AquaPulse: Leveraging Artificial Intelligence for the Comprehensive Management of Frontosa Freshwater Aquarium
The growing interest in aquarium keeping, particularly for species like Frontosa, has underscored the need for more sophisticated tools to maintain optimal aquatic environments. Traditional aquarium management demands continuous monitoring and precise control of water quality, temperature, and feeding schedules, posing significant challenges for enthusiasts. This study introduces AquaPulse, an innovative IoT-powered system integrated with artificial intelligence (AI) to enhance the management of Frontosa habitats in aquariums. The system combines IoT sensors, a mobile application, and AI algorithms to provide real-time monitoring and automated control of key parameters, including water quality and feeding schedules. AquaPulse aims to streamline aquarium maintenance, ensuring a stable and healthy environment for Frontosa cichlids. This research focuses on developing a user-friendly IoT-based system specifically designed for Frontosa, incorporating real-time water quality monitoring and anomaly detection, predictive maintenance alerts, and automated feeding optimization. A prototype was developed and evaluated through user testing involving 30 participants, ranging from novice to expert aquarium keepers. Results demonstrated that AquaPulse significantly reduced manual effort, enhanced the user experience, and effectively maintained optimal water conditions. The findings highlight the potential of integrating IoT and AI technologies to revolutionize aquarium management, providing a model that can be extended to other aquatic species and setups. This study contributes to the development of smart home technologies in aquarium care, fostering innovation and sustainable practices in ornamental fishkeeping.
Authors
- William P. Rey (ORCID: https://orcid.org/0000-0002-1345-2335)
- Kieth Wilhelm Jan D. Rey (ORCID: https://orcid.org/0009-0000-2087-3122)
- Althea Jodie B. Labayan
Institutions
- Mapúa University (PH)
Publication Details
- Published
- 2024-10-18
- DOI
- https://doi.org/10.1109/iccis63642.2024.10779441
- Primary Topic
- Water Quality Monitoring Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00