IOT Monitoring Systems in Fish Farming Case Study:" University of Rwanda Fish Farming and Research Station (Ur-FFRs)"

Authors

  • Ineza Yves Assistant Lecturer, Rwanda Polytechnic-Integrated Polytechnic Regional College (IPRC-Kigali))
  • Gasana James Madson Lecturer, Rwanda Polytechnic-Integrated Polytechnic Regional College Kigali (IPRC-Kigali)
  • Irankunda Innocent Senior Instructor, Rwanda Polytechnic-Integrated Polytechnic Regional College (IPRC-Kigali)
  • Habimana Jean claude Postgraduate Student, African Center of Excellence in Internet of Things (ACEIOT) - University of Rwanda
  • Niyonsaba Maximillien Lecturer, Rwanda Polytechnic-Integrated Polytechnic Regional College Kigali (IPRC-Kigali)
  • Bitegetsimana Gedeon Assistant Lecturer, Rwanda Polytechnic-Integrated Polytechnic Regional College Kigali (IPRC-Kigali)

DOI:

https://doi.org/10.47672/ejt.1559

Keywords:

IOT, Fish Farming, Monitoring System

Abstract

Purpose: Fish farming refers to the farming of aquatic organisms such as fish. It involves cultivating freshwater and saltwater populations under controlled conditions. The purpose of this research is to provide a solution to the fish farmer by developing an application that would be easy to monitor water quality during the fish farming process. This will help the fish farmers to intervene timely, and therefore increase their production. The design and implementation of IoT Monitoring Systems in Fish farming helps to observe the farming system remotely by using different sensors for the water parameters. The research focused on developing a system for real time monitoring of culture tank water quality as a proof of concept and testing the basic functionalities of the system.

Methodology: The research adopted the Rapid application methodology, which was deemed best due to its iterative approach to applications development as it also delivers systems faster at a lower cost in time-constrained projects. This methodology was suitable for our research given the time constraints in developing the application. Secondary data was used to determine the water quality aspects that require monitoring. Interviewing the operators of the culture tank also provided information on what should be incorporated in the model. by developing an application that would be easy to monitor water quality during the fish farming process. The application dashboard is the graphical user interface that the users shall use to interact with application components.

Findings: This research proposes a solution, which is a real-time culture tank (hatchery) water quality-monitoring model, which utilizes a web application that shall be adopted by the staff of the University of Rwanda Fish Farming and Research Station and farmers. The model utilizes the IoT concept, which enables information gathering about water quality through the corresponding sensors. The status of the water quality aspects shall then be relayed on a real-time basis through a cloud platform. The farmer can then act based on the information provided, or the model can act on the farmer's behalf based on predetermined actions. The model's data can be extracted and analysed in a variety of different ways.

Recommendations: This research contributed in developing a technological solution for real time monitoring water quality aspects of culture tank (hatchery) that can  be adopted by fish farmers in Rwasave Fish Farming and Research Station by providing them with real time data whenever they are within or away from the culture tank (hatchery) site. This  helps to eliminate or minimize the risk of losing fish and wastages due late interventions.it was validated by supervisors of the project.

Downloads

Download data is not yet available.

References

Alam, I., Sharif, K., Li, F., Latif, Z., Karim, M. M., Biswas, S., ... & Wang, Y. (2020). A survey of network virtualization techniques for Internet of Things using SDN and NFV. ACM Computing Surveys (CSUR), 53(2), 1-40.

Atat, R., Liu, L., Chen, H., Wu, J., Li, H., & Yi, Y. (2017). Enabling cyber"physical communication in 5G cellular networks: challenges, spatial spectrum sensing, and cyber"security. IET Cyber"Physical Systems: Theory & Applications, 2(1), 49-54.

Ao, S. I., Kim, H. K., & Amouzegar, M. A. (2015). World Congress on Engineering and Computer Science 2015. In Conference proceedings WCECS (p. 8).

