Integrating AIoT Technologies in Aquaculture: A Systematic Review DOI Creative Commons
Fahmida Wazed Tina, Nasrin Afsarimanesh, Anindya Nag

et al.

Future Internet, Journal Year: 2025, Volume and Issue: 17(5), P. 199 - 199

Published: April 30, 2025

The increasing global demand for seafood underscores the necessity sustainable aquaculture practices. However, several challenges, including rising operational costs, variable environmental conditions, and threat of disease outbreaks, impede progress in this field. This review explores transformative role Artificial Intelligence Things (AIoT) mitigating these challenges. We analyse current research on AIoT applications aquaculture, with a strong emphasis use IoT sensors real-time data collection AI algorithms effective analysis. Our focus areas include monitoring water quality, implementing smart feeding strategies, detecting diseases, analysing fish behaviour, employing automated counting techniques. Nevertheless, gaps remain, particularly regarding integration broodstock management, development multimodal systems, challenges model generalization. Future advancements should prioritise adaptability, cost-effectiveness, sustainability while emphasizing importance advanced biosensing capabilities, digital twin technologies. In conclusion, presents substantial opportunities enhancing practices, successful implementation will depend overcoming related to scalability, cost, technical expertise, improving models’ ensuring sustainability.

Language: Английский

Integration of Artificial Intelligence and Robotics into the industrial sector DOI
Vugar Abdullayev,

Ajesh Faizal,

Irada Seyidova

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 209 - 209

Published: Jan. 14, 2025

The 4th industrial revolution is driven by the implementation of automated robots and artificial intelligence (AI) to enhance efficiency, accuracy, safety. This integration encompasses several vital domains like optimizing supply chain, interaction between human on shop floor, predictive maintenance, automation repetitive tasks, customisation, behaviour design, safety management, data analysis, etc. AI-enabled perform tasks at very high precision, reducing chances error allowing workers focus more complex tasks. Automated upkeep utilizes AI determine time machinery will likely fail, which minimizes downtime maintenance costs. testing AI-driven vision systems support quality control ensuring a balanced product. improves chain processes, logistics inventory management. Collaboration humans collaborative robot’s results in safer productive environments with people working alongside each other. Artificial Intelligence plays an important role making smarter decisions, analysing effectively, providing valuable information that can be used improve operations. Manufacturing customization flexibility are reliant adaptive ability manufacture personalized products means productivity. Safe Risk Management consolidated because work dangerous scenarios models assess potential dangers. Despite challenges including labour displacement, cybersecurity, ethics, stemming from this technology, these all potentially available your terms. article reviews broader impacts have had sector, placing emphasis it could lead towards as well key elements consider before implementing it.

Language: Английский

Citations

0
Yapay Zeka Uygulamalarının Mavi Yüzgeçli Orkinos (Thunnus Thynnus (Linnaeus, 1758))’un Avcılığı ve Yetiştiriciliği’nin Rolü DOI
Oğulcan Kemal Sagun, Hülya Sayğı

Menba Kastamonu Üniversitesi Su Ürünleri Fakültesi Dergisi, Journal Year: 2025, Volume and Issue: 11(1), P. 96 - 115

Published: March 28, 2025

Yapay Zeka (AI); öğrenme, problem çözme ve karar verme gibi tipik olarak insan zekası gerektiren görevleri yerine getirebilen bilgisayar sistemlerinin geliştirilmesi uygulanması anlamına gelmektedir son yıllarda birçok sektörde kullanımı yaygınlaşmıştır. zeka; balık yetiştiriciliğinde büyümesi sağlığının anlaşılmasını yönetimini önemli ölçüde artırabilecek gerçek zamanlı izleme, veri analitiği, tahmine dayalı modelleme destek sistemleri için fırsatlar sunmaktadır. zekanın orkinos avcılığı et kalitesinin belirlenmesinde de kullanılmaya başlandığı görülmektedir. Ton balığının kalitesini değerlendiren bir AI sistemi olan TUNA SCOPE, Cermaq Umitron Corporation şirketlerin sağlığını refahını iyileştirmek çeşitli girişimlerde bulundukları AI'nın su ürünleri yetiştiriciliğine entegrasyonunun, işgücü maliyetlerini çevresel etkileri azaltırken verimliliği artıran odaklı kararlara olanak tanıyarak sürdürülebilir uygulamalarda devrim yaratması beklenmektedir. Çalışmamızın amacı; yapay zeka kullanımı, balıkçılık yetiştiriciliğindeki orkinoslarda ile ilgili yapılmış çalışmaların detaylı şekilde incelenerek sunmak ileride yapılacak uygulamaları alt yapı oluşturmaktır.

Citations

0
Review of state-of-the-art improvements in recirculating aquaculture systems: Insights into design, operation, and statistical modeling approaches DOI Creative Commons
Subha M. Roy, Hyun Soo Choi, Tae Ho Kim

et al.

Aquaculture, Journal Year: 2025, Volume and Issue: unknown, P. 742545 - 742545

Published: April 1, 2025

Language: Английский

Citations

0
Artificial intelligence in veterinary and animal science: applications, challenges, and future prospects DOI
Navid Ghavi Hossein‐Zadeh

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 235, P. 110395 - 110395

Published: April 16, 2025

Language: Английский

Citations

0
Integrating AIoT Technologies in Aquaculture: A Systematic Review DOI Creative Commons
Fahmida Wazed Tina, Nasrin Afsarimanesh, Anindya Nag

et al.

Future Internet, Journal Year: 2025, Volume and Issue: 17(5), P. 199 - 199

Published: April 30, 2025

The increasing global demand for seafood underscores the necessity sustainable aquaculture practices. However, several challenges, including rising operational costs, variable environmental conditions, and threat of disease outbreaks, impede progress in this field. This review explores transformative role Artificial Intelligence Things (AIoT) mitigating these challenges. We analyse current research on AIoT applications aquaculture, with a strong emphasis use IoT sensors real-time data collection AI algorithms effective analysis. Our focus areas include monitoring water quality, implementing smart feeding strategies, detecting diseases, analysing fish behaviour, employing automated counting techniques. Nevertheless, gaps remain, particularly regarding integration broodstock management, development multimodal systems, challenges model generalization. Future advancements should prioritise adaptability, cost-effectiveness, sustainability while emphasizing importance advanced biosensing capabilities, digital twin technologies. In conclusion, presents substantial opportunities enhancing practices, successful implementation will depend overcoming related to scalability, cost, technical expertise, improving models’ ensuring sustainability.

Language: Английский

Citations

0