We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Artificial intelligence in analysis of microplastic pollutants
AI summary Read the abstract
This Croatian thesis reviews artificial intelligence methods for analyzing and classifying microplastic pollutants. The author examines how machine learning and image analysis tools can improve the speed and accuracy of microplastic detection and characterization in environmental samples.
Sa sve većom upotrebom plastike dolazi i do nagomilavanja plastičnog otpada koji nerijetko završava u okolišu, a posebice u vodama. U zadnjih nekoliko godina istraživanja su usmjerena prema pronalasku načina detekcije i karakterizacije sićušnih čestica plastike pod nazivom mikroplastika koje predstavljaju globalni ekološki problem. Negativni utjecaj mikroplastike ogleda se u lakom ulasku u hranidbeni lanac te potencijalnoj toksičnosti. Dosadašnje metode detekcije su konvencionalne analitičke metode koje nažalost nemaju veliku učinkovitost. Veliki nedostatak takvih metoda je i dugo vrijeme provedbe analize kao i mogućnost ljudske pogreške. Kako bi se navedeni problemi izbjegli, sve češće se u karakterizaciji upotrebljava umjetna inteligencija. Cilj ovog rada je dati pregled metode umjetne inteligencije koje se koriste u detekciji i karakterizaciji mikroplastičnih čestica.
More Papers Like This
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
AI summary Read the abstract
Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors
AI summary Read the abstract
This review summarizes how artificial intelligence and machine learning are being used to identify, track, and predict the environmental behavior of microplastics in soil and water. AI methods can analyze the chemical composition, shape, and distribution of microplastics faster and more accurately than traditional techniques. The technology could help scientists better understand where microplastics accumulate and what risks they pose to ecosystems and human health.
Detection of Microplastics Using Machine Learning
AI summary Read the abstract
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.
Artificial intelligence in microplastics domain: Current progress, challenges, and sustainable prospects
AI summary Read the abstract
This critical review assesses how artificial intelligence tools—including machine learning and image recognition—are being applied to detect, characterize, and predict the behavior of microplastics in the environment. AI approaches show promise for overcoming persistent bottlenecks in large-scale microplastic analysis, but the authors highlight challenges around data quality, model interpretability, and standardization that must be addressed for these tools to reach their potential.
Artificial Intelligence and Machine Learning Approaches for Automatic Microplastics Identification and Characterization
AI summary Read the abstract
This review examines how artificial intelligence and machine learning algorithms are being applied to identify, characterize, and model microplastic pollution in the environment. The authors found that these tools can analyze large sensor datasets to detect microplastics in water bodies, predict transport patterns, and model adsorption behavior under various environmental conditions. The study highlights the growing role of computational approaches in understanding and mitigating microplastic contamination.
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.