Aquaculture based on the Internet of Things (IoT) is a growing field of interest in the fishing industry. The IoT technology is advancing the agriculture 4.0 era, and yet, Aquaculture 4.0 is a lagging field in many countries. This article presents results obtained so far from ongoing research of published work highlighting water quality monitoring in fishponds. This analysis was performed extensively from May to December 2020 by meticulously selecting a total of 30 internationally published research papers. This review is divided into five categories: (1) recent research (2011–2020), (2) aquaculture environments, (3) research approaches, (4) most common water quality parameters and (5) forms of the solution provided. Most of the published research concentrated on inland aquaculture (81%), while research articles on marine aquaculture species accounted for 19% of papers reviewed so far. The framework and architecture approach (48%) was the most widely practised research approach in IoT-based aquaculture for water quality monitoring. There is a need for long-term experimental research to identify the challenges and suggest appropriate solutions. With regards to water quality parameters, temperature (20%), dissolved oxygen (18%) and pH (17%) are the topmost prioritised water quality parameters considered in the IoT-based aquaculture. Finally, real-time monitoring (50%) is offered generally as a form of a solution while autonomous (3%) monitoring can be a unique solution. The findings from this study are expected to support the aquaculture industry, researchers, practitioners and decision-makers in the long run.

中文翻译：

---

基于物联网 (IoT) 的水产养殖：物联网在水质监测中的应用概述

基于物联网 (IoT) 的水产养殖是渔业中一个日益受到关注的领域。物联网技术正在推进农业 4.0 时代，然而，水产养殖 4.0 在许多国家是一个落后的领域。本文介绍了迄今为止从正在进行的研究中获得的结果，这些研究突出了鱼塘水质监测。这项分析在 2020 年 5 月至 2020 年 12 月期间广泛进行，精心挑选了总共 30 篇国际发表的研究论文。本综述分为五类：(1) 近期研究（2011-2020 年），(2) 水产养殖环境，(3) 研究方法，(4) 最常见的水质参数和 (5) 所提供解决方案的形式。大多数已发表的研究集中于内陆水产养殖（81%），而关于海洋水产养殖物种的研究文章占迄今为止审查的论文的 19%。框架和架构方法 (48%) 是基于物联网的水产养殖水质监测中应用最广泛的研究方法。需要进行长期的实验研究，以确定挑战并提出适当的解决方案。在水质参数方面，温度（20%）、溶解氧（18%）和 pH 值（17%）是物联网水产养殖中最优先考虑的水质参数。最后，实时监控（50%）通常作为解决方案的一种形式提供，而自主（3%）监控可能是一种独特的解决方案。从长远来看，这项研究的结果有望为水产养殖业、研究人员、从业者和决策者提供支持。