IoT-Based Water Quality Monitoring: An Application for Optimal Aquaculture Management

Abstract

Ineffective water quality monitoring and various farming difficulties causing fish kill events are persistent challenges in aquafarming, significantly impacting fish production. This paper presents a scalable, mobile IoT-based aquaculture monitoring application that tackles the challenges so farmers can enhance fish production, reduce labour costs, and improve food security. With the help of sensors, the application can monitor water quality indicators in real-time for optimal fish growth. In addition, we include predictive analytics and alert messages to mitigate aeration system failures, algal blooms, and unpredictable water changes. The application is a four-module architecture with data acquisition, communication, data processing, and user interface, supported by efficient data gathering, storage, and visualisation. For the application development methodology, we use Evolutionary Prototyping to refine the application repeatedly with the help of user feedback. This work addresses limitations through future work in automation, network backup, and machine learning integration.