Emerging Digital Technologies for Precision Aquaculture

Year 2025, Volume: 9 Issue: 2, 188 - 194

Tolga Şahin

Abstract

Aquaculture is undergoing a rapid transformation, driven by the adoption of digital technologies which are enhancing efficiency, resilience, and environmental sustainability. This paper synthesizes recent empirical evidence on the application of Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), computer vision, and robotics within aquaculture systems. IoT-based water quality monitoring has been demonstrated to improve growth rates, survival, and the early recognition of anomalies, while AI/ML algorithms deliver predictive analytics for dissolved oxygen fluctuations, disease incidence risk, and selective breeding performance. Computer vision platforms enable welfare monitoring through non-invasive assessment of respiration, behaviour, and biomass, thus supporting more precise feeding protocols and reductions in feed conversion ratios. Robotics and autonomous vehicles further expand these capabilities, performing net inspections, fouling removal, and environmental surveys in offshore farms where human presence is limited. Despite these advances, obstacles remain, including sensor durability, elevated implementation cost, lack of system interoperability, and restricted transferability across different cultured species. Looking forward, future prospects emphasize the convergence of biotechnology with digital twins, AI-driven early-warning frameworks, and renewable energy–powered robotics as pathways towards more autonomous and intelligent aquaculture. Collectively, these innovations mark the emergence of precision aquaculture, where continuous sensing integrated with data-driven decision-making underpins both improved productivity and long-term sectoral sustainability.

Keywords

Artificial intelligence , Computer vision , Iot , Machine learning , Robotics

Ethical Statement

Ethical review and approval were not required for this study.

Supporting Institution

This research received no external funding.

Akıllı Su Ürünleri Yetiştiriciliğinde Yeni Dijital Teknolojiler

Abstract

Akuakültür, dijital teknolojilerin hızla benimsenmesiyle birlikte verimliliği, dayanıklılığı ve çevresel sürdürülebilirliği artıran bir dönüşümden geçmektedir. Bu makale, Nesnelerin İnterneti (IoT), yapay zekâ (AI), makine öğrenmesi (ML), bilgisayarlı görü ve robotik uygulamalarına ilişkin yakın dönem araştırma bulgularını akuakültür sistemleri bağlamında özetlemektedir.

IoT-tabanlı su kalitesi izleme, büyüme performansını ve yaşama oranlarını artırmanın yanı sıra erken anomali tespitini mümkün kılmaktadır. AI/ML algoritmaları, çözünmüş oksijen dalgalanmaları, hastalık görülme riski ve seçici ıslah performansına ilişkin tahmine dayalı analizler sunmaktadır. Bilgisayarlı görü platformları, solunum, davranış ve biyokütleyi girişimsel olmayan yöntemlerle değerlendirerek hayvan refahının izlenmesini sağlamaktadır; böylece daha isabetli besleme protokollerini desteklemekte ve yem dönüşüm oranlarını düşürmektedir. Robotik sistemler ve otonom araçlar bu yetenekleri daha da genişleterek, insan erişiminin kısıtlı olduğu açık deniz çiftliklerinde ağ kontrolleri, biyolojik kirlenmenin giderimi (biofouling) ve çevresel izleme çalışmaları gerçekleştirmektedir.

Bununla birlikte, sensör dayanıklılığı, yüksek uygulama maliyetleri, sistemler arası uyum eksikliği ve farklı yetiştirilen türler arasında sınırlı aktarılabilirlik gibi engeller sürmektedir. İleriye dönük olarak, biyoteknolojinin dijital ikizlerle entegrasyonu, yapay zekâ destekli erken uyarı sistemleri ve yenilenebilir enerjiyle çalışan robotik sistemler, daha otonom ve akıllı bir akuakültüre giden başlıca eğilimler olarak öne çıkmaktadır. Bir bütün olarak bu yenilikler, sürekli ölçüm ve izlemenin veriye dayalı karar verme ile entegre olduğu, üretkenliği artırırken sektörün uzun vadeli sürdürülebilirliğini güvence altına alan akıllı (precision) akuakültür döneminin yükselişine işaret etmektedir.

Keywords

Artificial intelligence , Computer vision , Iot , Machine learning , Robotics

Ethical Statement

Bu çalışma için etik kurul incelemesi ve onayı gerekmemektedir.

Supporting Institution

Bu araştırma herhangi bir kurum veya kuruluş tarafından desteklenmemiştir.

