An AI-Driven IoT Framework for Aquaculture Management

dc.contributor.authorWambua, Anthony
dc.contributor.authorOmambia, Andrew
dc.contributor.authorAwour, Fedrick
dc.contributor.authorMaake, Benard
dc.contributor.authorOrina, Paul
dc.date.accessioned2025-06-28T07:08:48Z
dc.date.issued2025
dc.descriptionConference Paper
dc.description.abstractAquaculture is a rapidly growing food production sector globally that provides fish protein to millions worldwide. However, the sector faces many challenges, including water quality management. Most small-scale aquaculture farmers, unlike commercial farms, rely on experience in managing water quality challenges. The limitation of real-time monitoring tools has contributed to significant economic losses at the aquaculture fish production level. This study explores existing water quality monitoring technologies in small-scale aquaculture to develop an IoT-based framework for improved efficiency. It also provides recommendations to enhance adoption and sustainability in aquaculture management. 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 and resilience in the aquaculture industry, ensuring broader adoption of data-driven fish farming solutions. Further, other supply chain stakeholders, such as suppliers, can monitor the feed supply in real time. The framework aims to enhance water quality in aquaculture production systems, thus sustainably upscaling farmed fish production, 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 utilizing advanced technology, they can observe key parameters such as aquatic turbidity, dissolved oxygen, ammonia, alkalinity, and acidity, enabling them to assess the suitability of water for fish farming
dc.identifier.citationWambua, A., Omambia, A., Awour, F., Maake, B., & Orina, P. (2025). An AI-Driven IoT Framework for Aquaculture Management. Miriam Cunningham and Paul Cunningham (Eds) IST-Africa Institute and IIMC.
dc.identifier.isbn978-1-905824-74-8
dc.identifier.urihttps://repository.daystar.ac.ke/handle/123456789/6949
dc.language.isoen
dc.publisherMiriam Cunningham and Paul Cunningham (Eds) IST-Africa Institute and IIMC
dc.subjectIoT
dc.subjectAI
dc.subjectAquaculture
dc.subjectWater quality
dc.subjectFish farming
dc.titleAn AI-Driven IoT Framework for Aquaculture Management
dc.typePresentation

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