We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
AI-Enabled Plastic Pollution Monitoring System for Toronto Waterways
Summary
Researchers developed an AI-based monitoring system to detect plastic pollution in Toronto waterways using camera sensors. Automated AI monitoring enables continuous, large-scale tracking of plastic pollution, which is the precursor to the microplastics that accumulate in aquatic ecosystems.
Plastic pollution in waterways is a severe environmental concern predicted to increase globally in the coming years. Many activists and research groups are currently invested in monitoring pollution and removing plastics from aquatic ecosystems. However, with much focus on microplastics and the direct removal of larger plastics, accurate data on the sources and pathways of larger floating plastics in freshwater environments is scarce. We present an IoT system with an integrated computer vision model to identify and store data on trash in natural settings. This would include a camera sensor system, edge computing, cloud storage, and the eventual implementation of connected trash-collecting robots. Such a system deployed at multiple sites would inform trash monitoring and removal efforts while providing invaluable open-access environmental data for researchers and future technologies.
Sign in to start a discussion.
More Papers Like This
Real-Time Detection of Microplastics Using an AI Camera
Researchers developed a camera-based system using artificial intelligence to detect and measure microplastics in real time as they move through water. The system was tested with three different camera setups and could identify particles, measure their size, and track their speed. This technology could provide a faster and more practical alternative to the labor-intensive laboratory methods currently used to monitor microplastic pollution.
Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
This review explores how artificial intelligence technologies, including machine learning, computer vision, and remote sensing, can improve the detection, tracking, and removal of microplastic pollution in waterways. Researchers found that AI-based approaches offer significant advantages over traditional monitoring methods for identifying microplastic distribution patterns. The study highlights the potential of AI-driven robotic systems to support more efficient and scalable environmental cleanup efforts.
An Artificial Intelligence based Optical Sensor for Microplastic Detection in Seawater
Researchers developed an AI-based optical sensor system combining an optical detection subsystem and an image acquisition subsystem to detect and identify microplastic particles in seawater, distinguishing them from naturally occurring marine particles. The device applies AI algorithms to analyze consecutive image frames and classify particles as microplastic or non-microplastic, with the full system housed in two portable cases.
IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks
Researchers developed an IoT-based system using artificial intelligence to automatically detect and count microplastics in water samples through image recognition. The system uses cameras at distributed sensor points to continuously monitor waterways and can identify microplastics of different sizes, shapes, and colors. This technology could improve environmental monitoring of microplastic pollution in real time, helping communities and agencies respond faster to contamination threats in drinking water sources.
Real-time detection of microplastics in aquatic environments using emerging technologies
Researchers proposed a real-time microplastic detection system combining AI-enhanced optical sensors and IoT devices, capable of automatically classifying microplastics in ocean water without the time-consuming manual steps required by spectroscopy or microscopy.