# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 6 Challenges to AIoT Implementation in Aquaculture  
Artificial Intelligence of Things (AIoT) integration is undeniably transforming aquaculture practices, yet its widespread adoption faces significant hurdles. A primary obstacle is the substantial initial investment required. Deploying comprehensive AIoT systems encompassing sensor networks, robust data infrastructures, and powerful computational resources often presents prohibitive upfront costs, particularly for small to medium-sized aquaculture farms. This initial outlay includes purchasing and installing diverse sensors for monitoring parameters like water quality, temperature, and salinity, as well as data processing and storage hardware. Beyond the initial setup, ongoing sensor maintenance, software updates, and data storage expenses compound these financial challenges, demanding significant capital and specialised technical knowledge. Furthermore, the inherent variability of aquatic environments poses a considerable challenge to the effectiveness of the AI model. Aquaculture operations are constantly influenced by fluctuating water quality, temperature, and salinity. These dynamic conditions can lead to AI models that struggle to generalise across aquatic settings or species. Consequently, frequent retraining and adaptation become necessary, consuming substantial resources and time, especially for smaller operations. The interconnected nature of AIoT systems also raises critical concerns regarding data privacy and security. The continuous collection of sensitive operational data, including fish health and environmental conditions, creates hacking and unauthorised access vulnerabilities. Ensuring the integrity and confidentiality of these data is paramount to prevent economic losses, protect proprietary farming techniques, and maintain consumer trust. Therefore, stringent cybersecurity measures, such as encryption, secure network architectures, and robust access controls, are essential to mitigate these risks. Scalability presents another significant barrier to AIoT adoption in aquaculture. The diverse nature of aquaculture operations, ranging from small family farms to extensive industrial facilities, complicates the widespread deployment of AIoT systems. Small-scale farms especially might struggle with the high expenses of installing widespread sensor networks and handling the data they generate. Additionally, setting up and maintaining AIoT systems demands specialised knowledge in data science, machine learning, and IoT device management. Many aquaculture operators, particularly those in small to medium-sized businesses, may not have the expertise to implement and maintain these technologies effectively. Beyond the technology itself, a critical barrier is the need for a skilled workforce. Setting up, maintaining, and effectively utilizing AIoT systems demands specialised knowledge in areas such as data science, machine learning, IoT device management, and cybersecurity. Many aquaculture operators, particularly those in small to medium-sized businesses or remote locations, may lack the expertise to implement and maintain these advanced technologies effectively. This highlights a significant need for targeted technical training programs and comprehensive workforce development initiatives within the aquaculture sector to bridge this skills gap and ensure successful AIoT adoption and utilization. The complexity of interpreting AI-driven insights and translating them into practical farming decisions also requires adequate training. A further limitation lies in the generalisation and transferability of AI models across different species and applications. Many AI models are tailored explicitly to particular species, farming methods, or environmental conditions, restricting their broader applicability.