Cerny, C., & Casto, M. (2017, June). NAECON tutorials: Trusted systems and electronics. In 2017 IEEE National Aerospace and Electronics Conference (NAECON) (pp. 1-1). IEEE.

Chiu, M. C., Yan, W. M., Bhat, S. A., & Huang, N. F. (2022). Development of smart aquaculture farm management system using IoT and AI-based surrogate models. Journal of Agriculture and Food Research, 9, 100357.

Dhanda, N., Datta, S. S., & Dhanda, M. (2022). Machine Learning Algorithms. In Research Anthology on Machine Learning Techniques, Methods, and Applications (pp. 849-869). IGI Global.

Espinosa-Curiel, I., Prez-Espinosa, H., González-González, J., & Rodríguez-Jacobo, J. (2016, February). A mobile platform for remote monitoring of water quality on live fish transport containers: Lessons learned. In 2016 International Conference on Electronics, Communications and Computers (CONIELECOMP) (pp. 40-47). IEEE.

FAO, (2017). Developing an Environmental Monitoring System to Strengthen Fisheries and aquaculture Resilience and Improve Early Warning in the Lower Mekong Basin, Rome,Italy.

Glória, A., Cercas, F., & Souto, N. (2017, September). Comparison of communication protocols for low-cost Internet of Things devices. In 2017 South Eastern European Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) (pp. 1-6). IEEE

Kubler, S., Robert, J., Hefnawy, A., Främling, K., Cherifi, C., & Bouras, A. (2017). Open IoT ecosystem for sporting event management. IEEE Access, 5, 7064-7079.

Mahalik, N., & Kim, K. (2014). Aquaculture monitoring and control systems for seaweed and fish farming.

Marques, G., Pitarma, R., M. Garcia, N., & Pombo, N. (2019). Internet of things architectures,technologies, applications, challenges, and future directions for enhanced living environments and healthcare systems: a review. Electronics, 8(10), 1081

Ogudo, K. A., Saha, S. K., & Bhattacharyya, D. (Eds.). (2023). Smart Technologies in Data Science and Communication: Proceedings of SMART-DSC 2022 (Vol. 558). Springer Nature.

Patel, C. (2018). In What Ways the Mobile App is Leveraging the IoT World. Customer Think, 31.

Rauch, H., Botsford, L., & Shleser, R. (1975). Economic optimization of an aquaculture facility. IEEE Transactions on Automatic Control, 20(3), 310-319.

Rajmohan, P., & Srinivasan, P. S. S. (2019). RETRACTED ARTICLE: IoT based industrial safety measures monitoring and reporting system using accident reduction model (ARM) control algorithm. Cluster Computing, 22(Suppl 5), 11259-11269.

Raju, K. R. S. R., & Varma, G. H. K. (2017, January). Knowledge based real time monitoring system for aquaculture using IoT. In 2017 IEEE 7th international advance computing conference (IACC) (pp. 318-321). IEEE.

Shitote, Z., Wakhungu, J., & China, S. (2012). Challenges facing fish farming development in Western Kenya. Greener Journal of Agricultural Sciences, 3(5), 305-311.

Ujwala, T., Devareddy, S. G., Yamuna, S., & Vandana, S. (2020). A Review on Fish Farm aquaculture Monitoring & Controlling System. Int. J. of Recent Tech. and Eng. (IJRTE), 7, 2880-871.

Wu, J., Guo, S., Li, J., & Zeng, D. (2016). Big data meet green challenges: Greening big data. IEEE Systems Journal, 10(3), 873-887.

Downloads

Published

2023-08-13

How to Cite

Yves, I. ., Madson, G. ., Innocent, I. ., Claude, H. ., Maximillien, N. ., & Gedeon, B. . (2023). IOT Monitoring Systems in Fish Farming Case Study:" University of Rwanda Fish Farming and Research Station (Ur-FFRs)". European Journal of Technology, 7(3), 43–61. https://doi.org/10.47672/ejt.1559

Issue

Section

Articles