doi: 10.1016/j.marstruc.2023.103469.

W.-T. Sung, F. N. Fadillah, dan S.-J. Hsiao, “IoT-based Water Quality Monitoring,” Sensors Mater., vol. 33, no. 8, hal. 2971, Agu 2021, doi: 10.18494/SAM.2021.3342.

M. A. López-Munoz et al., “Wireless Dynamic Sensor Network for Water Quality Monitoring Based on the IoT,” Technologies, vol. 12, no. 11, hal. 211, Okt 2024, doi: 10.3390/technologies12110211.

E. N. L. Alave, H. A. G. Lim, J. L. Ronquillo, dan M. M. Tugade, “PORTABLE ARDUINO-BASED INTEGRATED WATER QUALITY ANALYZER WITH REAL-TIME DATA TRANSMITTER,” MATTER Int. J. Sci. Technol., vol. 6, no. 3, hal. 58–71, Des 2020, doi: 10.20319/mijst.2020.63.5871.

F. Akhter, H. R. Siddiquei, M. E. E. Alahi, K. P. Jayasundera, dan S. C. Mukhopadhyay, “An IoT-Enabled Portable Water Quality Monitoring System With MWCNT/PDMS Multifunctional Sensor for Agricultural Applications,” IEEE Internet Things J., vol. 9, no. 16, hal. 14307–14316, Agu 2022, doi: 10.1109/JIOT.2021.3069894.

N. Inas Fikri, V. Louis Nathaniel, M. Syahrul Gunawan, dan T. Abuzairi, “Design of Real-Time Aquarium Monitoring System for Endemic Fish on the Smartphone,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 7, no. 2, hal. 269, Agu 2021, doi: 10.26555/jiteki.v7i2.21137.

A. Abu Sneineh dan A. A. A. Shabaneh, “Design of a smart hydroponics monitoring system using an ESP32 microcontroller and the Internet of Things,” MethodsX, vol. 11, hal. 102401, Des 2023, doi: 10.1016/j.mex.2023.102401.

L. K. S. Tolentino et al., “Development of an IoT-based Intensive Aquaculture Monitoring System with Automatic Water Correction,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, hal. 1355–1365, Des 2021, doi: 10.12785/ijcds/1001120.

M. M. Islam, “Real-time dataset of pond water for fish farming using IoT devices,” Data Br., vol. 51, hal. 109761, Des 2023, doi: 10.1016/j.dib.2023.109761.

H. Fakhrurroja, E. T. Nuryatno, A. Munandar, M. Fahmi, dan N. A. Mahardiono, “Water quality assessment monitoring system using fuzzy logic and the internet of things,” J. Mechatronics, Electr. Power, Veh. Technol., vol. 14, no. 2, hal. 198–207, Des 2023, doi: 10.14203/j.mev.2023.v14.198-207.

R. P. Shete, A. M. Bongale, dan D. Dharrao, “IoT-enabled effective real-time water quality monitoring method for aquaculture,” MethodsX, vol. 13, hal. 102906, Des 2024, doi: 10.1016/j.mex.2024.102906.

W.-T. Sung, I. G. T. Isa, dan S.-J. Hsiao, “Integrated Aquaculture Monitoring System Using Combined Wireless Sensor Networks and Deep Reinforcement Learning,” Sensors Mater., vol. 36, no. 3, hal. 1019, Mar 2024, doi: 10.18494/SAM4660.

I. Essamlali, H. Nhaila, dan M. El Khaili, “Advances in machine learning and IoT for water quality monitoring: A comprehensive review,” Heliyon, vol. 10, no. 6, hal. e27920, Mar 2024, doi: 10.1016/j.heliyon.2024.e27920.

M.-C. Chiu, W.-M. Yan, S. A. Bhat, dan N.-F. Huang, “Development of smart aquaculture farm management system using IoT and AI-based surrogate models,” J. Agric. Food Res., vol. 9, hal. 100357, Sep 2022, doi: 10.1016/j.jafr.2022.100357.

A. Chatziantoniou, N. Papandroulakis, O. Stavrakidis-Zachou, S. Spondylidis, S. Taskaris, dan K. Topouzelis, “Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming,” Appl. Sci., vol. 13, no. 10, hal. 6122, Mei 2023, doi: 10.3390/app13106122.

M. L. Yasruddin, M. Amir Hakim Ismail, Z. Husin, dan W. K. Tan, “Development of Automated Real-Time Water Quality Monitoring and Controlling System in Aquarium,” in 2022 IEEE 12th Symposium on Computer Applications & Industrial Electronics (ISCAIE), IEEE, Mei 2022, hal. 241–245. doi: 10.1109/ISCAIE54458.2022.9794472.

A. Mariu, A. M. M. Chatha, S. Naz, M. F. Khan, W. Safdar, dan I. Ashraf, “Effect of Temperature, pH, Salinity and Dissolved Oxygen on Fishes,” J. Zool. Syst., vol. 1, no. 2, hal. 1–12, Nov 2023, doi: 10.56946/jzs.v1i2.198.

M. M. Fitriana, N. Fadli, dan Z. A. Muchlisin, “Effect of water acidity on the growth performance, survival, and hematology condition of the barramundi fish Lates calcarifer (Bloch, 1790) fingerling,” Depik, vol. 12, no. 1, hal. 19–25, Apr 2023, doi: 10.13170/depik.12.1.31246.

H. T. Yudha, R. A. Santoso, S. S. P. Rahardjo, G. S. Wibawa, dan S. Nuswantoro, “Hatchery Performance of Barramundi (Lates calcarifer) in the Integrated Pond Systems,” J. Fish Heal., vol. 5, no. 2, hal. 142–152, Mar 2025, doi: 10.29303/jfh.v5i2.6291.

