An Intuitive Remote Monitoring Framework for Water Quality in Fish Pond using Cloud Computing

Abstract Accuracy in prediction of water quality is a recent research domain gaining popularity smart aquaculture. In aquaculture, the modifications parameters possess nonlinearity, dynamicity, unstableness and complexity due to open environment with its surroundings. Conventional methods have several disadvantages like poor generalization, lower accuracy high time complexity. By considering these issues, Novel method interfaced (IoT)Internet Things termed as Low-Cost Real-Time Monitoring System (LCRTMS) proposed predict temperature, pH, DO, Ammonia. Multi-sensors Blynk private cloud integrated framework are used for collecting data real-time enhancing remote monitoring capabilities. Experimental results indicate that predicting pH ammonia can attain 98.56% 0.257 seconds 98.97% 0.301seconds short-term prediction.