Instrumentation et métrologie

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An IOT based in wastewater for irrigation using Deep Learning
(2024) DIAF Amel
Utilizing wastewater for irrigation offers significant benefits due to its high nutrient content, reducing the reliance on chemical fertilizers. Additionally, wastewater irrigation aids in the conservation of water resources. Treated wastewater has emerged as a dependable source for farmers, enabling year-round crop cultivation, particularly in arid regions. This study proposes a novel approach that integrates artificial intelligence and edge computing capabilities to offer instantaneous monitoring and prediction of water quality, adhering to parameter thresholds set by the World Health Organization (WHO). This master thesis encompasses both the front-end and back-end aspects, comprising software and hardware architectures. The hardware components include actuators, sensors, and controllers connected remotely via wireless networks. Meanwhile, the software incorporates applications linked with artificial intelligence (AI) models for intelligent predictions. The dataset comprises various features of five parameters essential for determining water quality: conductivity, turbidity, oxygen levels, pH, and temperature, all of which are monitored by sensors included in the sensor module.