Editorial
Brackish water parameters monitoring dashboard using Internet of things and industry 4.0
@ARTICLE{10.4108/eetiot.6860, author={V. Sowmiya and G. R. Kanagachidambaresan}, title={Brackish water parameters monitoring dashboard using Internet of things and industry 4.0}, journal={EAI Endorsed Transactions on Internet of Things}, volume={11}, number={1}, publisher={EAI}, journal_a={IOT}, year={2024}, month={12}, keywords={Brackish aquaculture, Machine Learning, Long Short-Term Memory, Water quality management, Web-based application}, doi={10.4108/eetiot.6860} }- V. Sowmiya
G. R. Kanagachidambaresan
Year: 2024
Brackish water parameters monitoring dashboard using Internet of things and industry 4.0
IOT
EAI
DOI: 10.4108/eetiot.6860
Abstract
INTRODUCTION: Brackish water aquaculture plays a crucial role in meeting the growing global demand for seafood. It offers an opportunity to diversify aquaculture production and reduce pressure on overexploited marine resources. OBJECTIVES: By harnessing the unique properties of brackish ecosystems, this practice contributes to food security, economic growth, and sustainable resource management, while also promoting the conservation of valuable marine habitats. The development of a cutting-edge Indigenous Water Quality Monitoring Prototype named "Aqua BuoySis" for precision brackish water aquaculture utilizing machine intelligence. METHODS: The prototype integrates sensors for Dissolved Oxygen (DO), pH, Temperature, Turbidity, and Total Dissolved Solids (TDS). These sensors are calibrated using a dynamic temperature-based machine-learning approach to ensure accuracy in real-time environments. Sensor calibration constants are uploaded to a server for comprehensive data calibration. RESULTS: The system collects data at 20-second intervals, associating it with specific pond IDs. Data refinement is achieved through Long Short-Term Memory (LSTM) processing. An Android and Web application, available in native languages such as Tamil and Telugu, has been developed to provide live updates to aqua farmers, facilitating informed decision-making. CONCLUSION: This technology represents a significant step towards enhancing precision in brackish water aquaculture through the fusion of machine intelligence and water quality management.
Copyright © 2024 V. Sowmiya and G. R. Kanagachidambaresan, licensed to EAI. This open-access article is distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transforming, and building upon the material in any medium so long as the original work is properly cited.
