An Intelligent, Low-Cost IoT System for Real-Time Aquaculture Monitoring

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dc.contributor.authorKEDJAR Sara
dc.date.accessioned2025-10-08T15:04:54Z
dc.date.available2025-10-08T15:04:54Z
dc.date.issued2024
dc.description.abstractAquaculture plays a pivotal role in global food security, but its sustainability is challenged by the need for real-time monitoring and efficient resource management. This master thesis presents an intelligent IoT and AI-powered system designed for real-time, low-cost aquaculture monitoring. The proposed system integrates advanced sensors with AI-driven data processing to monitor key parameters such as Ph, Turbidity, Electrical conductivity, temperature, and dissolved oxygen. A robust anomaly detection module, leveraging deep learning algorithms, ensures early identification of irregularities, mitigating risks to aquatic life. In addition,the system incorporates predictive analytics to forecast environmental changes, enabling proactive ddecision making.
dc.identifier.urihttps://dspace.univ-oran1.dz/handle/123456789/4713
dc.language.isoen
dc.subjectIoT, Aquaculture Monitoring, Sensing, Anomaly detection, Machine learning.
dc.titleAn Intelligent, Low-Cost IoT System for Real-Time Aquaculture Monitoring
dc.typeThesis
la.SpécialitéInstrumentation and Metrology
la.coteMI16
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