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Detection of Microplastics Using Machine Learning
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Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
Monitoring the presence of micro-plastics in human and animal habitats is fast becoming an important research theme due to a need to preserve healthy ecosystems. Microplastics pollute the environment and can represent a serious threat for biological organisms including the human body, as they can be inadvertently consumed through the food chain. To perceive and understand the level of microplastics pollution threats in the environment there is a need to design and develop reliable methodologies and tools that can detect and classify the different types of the microplastics. This paper presents results of our work related to exploration of methods and techniques useful for detecting suspicious objects in their respective ecosystem captured in hyperspectral images and then classifying these objects with the use of Neural Networks technique.
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This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.