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DOI: 10.14569/IJACSA.2021.0120608
PDF

IoT based Smart Water Quality Prediction for Biofloc Aquaculture

Author 1: Md. Mamunur Rashid
Author 2: Al-Akhir Nayan
Author 3: Sabrina Afrin Simi
Author 4: Joyeta Saha
Author 5: Md. Obaidur Rahman
Author 6: Muhammad Golam Kibria

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 6, 2021.

  • Abstract and Keywords
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Abstract: Traditional fish farming faces several challenges, including water pollution, temperature imbalance, feed, space, cost, etc. Biofloc technology in aquaculture transforms the manual into an advanced system that allows the reuse of unused feed by converting them into microbial protein. The objective of the research is to propose an IoT-based solution to aquaculture that increases efficiency and productivity. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI), and sends notifications to the user. The proposed system has been implemented and tested to validate and achieve a satisfactory result.

Keywords: Smart aquaculture system; biofloc technology; machine learning; life below water

Md. Mamunur Rashid, Al-Akhir Nayan, Sabrina Afrin Simi, Joyeta Saha, Md. Obaidur Rahman and Muhammad Golam Kibria. “IoT based Smart Water Quality Prediction for Biofloc Aquaculture”. International Journal of Advanced Computer Science and Applications (IJACSA) 12.6 (2021). http://dx.doi.org/10.14569/IJACSA.2021.0120608

@article{Rashid2021,
title = {IoT based Smart Water Quality Prediction for Biofloc Aquaculture},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120608},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120608},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {6},
author = {Md. Mamunur Rashid and Al-Akhir Nayan and Sabrina Afrin Simi and Joyeta Saha and Md. Obaidur Rahman and Muhammad Golam Kibria}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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