Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Nov 4:51:109761.
doi: 10.1016/j.dib.2023.109761. eCollection 2023 Dec.

Real-time dataset of pond water for fish farming using IoT devices

Md Monirul Islam  1
Affiliations

Real-time dataset of pond water for fish farming using IoT devices

Md Monirul Islam. Data Brief. .

Abstract

This paper introduces a real-time water quality dataset of five ponds for fish farming obtained through an IoT framework for monitoring the aquatic environmental conditions. It utilizes sensors and an Arduino microcontroller to collect data on pH, temperature, and turbidity in pond water in Jamalpur District, Bangladesh. The data is stored in an IoT cloud platform named ThingSpeak and analyzed using 10 machine learning algorithms. The dataset consists of 4 columns and 40,280 rows, where pH, temperature, turbidity, and fish are recorded. Fish represents the target variable, while the others serve as independent variables. Within the dataset, there are 11 distinct fish categories including sing, silver carp, Katla, prawn, karpio, shrimp, rui, pangas, tilapia, magur, and koi. Results showed that only three ponds are suitable for fish farming among five ponds and the Random Forest algorithm performs the best. The study also includes details of the IoT system's hardware. This dataset will be useful for researchers and fish farmers to predict fish survival.

Keywords: Aquatic biology; IoT sensor; Smart fish farming.

PubMed Disclaimer

Figures

Fig 1
Fig. 1
11 fishes.
Fig 2
Fig. 2
Workflow of designed method.
Fig 3
Fig. 3
Block diagram of proposed methodology.
Fig 4
Fig. 4
Some experimental images.
See this image and copyright information in PMC

References

    1. Islam M.M., Kashem M.A., Alyami S.A., Moni M.A. Monitoring water quality metrics of ponds with IoT sensors and machine learning to predict fish species survival. Microprocess. Microsyst. 2023;102 doi: 10.1016/j.micpro.2023.104930. - DOI
    1. Islam M.M, Kashem M.A., Uddin J. Fish survival prediction in an aquatic environment using random forest model. Int. J. Artif. Intell. 2021;10:614–622. doi: 10.11591/ijai.v10.i3. - DOI
    1. Adriman M.Fitria, Afdhal A., Fernanda A.Y. An IoT-based system for water quality monitoring and notification system of aquaculture prawn pond. International Conference on Communication, Networks and Satellite (COMNETSAT); Solo, Indonesia; 2022. - DOI
    1. Islam M.M., Kashem M.A., Uddin J. An internet of things framework for real-time aquatic environment monitoring using an Arduino and sensors. Int. J. Electr. Comput. Eng. 2022;12:826–833. doi: 10.11591/ijece.v12i1. - DOI
    1. Islam M.M., Uddin J., Kashem M.A., Rabbi F., Hasnat M.W. Intelligent Human Computer Interaction. IHCI. Vol. 2020. 2021. Design and implementation of an IoT system for predicting aqua fisheries using Arduino and KNN. Lecture Notes in Computer Science. - DOI