Badrudin et al., Budidaya Ikan Kerapu Putih (Lates Calcarifer) Blok 1790 Di Keramba Jaring Apung dan Tambak, 1 ed. Jakarta Selatan: WWF - Indonesia, 2015.

E. Lien, “Tension Leg Cage — A new net pen cage for fish farming,” in Fish Farming Technology, CRC Press, 2020, hal. 251–258. doi: 10.1201/9781003077770-42.

A. K. Gupta, “Fuzzy Logic and Their application in Different Areas of Engineering Science and Research : A Survey,” Int. J. Sci. Res. Sci. Technol., hal. 71–75, Mar 2021, doi: 10.32628/IJSRST218212.

O. Castillo dan P. Melin, “Type-3 Fuzzy Theory,” 2023, hal. 5–15. doi: 10.1007/978-3-031-46088-3_2.

E. Kayacan dan M. A. Khanesar, “Fundamentals of Type-2 Fuzzy Logic Theory,” in Fuzzy Neural Networks for Real Time Control Applications, Elsevier, 2016, hal. 25–35. doi: 10.1016/B978-0-12-802687-8.00003-7.

K. Ma’ruf, R. J. Setiawan, A. Al Kafah Alam, T. Ismail, C. Insaniah Muhammad, dan J. Ali, “Internet of Things for Real-Time Monitoring of Water Quality with Integrated Temperature, pH, and TDS Sensors,” in 2024 International Conference on Electrical Engineering and Computer Science (ICECOS), IEEE, Sep 2024, hal. 314–319. doi: 10.1109/ICECOS63900.2024.10791209.

J. Betancourt, W. Coral, dan J. Colorado, “An integrated ROV solution for underwater net-cage inspection in fish farms using computer vision,” SN Appl. Sci., vol. 2, no. 12, hal. 1946, Des 2020, doi: 10.1007/s42452-020-03623-z.

A. Bhatnagar dan A. Garg, “Climate Variabilities and its Impact on Aquatic Systems,” J. Aquac. Livest. Prod., hal. 1–3, Des 2022, doi: 10.47363/JALP/2022(3)117.

Sonia Bajaj, “TEMPERATURE VARIABILITY AND ITS IMPLICATIONS FOR AQUACULTURE SUSTAINABILITY,” Int. Educ. Res. J., vol. 10, no. 9, Sep 2024, doi: 10.21276/IERJ24629497911776.

C. Wang dan Y. Kuzyakov, “Soil organic matter priming: The pH effects,” Glob. Chang. Biol., vol. 30, no. 6, Jun 2024, doi: 10.1111/gcb.17349.

G. E. Adjovu, H. Stephen, D. James, dan S. Ahmad, “Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques,” Remote Sens., vol. 15, no. 14, hal. 3534, Jul 2023, doi: 10.3390/rs15143534.

A. A. F. Al Ayyubi, P. Y. Aisyah, A. M. Chafsah, dan C. Nur Ramadhani, “Design And Build of A Total Dissolved Solid (Tds) Control System to Maintain Water Quality in Koi Fish Ponds,” in 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), IEEE, Nov 2023, hal. 1–3. doi: 10.1109/ICAMIMIA60881.2023.10427848.

J. M. Munguti et al., “Aqua-Feed Wastes: Impact on Natural Systems and Practical Mitigations—A Review,” J. Agric. Sci., vol. 13, no. 1, hal. 111–121, 2020, doi: 10.5539/jas.v13n1p111.

C. B. Carballeira Braña, K. Cerbule, P. Senff, dan I. K. Stolz, “Towards Environmental Sustainability in Marine Finfish Aquaculture,” Front. Mar. Sci., vol. 8, Apr 2021, doi: 10.3389/fmars.2021.666662.

K. F. Nugraha Isnoor, “Analisis Persentase Komponen Angin Pada Landasan Pacu Bandara Internasional Raja Haji Fisabilillah Tanjungpinang,” J. Ilmu dan Inov. Fis., vol. 8, no. 1, hal. 36–44, 2024, doi: 10.24198/jiif.v8i1.43730.

A. Lokman, W. Z. W. Ismail, N. A. A. Aziz, dan A. K. Ghazali, “Interactive Fuzzy Logic Interface for Enhanced Real-Time Water Quality Index Monitoring,” Algorithms, vol. 18, no. 9, hal. 591, Sep 2025, doi: 10.3390/a18090591.

S. K. Nagothu, P. Bindu Sri, G. Anitha, S. Vincent, dan O. P. Kumar, “Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture,” Aquac. Int., vol. 33, no. 1, hal. 32, Jan 2025, doi: 10.1007/s10499-024-01701-2.

H.-C. Li, K.-W. Yu, C.-H. Lien, C. Lin, C.-R. Yu, dan S. Vaidyanathan, “Improving Aquaculture Water Quality Using Dual-Input Fuzzy Logic Control for Ammonia Nitrogen Management,” J. Mar. Sci. Eng., vol. 11, no. 6, hal. 1109, Mei 2023, doi: 10.3390/jmse11061109.

H. Rastegari et al., “Internet of Things in aquaculture: A review of the challenges and potential solutions based on current and future trends,” Smart Agric. Technol., vol. 4, hal. 100187, Agu 2023, doi: 10.1016/j.atech.2023.100187.

H. Yang, M. Sun, dan S. Liu, “A hybrid intelligence model for predicting dissolved oxygen in aquaculture water,” Front. Mar. Sci., vol. 10, Jun 2023, doi: 10.3389/fmars.2023.1126556.