Prince Jebedass Isaac Chandran , Hana Ahmed Khalil , PK Hashir , Veerasingam S
{"title":"Smart technologies in aquaculture: An integrated IoT, AI, and blockchain framework for sustainable growth","authors":"Prince Jebedass Isaac Chandran , Hana Ahmed Khalil , PK Hashir , Veerasingam S","doi":"10.1016/j.aquaeng.2025.102584","DOIUrl":null,"url":null,"abstract":"<div><div>Aquaculture is vital for global food security, yet traditional methods often struggle with inefficiencies, disease outbreaks, and environmental concerns. This review explores how an integrated framework of Internet of Things (IoT), Artificial Intelligence (AI), and blockchain technology can transform aquaculture into a more efficient, sustainable, and intelligent industry. IoT enhances real-time monitoring and precision feeding, AI optimizes disease detection and resource management, and blockchain ensures transparency and traceability across the supply chain. This novel approach not only mitigates existing challenges but also fosters predictive analytics, automation, and data-driven decision-making. Although high costs and technical constraints pose challenges, adopting emerging technologies like 5 G, edge computing, and decentralized ledgers can accelerate industry-wide adoption and enhance resilience and scalability. Future innovations in AI-driven aquaculture must focus on adaptive machine learning models and cross-disciplinary collaborations to ensure resilience and scalability in the face of global demands.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"111 ","pages":"Article 102584"},"PeriodicalIF":4.3000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aquacultural Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0144860925000731","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Aquaculture is vital for global food security, yet traditional methods often struggle with inefficiencies, disease outbreaks, and environmental concerns. This review explores how an integrated framework of Internet of Things (IoT), Artificial Intelligence (AI), and blockchain technology can transform aquaculture into a more efficient, sustainable, and intelligent industry. IoT enhances real-time monitoring and precision feeding, AI optimizes disease detection and resource management, and blockchain ensures transparency and traceability across the supply chain. This novel approach not only mitigates existing challenges but also fosters predictive analytics, automation, and data-driven decision-making. Although high costs and technical constraints pose challenges, adopting emerging technologies like 5 G, edge computing, and decentralized ledgers can accelerate industry-wide adoption and enhance resilience and scalability. Future innovations in AI-driven aquaculture must focus on adaptive machine learning models and cross-disciplinary collaborations to ensure resilience and scalability in the face of global demands.
期刊介绍:
Aquacultural Engineering is concerned with the design and development of effective aquacultural systems for marine and freshwater facilities. The journal aims to apply the knowledge gained from basic research which potentially can be translated into commercial operations.
Problems of scale-up and application of research data involve many parameters, both physical and biological, making it difficult to anticipate the interaction between the unit processes and the cultured animals. Aquacultural Engineering aims to develop this bioengineering interface for aquaculture and welcomes contributions in the following areas:
– Engineering and design of aquaculture facilities
– Engineering-based research studies
– Construction experience and techniques
– In-service experience, commissioning, operation
– Materials selection and their uses
– Quantification of biological data and constraints