Fish health depends on numerous factors, therefore precise estimates are crucial. Historical data on water temperature, pH, dissolved oxygen, and fish health are used. CNNs extracted features from multidimensional input data to predict fish health. The memory neural networks relayed and mined biological and environmental variable feature correlations. The CNN-LSTM model extracted temporal and spatial trends from the time-series aquaponics dataset. AM was added to the time-series prediction model to improve LSTM accuracy. The integrated prediction model uses AM, LSTM, and CNN to focus. To verify accuracy, the CNN-LSTM-AM novel model was tested weekly and monthly. The novel model has RMSE 0.0997, MAE 0.097, MAPE 0.0154, and short-term and long-term R2 values of 92.75 and 90.25. This model allows real-time aquaponics fish health monitoring. CNN-LSTM model, recurrent neural network, long short-term memory neural network, and autoregressive integrated moving average performance were computed and visualised. Increasing aquaponics automation is proposed. Aquaponics multi-step time series fish health forecasting is improved by the provided strategy. This firms sustainability has improved thanks to the unique model's accuracy.
Begam et al. (Thu,) studied this question.