![]() ![]() ![]() Implementing WQI in the IoT monitoring dashboard using the MGGP has significantly addressed the present challenges in deploying other complex AI-based models for WQI, such as the FIS and neural networks in low-computing capable platforms.ĭetermination of excess feed pellet count is an essential indication of fish feeding behavior responses. Results have shown that the implemented novel system can accurately predict the WQI IoT monitoring with an average of R ² and RMSE of 0.9112 and 0.6441, respectively. ![]() Thus, the development of the IoT-based WQI fuzzy inference system (FIS) was simplified by the multi-gene genetic programming (MGGP) to search for non-linear equations given the simulated WQP fuzzy sets. Although several monitoring systems for water quality parameters (WQP) use IoT, there is no existing WQI IoT monitoring for Oreochromis niloticus because the current WQI models are too complex to be deployed for low-level computing platforms such as the IoT modules and dashboards. Real-time water quality index (WQI) monitoring – a simplified single variable indication of water quality (WQ) – is vital in attaining a sustainable future in precision aquaculture. ![]()
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