References

• [1] Food and Agriculture Organization of the United Nations (FAO), The State of World Fisheries and Aquaculture 2024: Blue Transformation in Action. Rome, Italy: FAO, 2024, doi: 10.4060/cd0683en.
• [2] S. K. Nagothu, P. Bindu Sri, G. Anitha, S. Vincent, and O. P. Kumar, “Advancing Aquaculture: Fuzzy Logic-Based Water Quality Monitoring and Maintenance System for Precision Aquaculture,” Aquaculture International, vol. 33, no. 1, Art. no. 32, 2024, doi: 10.1007/s10499-024-01701-2.
• [3] T. Li, J. Lu, J. Wu, Z. Zhang, and L. Chen, “Predicting Aquaculture Water Quality Using Machine Learning Approaches,” Water, vol. 14, no. 18, Art. no. 2836, 2022, doi: 10.3390/w14182836.
• [4] A. F. Zambrano, L. F. Giraldo, J. Quimbayo, B. Medina, and E. Castillo, “Machine Learning for Manually-Measured Water Quality Prediction in Fish Farming,” PLOS ONE, vol. 16, no. 8, Art. no. e0256380, 2021, doi: 10.1371/journal.pone.0256380.
• [5] E. B. Høgstedt, C. Schellewald, R. Mester, and A. Stahl, “Automated Computer Vision-Based Individual Salmon (Salmo salar) Breathing Rate Estimation (SaBRE) for Improved State Observability,” Aquaculture, vol. 595, Art. no. 741535, 2025, doi: 10.1016/j.aquaculture.2024.741535.
• [6] U. Ilyasu, Z. Sani, and T. Suleiman, “Internet of Things-Based Smart Fish Farming: Application of Smart Sensors and Computer Vision to Provide Real-Time Monitoring and Diagnosis in Aquaculture,” Journal of Basics and Applied Sciences Research, vol. 3, no. 2, pp. 70–77, 2025, doi: 10.4314/jobasr.v3i2.8.
• [7] T. Şahin, “Ecological and Geopolitical Challenges in Sustaining Global Fishmeal Supply for Aquaculture,” Marine Reports, vol. 4, no. 1, pp. 55–67, 2025, doi: 10.5281/zenodo.15764050.
• [8] M. Flores-Iwasaki, G. A. Guadalupe, M. Pachas-Caycho, S. Chapa-Gonza, R. C. Mori-Zabarburú, and J. C. Guerrero-Abad, “Internet of Things (IoT) Sensors for Water Quality Monitoring in Aquaculture Systems: A Systematic Review and Bibliometric Analysis,” AgriEngineering, vol. 7, no. 3, Art. no. 78, 2025, doi: 10.3390/agriengineering7030078.
• [9] E. B. Blancaflor and M. Baccay, “Assessment of an Automated IoT-Biofloc Water Quality Management System in the Litopenaeus vannamei’s Mortality and Growth Rate,” Automatika, vol. 63, no. 2, pp. 259–274, 2022, doi: 10.1080/00051144.2022.2031540.
• [10] T. D. Le et al., “Exploring New Frontiers: Current Status and Future Research Directions for AIoT Application in Shrimp Farming in the Vietnamese Mekong Delta,” Aquacultural Engineering, vol. 111, Art. no. 102559, 2025, doi: 10.1016/j.aquaeng.2025.102559.
• [11] A. B. Wibisono and R. Jayadi, “Experimental IoT System to Maintain Water Quality in Catfish Pond,” International Journal of Advanced Computer Science and Applications, vol. 15, no. 3, 2024, doi: 10.14569/IJACSA.2024.0150340.
• [12] L. W. K. Lim, “Implementation of Artificial Intelligence in Aquaculture and Fisheries: Deep Learning, Machine Vision, Big Data, Internet of Things, Robots and Beyond,” Journal of Computational and Cognitive Engineering, vol. 3, no. 2, pp. 112–118, 2024, doi: 10.47852/bonviewJCCE3202803.
• [13] M. A. A. M. Hridoy, C. Bordin, A. Masood, and K. Masood, “Predictive Modelling of Aquaculture Water Quality Using IoT and Advanced Machine Learning Algorithms,” Results in Chemistry, vol. 16, Art. no. 102456, 2025, doi: 10.1016/j.rechem.2025.102456.
