An Intelligent, Low-Cost IoT System for Real-Time Aquaculture Monitoring
An Intelligent, Low-Cost IoT System for Real-Time Aquaculture Monitoring
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Date
2024
Auteurs
KEDJAR Sara
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Résumé
Aquaculture 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.
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Mots-clés
IoT, Aquaculture Monitoring, Sensing, Anomaly detection, Machine learning.