# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 5 Research Gaps
However, there is a lack of real-time, multimodal AI systems that integrate information from multiple sensor inputs to provide a more holistic understanding of the aquaculture environment. Such systems would combine data from sources such as water quality sensors (measuring parameters like temperature, pH, and dissolved oxygen), video cameras (for observing fish behaviour), acoustic sensors (for detecting fish sounds related to stress or disease), and even genetic profiling. Integrating these diverse data streams through AI analysis would allow for identifying subtle correlations and patterns that single-source analysis might miss. This capability would enable more accurate and timely decision-making, for example, by predicting disease outbreaks early through the correlation of water quality changes and behavioural anomalies, or by optimizing feeding strategies based on genetic information and growth rates. The development of these multimodal AI systems is crucial for advancing aquaculture management. 3. Scarcity of AI Solutions for Crustacean Aquaculture: While numerous studies have explored AI applications for fish species, AI solutions are relatively scarce and tailored to crustacean aquaculture, particularly for species like mud crabs. Crustacean aquaculture presents unique challenges that necessitate specialised AI approaches. For instance, the complex life cycle of crustaceans, with its multiple larval stages, demands specific monitoring techniques. AI offers the potential to develop automated systems for monitoring crustacean broodstock, tracking reproductive cycles, and assessing health. It can also be applied to analyse microscopic images of larvae to evaluate their health and predict survival rates, enabling early interventions to improve production yields. Moreover, AI can contribute to optimizing environmental conditions for crustacean growth and survival by predicting the effects of temperature and salinity. Developing AI solutions designed explicitly for crustacean aquaculture is essential for enhancing this important sector's sustainability and economic viability. 4. Limited AI-Driven Predictive Analytics for Sustainability: Many current AI applications in aquaculture focus on classification and detection tasks, such as identifying diseased fish. However, there is a need to move beyond these applications and develop more advanced AI-driven predictive analytics to enhance the sustainability of aquaculture practices. AI has the potential to create forecasting models that predict future events, enabling proactive management strategies. Examples of such applications include the early prediction of disease outbreaks, which would allow farmers to implement preventive measures and minimise losses, and the automated adjustment of breeding conditions based on predicted environmental changes, optimising reproductive success and reducing resource waste. Furthermore, AI can forecast yields, assisting farmers in production planning and marketing. Developing and implementing AI-driven predictive analytics is crucial for promoting environmentally and economically sustainable aquaculture. * Challenges in AI Model Generalization and Transferability: AI models' limited generalisation and transferability is a significant challenge in applying AI to aquaculture. Many models are developed for specific environments or datasets, which restricts their applicability to diverse aquaculture settings. Aquaculture environments vary significantly regarding species, geographic location, environmental conditions, and management practices. Therefore, there is a pressing need for research on adaptive AI models that can generalise across these different contexts. This includes developing robust AI models that are less sensitive to variations in input data and employing techniques such as transfer learning, which enables models trained on one dataset to be adapted for use with another.