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Internet of Things IoT based aquaculture: an overview of IoT application on water quality monitoring


Citation

Prapti, Dipika Roy and Mohamed Shariff, Abdul Rashid and Che Man, Hasfalina and Mohamed Ramli, Norulhuda and Perumal, Thinagaran and Mohamed Shariff (2021) Internet of Things IoT based aquaculture: an overview of IoT application on water quality monitoring. Reviews in Aquaculture, 14 (2). 979 - 992. ISSN 1753-5131

Abstract

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.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
Faculty of Engineering
Institute of Bioscience
DOI Number: https://doi.org/10.1111/raq.12637
Publisher: John Wiley & Sons
Keywords: Aquaculture 4.0; Aquaculture water quality; Aqua-tech; Industrial Revolution 4.0; Smart aquaculture; Water quality monitoring
Depositing User: Ms. Che Wa Zakaria
Date Deposited: 15 Dec 2023 23:42
Last Modified: 15 Dec 2023 23:42
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1111/raq.12637
URI: http://psasir.upm.edu.my/id/eprint/101964
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