• [14] M. S. Ahmed, T. T. Aurpa, and M. A. K. Azad, “Fish Disease Detection Using Image-Based Machine Learning Technique in Aquaculture,” Journal of King Saud University – Computer and Information Sciences, vol. 34, no. 8, pt. A, pp. 5170–5182, 2022, doi: 10.1016/j.jksuci.2021.05.003.
• [15] C. Palaiokostas, “Predicting for Disease Resistance in Aquaculture Species Using Machine Learning Models,” Aquaculture Reports, vol. 20, Art. no. 100660, 2021, doi: 10.1016/j.aqrep.2021.100660.
• [16] H. Zhao et al., “Vision-Based Dual Network Using Spatial-Temporal Geometric Features for Effective Resolution of Fish Behavior Recognition with Fish Overlap,” Aquacultural Engineering, vol. 105, Art. no. 102409, 2024, doi: 10.1016/j.aquaeng.2024.102409.
• [17] Y. Deng, H. Tan, M. Tong, D. Zhou, Y. Li, and M. Zhu, “An Automatic Recognition Method for Fish Species and Length Using an Underwater Stereo Vision System,” Fishes, vol. 7, no. 6, Art. no. 326, 2022, doi: 10.3390/fishes7060326.
• [18] B. Correia, O. Pacheco, R. J. M. Rocha, and P. L. Correia, “Image-Based Shrimp Aquaculture Monitoring,” Sensors, vol. 25, no. 1, Art. no. 248, 2025, doi: 10.3390/s25010248.
• [19] F. J. Ramírez-Coronel, E. Esquer-Miranda, O. M. Rodríguez-Elias, P. García-Hinostro, and G. C. Parra-Salazar, “A Litopenaeus vannamei Shrimp Dataset for Artificial Intelligence-Based Biomass Estimation and Organism Detection Algorithms,” Data in Brief, vol. 57, Art. no. 110964, 2024, doi: 10.1016/j.dib.2024.110964.
• [20] W. Akram, A. Casavola, N. Kapetanović, and N. Miškovic, “A Visual Servoing Scheme for Autonomous Aquaculture Net Pens Inspection Using ROV,” Sensors, vol. 22, no. 9, Art. no. 3525, 2022, doi: 10.3390/s22093525.
• [21] B. O. A. Haugaløkken, O. Nissen, M. B. Skaldebø, S. J. Ohrem, and E. Kelasidi, “Low-Cost Sensor Technologies for Underwater Vehicle Navigation in Aquaculture Net Pens,” IFAC-PapersOnLine, vol. 58, no. 20, pp. 87–94, 2024, doi: 10.1016/j.ifacol.2024.10.037.
• [22] Q. Zhang, N. Bloecher, L. D. Evjemo, M. Føre, B. Su, E. Eilertsen, M. A. Mulelid, and E. Kelasidi, “Farmed Atlantic salmon (Salmo salar L.) avoid intrusive objects in cages: The influence of object shape, size and colour, and fish length,” Aquaculture, vol. 581, p. 740429, Feb. 2024, doi: 10.1016/j.aquaculture.2023.740429.
• [23] I. Stenius et al., “A System for Autonomous Seaweed Farm Inspection with an Underwater Robot,” Sensors, vol. 22, no. 13, Art. no. 5064, 2022, doi: 10.3390/s22135064.
• [24] Y. Ji, Y. Wei, J. Liu, and D. An, “Design and Realization of a Novel Hybrid-Drive Robotic Fish for Aquaculture Water Quality Monitoring,” Journal of Bionic Engineering, vol. 20, no. 2, pp. 543–557, 2023, doi: 10.1007/s42235-022-00282-1.
• [25] M. Vasileiou, N. Manos, and E. Kavallieratou, “MURA: A Multipurpose Underwater Robotic Arm Mounted on Kalypso UUV in Aquaculture,” Marine Systems & Ocean Technology, vol. 18, no. 3, pp. 111–123, 2023, doi: 10.1007/s40868-023-00129-2.
• [26] L. Liu, W. Cheng, and H.-W. Kuo, “A Narrative Review on Smart Sensors and IoT Solutions for Sustainable Agriculture and Aquaculture Practices,” Sustainability, vol. 17, no. 12, Art. no. 5256, 2025, doi: 10.3390/su17125256.
• [27] J.-Y. Lin, H.-L. Tsai, and W.-H. Lyu, “An Integrated Wireless Multi-Sensor System for Monitoring the Water Quality of Aquaculture,” Sensors, vol. 21, no. 24, p. 8179, 2021, doi: 10.3390/s21248179.
• [28] S. Bhattacharya et al., “An Advanced Internet of Things (IoT)-based Water Quality Monitoring Architecture for Sustainable Aquaculture Leveraging Long Range Wide Area Network (LoRaWAN) Communication Protocol,” ES Gen., vol. 10, Art. no. 1780, 2025, doi: 10.30919/esg1780.
