Emerging Digital Technologies for Precision Aquaculture

Yıl 2025, Cilt: 9 Sayı: 2, 188 - 194

Tolga Şahin

Öz

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.

Anahtar Kelimeler

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

Etik Beyan

Ethical review and approval were not required for this study.

Destekleyen Kurum

This research received no external funding.

Akıllı Su Ürünleri Yetiştiriciliğinde Yeni Dijital Teknolojiler

Öz

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.

Anahtar Kelimeler

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

Etik Beyan

Bu çalışma için etik kurul incelemesi ve onayı gerekmemektedir.

Destekleyen Kurum

Bu araştırma herhangi bir kurum veya kuruluş tarafından desteklenmemiştir.

Kaynakça

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