An Analysis of Water Quality Monitoring in Aquaculture Using Diverse Approaches

The creation of a water quality monitoring by digital image analysis and developments in prawn farming and aquaculture in Kothapatnam, Ongole, Andhra Pradesh are both covered in this extensive research. In order to manage aquaculture in a sustainable and profitable manner, water quality prediction (WQP) is essential. This study presents new deep learning (DL) models for aquaculture WQP that combine CNN, LSTM, and GRU, and are specifically designed for this purpose. While GRU and LSTM are great at learning long-term relationships in time series data, CNNs are great at capturing the features of aquaculture water quality. By running trials on two separate water quality datasets, we were able to thoroughly examine how hyperparameters affected the performance of these hybrid DL models. The suggested CNN, LSTM & GRU hybrid models are tested against CNN, LSTM, and GRU models. In terms of computing speed and prediction accuracy, our research shows that the hybrid LSTM model is the best option for forecasting aquaculture water quality.