• [29] L. Parri, S. Parrino, G. Peruzzi, and A. Pozzebon, “Low Power Wide Area Networks (LPWAN) at Sea: Performance Analysis of Offshore Data Transmission by Means of LoRaWAN Connectivity for Marine Monitoring Applications,” Sensors, vol. 19, no. 14, Art. no. 3239, 2019, doi: 10.3390/s19143239.
• [30] W. Hassan, M. Føre, J. B. Ulvund, and J. A. Alfredsen, “Internet of Fish: Integration of acoustic telemetry with LPWAN for efficient real-time monitoring of fish in marine farms,” Computers and Electronics in Agriculture, vol. 163, Art. no. 104850, 2019, doi: 10.1016/j.compag.2019.06.005.
• [31] H. Bates, M. Pierce, and A. Benter, “Real-Time Environmental Monitoring for Aquaculture Using a LoRaWAN-Based IoT Sensor Network,” Sensors, vol. 21, no. 23, p. 7963, 2021, doi: 10.3390/s21237963.
• [32] C. F. Soon, M. Jamia’an, N. M. Sunar, S. N. H. Arifin, K. G. Tay, C. Heng, C. H. See, N. H. M. Nayan, and K. S. Tee, “Smart sensing and anomaly detection for microalgae culture based on LoRaWAN sensors and LSTM autoencoder,” Aquaculture International, vol. 33, p. 438, 2025, doi: 10.1007/s10499-025-02104-7.
• [33] G. E. Quintanilla-Villanueva, J. Maldonado, D. Luna-Moreno, J. M. Rodríguez-Delgado, J. F. Villarreal-Chiu, and M. M. Rodríguez-Delgado, “Progress in Plasmonic Sensors as Monitoring Tools for Aquaculture Quality Control,” Biosensors, vol. 13, no. 1, p. 90, 2023, doi: 10.3390/bios13010090.
• [34] S. Shreesha, M. M. M. Pai, R. M. Pai, and U. Verma, “Pattern Detection and Prediction Using Deep Learning for Intelligent Decision Support to Identify Fish Behaviour in Aquaculture,” Ecological Informatics, vol. 78, Art. no. 102287, 2023, doi: 10.1016/j.ecoinf.2023.102287.
• [35] H. Manoharan, Y. Teekaraman, P. R. Kshirsagar, S. Sundaramurthy, and A. Manoharan, “Examining the Effect of Aquaculture Using Sensor-Based Technology with Machine Learning Algorithm,” Aquaculture Research, vol. 51, no. 11, pp. 4748–4758, 2020, doi: 10.1111/are.14821.
• [36] A. Davis, P. S. Wills, J. E. Garvey, W. Fairman, M. A. Karim, and B. Ouyang, “Developing and Field-Testing Path Planning for Robotic Aquaculture Water Quality Monitoring,” Applied Sciences, vol. 13, no. 5, Art. no. 2805, 2023, doi: 10.3390/app13052805.
• [37] J. Sverdrup-Thygeson, E. Kelasidi, K. Y. Pettersen, and J. T. Gravdahl, “The underwater swimming manipulator — a bioinspired solution for subsea operations,” IEEE Journal of Oceanic Engineering, vol. 43, no. 2, pp. 402–417, Apr. 2018, doi: 10.1109/JOE.2017.2768108.
• [38] E. Kelasidi, P. Liljebäck, K. Y. Pettersen, and J. T. Gravdahl, “Innovation in underwater robots: Biologically inspired swimming snake robots,” IEEE Robotics & Automation Magazine, vol. 23, no. 1, pp. 44–62, Mar. 2016, doi: 10.1109/MRA.2015.2506121.
• [39] Y. Wu, Y. Duan, Y. Wei, D. An, and J. Liu, “Application of intelligent and unmanned equipment in aquaculture: A review,” Computers and Electronics in Agriculture, vol. 199, Art. no. 107201, Aug. 2022, doi: 10.1016/j.compag.2022.107201.
• [40] N. A. Ubina and S.-C. Cheng, “A Review of Unmanned System Technologies with Its Application to Aquaculture Farm Monitoring and Management,” Drones, vol. 6, no. 1, Art. no. 12, 2022, doi: 10.3390/drones6010012.
• [41] C. Wang, Z. Li, T. Wang, X. Xu, X. Zhang, and D. Li, “Intelligent Fish Farm—The Future of Aquaculture,” Aquaculture International, vol. 29, no. 6, pp. 2681–2711, 2021, doi: 10.1007/s10499-021-00773-8.