An AI-Driven IoT Framework for Aquaculture Management

Abstract

Aquaculture is a rapidly growing food production sector globally thatprovides fish protein to millions worldwide. However, the sector faces manychallenges, including water quality management. Most small-scale aquaculture farmers, unlike commercial farms, rely on experience in managing water qualitychallenges. The limitation of real-time monitoring tools has contributed tosignificant economic losses at the aquaculture fish production level. This studyexplores existing water quality monitoring technologies in small-scale aquaculture todevelop an IoT-based framework for improved efficiency. It also provide srecommendations to enhance adoption and sustainability in aquaculturemanagement. The framework adopts IoT and machine learning with a further integration of LLMs to automate aquaculture management. Water quality parameter-specific sensors are key components of the framework and their interlinkage to systems operator devices such as mobile phones and computers. The framework can be scaled and commercialized upon successful testing and refinement, benefiting a wider range of stakeholders, including large-scale fish farmers and caged aquaculture operations. This expansion would further enhance sustainability andresilience in the aquaculture industry, ensuring broader adoption of data-driven fish farming solutions.Further, other supply chain stakeholders, such as suppliers, canmonitor the feed supply in real time. The framework aims to enhance water qualityin aquaculture production systems, thus sustainably upscaling farmed fishproduction, productivity, and quality. Implementing this plan can provide several advantages. Small-scale fish farmers can enhance their decision-making processes by automating the monitoring and management of water quality. By utilizingadvanced technology, they can observe key parameters such as aquatic turbidity,dissolved oxygen, ammonia, alkalinity, and acidity, enabling them to assess thesuitability of water for fish farming