Emerging Digital Technologies for Smart Aquaculture

Abstract

Digital technologies, as seen in all industries, have great potential to increase present benefits to an extended level. In this context, together with their apparent advantages, they have swiftly and continuously transformed the aquaculture industry into a more modern one in terms of productivity, resilience, and environmental sustainability. The present review, which is a narrative, is intended to address those improvements by reporting the latest empirical findings on the use of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), computer vision (CV), and robotic applications in aquaculture systems. According to the reviewed literature, IoT-based water quality monitoring has been demonstrated to cause improved growth rates, survival, and early detection of anomalies in farmed aquatic animals, while AI/ML algorithms, in parallel, predict changes in the levels of dissolved oxygen, incidences of disease risks, and selective breeding performances. Through non-invasive evaluation of respiration, behavior, and biomass, CV platforms facilitate comprehensive welfare monitoring, consequently supporting more precise feeding applications and better feed conversion ratios. Robotics and autonomous vehicles/tools, which carry out environmental surveys, fouling removal, and net inspections in offshore farms with very little human presence, lead to enhanced operability and vision. In spite of these developments, some issues such as sensor durability/robustness, higher implementation costs, system inter-operability restrictions, and limited transferability over various aquatic species to be farmed, are regretfully present. Hereby, in the future, biotechnology with digital twins, AI-supported early warning systems, and robotics powered by renewable energy seem to be the main path toward a more autonomous and intelligent aquaculture.

Keywords

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

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

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