# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 4 AIoT Applications in Aquaculture
### Water Quality Management  
Water quality management is crucial in sustainable aquaculture, directly influencing aquatic species' health, growth, and welfare. Deviations in key parameters such as temperature, pH, dissolved oxygen (DO), ammonia, and turbidity can lead to significant health issues, impacting growth rates and increasing susceptibility to diseases [104]. Integrating IoT sensors and AI-based predictive analytics has become crucial for real-time monitoring and early intervention in modern aquaculture. Several studies have shown that proactive monitoring systems using deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can accurately predict critical hydrological conditions, such as degradation of water quality and oxygen depletion [105]. AI-based approaches, including hybrid CNN-LSTM-GRU models and TD-LSTM models, have been utilised for accurate temperature predictions, improving monitoring across various aquatic environments [44].  
pH levels are another crucial factor in aquaculture, influencing biochemical processes, metabolic rates, and fish health [7; 8]. AI models such as random forests and simple recurrent units (SRUs) have been applied to predict pH fluctuations, ensuring optimal conditions and preventing stress-induced health issues [106]. Similarly, salinity control is vital for maintaining osmotic balance and physiological stability in aquatic species. Advances in IoT-enabled salinity sensors have enhanced measurement accuracy, with novel transducer designs and conductivity meters providing real-time monitoring capabilities [56; 107]. Effective management of DO is also critical, as hypoxia can severely impact aquatic organisms. Predictive models integrating LightGBM, BiSRU, and attention mechanisms have improved DO forecasting, allowing for timely adjustments in high-density aquaculture systems [108]. Other approaches [109], such as genetic algorithms combined with XGBoost and CatBoost, have demonstrated strong predictive performance, though model recalibration is necessary for different environmental conditions [37].  
Turbidity affects water clarity, oxygen distribution, and fish-feeding behaviour, necessitating efficient monitoring systems. Neural networks embedded in IoT sensor nodes have been used to classify pollutants. At the same time, decision tree and support vector machine (SVM)-based models have been applied for fault diagnosis in turbidity data [110; 111]. Chlorophyll-a levels indicate phytoplankton biomass, with excessive growth leading to harmful algal blooms. AI techniques such as CNNs and LSTMs have been employed for chlorophyll monitoring, supporting early intervention strategies to prevent ecological  
Figure 3: Conceptual Framework of AIOT Applications in Aquaculture, showcasing the process from data acquisition to beneficial outputs.  
imbalances [112; 113]. Similarly, high concentrations of ammonia, a toxic byproduct of fish metabolism, can be hazardous. Ammonia levels must be regularly monitored to avoid harmful accumulations, as their toxicity increases with higher pH. AI-driven predictive models, such as general regression neural networks (GRNN) and LSTMs, have been used to predict ammonia fluctuations in high-density shrimp aquaculture [58]. Advanced approaches like XAdBaBoost combined with LSTMs have also achieved lower error rates than the conventional method [18].