We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Holographic imaging boosts machine learning for accurate micro-plastics recognition in seawater sample
AI summary Read the abstract
Researchers combined digital holographic microscopy with machine learning to develop an automated system for identifying microplastics in filtered water samples. The system achieved over 99% classification accuracy in distinguishing microplastic particles from diatoms and other natural particles. The approach offers a fast and reliable alternative to manual microscopy for environmental microplastic monitoring.
An effective strategy, that combines Digital Holography with machine learning, for achieving accurate and automatic identification of microplastics in filtered water sample, is proposed, reaching over 99% in classification performance among microplastics and diatoms.
More Papers Like This
Microplastic Identification via Holographic Imaging and Machine Learning
AI summary Read the abstract
Researchers combined holographic imaging with machine learning algorithms to automatically identify and classify microplastics in water samples, achieving accurate particle detection without manual microscopy. This automated approach could significantly speed up microplastic monitoring in environmental samples.
Micro-Objects Classification for Microplastic Pollution Detection using Holographic Images
AI summary Read the abstract
Researchers developed a machine learning system that uses holographic 3D images to automatically classify microplastics in water samples, distinguishing them from other microscopic particles with high precision. Current microplastic monitoring is slow and labor-intensive, so automated detection tools are essential for large-scale environmental surveillance. This approach could significantly speed up the monitoring of microplastic pollution in aquatic environments.
Digital holographic microplastics detection and characterization in heterogeneous samples via deep learning
AI summary Read the abstract
Researchers used digital holographic microscopy combined with deep learning to detect and characterize microplastic particles in heterogeneous samples containing algae, microorganisms, and other natural particles. This automated approach could improve the speed and accuracy of environmental microplastic monitoring.
Microplastic pollution monitoring with holographic classification and deep learning
AI summary Read the abstract
This study used digital holographic microscopy combined with deep learning to classify microplastic particles in water samples, achieving high classification accuracy and demonstrating the potential for automated, high-throughput microplastic monitoring.
Automatic Detection of Microplastics by Deep Learning Enabled Digital Holography
AI summary Read the abstract
Researchers developed a digital holography system combined with deep learning to automatically detect and identify microplastics in water without manual image analysis. The system processes raw holographic images directly, offering a faster and more scalable approach to microplastic monitoring in environmental samples.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.