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Development of an Iot-Integrated AI System for Microplastic Detection in Water Samples
Summary
Researchers developed an IoT-integrated AI system using high-resolution microscopy, a Raspberry Pi platform, and machine learning to detect and classify microplastic particles in water samples in real time via MQTT, achieving detection accuracy exceeding 95% in simulated dataset validation.
Microplastics are emerging pollutants that threaten aquatic ecosystems and global water safety. This research introduces an integrated IoT and AI-based system designed for real-time detection of microplastic particles in water samples. The system employs high-resolution microscopy integrated with a Raspberry Pi platform, utilizing machine learning models trained to identify and classify microplastic types based on size and shape. Data captured by the camera is processed and transmitted via MQTT to a centralized dashboard, providing live visualization of contamination levels, particle types, and water quality parameters. Validation using simulated datasets demonstrates detection accuracy exceeding 95%, with potential to scale for environmental monitoring across multiple sites. This work highlights a cost-effective, scalable approach for continuous water quality assessment, contributing to environmental protection and pollution management efforts.