水产养殖中的智能技术:实现可持续增长的综合物联网、人工智能和区块链框架

IF 4.3 2区 农林科学 Q2 AGRICULTURAL ENGINEERING
Prince Jebedass Isaac Chandran , Hana Ahmed Khalil , PK Hashir , Veerasingam S
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引用次数: 0

摘要

水产养殖对全球粮食安全至关重要,但传统方法往往存在效率低下、疾病暴发和环境问题。本文探讨了物联网(IoT)、人工智能(AI)和区块链技术的集成框架如何将水产养殖业转变为一个更高效、可持续和智能的产业。物联网增强了实时监控和精准喂养,人工智能优化了疾病检测和资源管理,区块链确保了整个供应链的透明度和可追溯性。这种新颖的方法不仅减轻了现有的挑战,而且还促进了预测分析、自动化和数据驱动的决策。尽管高成本和技术限制带来了挑战,但采用5 G、边缘计算和去中心化分类账等新兴技术可以加速整个行业的采用,并增强弹性和可扩展性。人工智能驱动的水产养殖的未来创新必须侧重于自适应机器学习模型和跨学科合作,以确保面对全球需求的弹性和可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart technologies in aquaculture: An integrated IoT, AI, and blockchain framework for sustainable growth
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.
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来源期刊
Aquacultural Engineering
Aquacultural Engineering 农林科学-农业工程
CiteScore
8.60
自引率
10.00%
发文量
63
审稿时长
>24 weeks
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
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