# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 7 Future Directions in AIoT for Aquaculture  
While current AIoT applications significantly impact aquaculture through smart feeding, water quality management, behaviour monitoring, disease detection, biomass estimation, and automation, substantial work remains to realise their full potential. Future endeavours should prioritise enhancing real-time adaptability, building resilience against environmental variations, ensuring extensibility, and achieving cost-effectiveness across diverse aquaculture settings. An essential focus should be improving real-time decision making, enabling aquaculture facilities to respond swiftly to dynamic environmental changes. This would facilitate continuous operations, even in remote environments, allowing for the optimisation of critical parameters like feeding intensity and water quality to support fish health and environmental sustainability. Furthermore, adapting AloT systems to various species and environmental conditions represents an ongoing challenge. Many models that perform well under specific conditions struggle with generalisation when faced with variations in lighting, water turbidity, or species behaviour. Future studies should focus on creating adaptive algorithms and multimodal systems to address these challenges. This can be accomplished by utilising diverse training datasets encompassing various species and environments or leveraging transfer learning methods to enhance models' ability to generalise across different aquaculture settings. Scalability and cost-effectiveness are crucial for more widespread use of AloT, particularly among small aquaculture operators. Innovations in sensor integration with mobile-compatible systems and using cloud-based platforms for data processing can significantly reduce hardware requirements and make AloT solutions more accessible. Integrating diverse data sources, such as visual, acoustic, and environmental data, into unified monitoring platforms is also essential. As demonstrated in biomass estimation and behaviour analysis, this multimodal approach provides a comprehensive view of fish health and population dynamics, fostering resilience by mitigating reliance on single data sources. Enhancing early disease detection and prevention by real-time biosensing is another critical area for development. Future research should focus on integrating real-time biosensors that track pathogen levels, enabling early detection and intervention. AloT solutions that are mobile and compatible with offline use can enhance disease detection in remote and resource-constrained aquaculture facilities. Recent advancements in biosensing technologies, such as microfluidic devices and lab-on-a-chip platforms, show promise for rapid and sensitive detection of disease biomarkers in aquaculture environments. Combined with AI-driven analysis, these technologies can enable early-warning systems and automated responses to mitigate disease outbreaks, representing a significant step forward in proactive aquaculture management. Additionally, the integration of advanced automation and robotics has the potential to transform aquaculture operations significantly. Future robotic systems should prioritise adaptability, efficiency, and reliability in diverse environments facilitated by enhanced sensor robustness, algorithm efficiency, and improved energy solutions. Multi-sensor fusion combined with AI can lead to highly autonomous robotic systems capable of performing routine tasks, reducing labour, and improving operational efficiency. Energy independence is paramount for aqua farms located in remote or offshore areas. Integrating renewable energy sources like solar and wind power with AloT-enabled energy optimisation can reduce reliance on conventional energy sources and minimise operational costs. Finally, digital twin technology, involving the development of virtual aquaculture system models, offers promising potential for simulation in real time, optimisation, and predictive analytics.