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Papers
61,005 resultsShowing papers similar to Assessment of sustainable baits for passive fishing gears through automatic fish behavior recognition
ClearEstimating precision and accuracy of automated video post-processing: A step towards implementation of AI/ML for optics-based fish sampling
Researchers developed automated computer vision models for identifying commercially important Gulf of Mexico fish species from video surveys, assessing precision and accuracy as a step toward replacing manual review with AI-based processing.
Fishing Behavior Detection and Analysis of Squid Fishing Vessel Based on Multiscale Trajectory Characteristics
This paper is not about microplastics; it proposes a trajectory-based algorithm to detect fishing activity from vessel movement data, applied to Chinese squid fishing boats in Argentine waters.
Detection of microplastics in fish using computed tomography and deep learning
CT scanning combined with deep learning neural networks enabled non-destructive, automated detection and localization of microplastics in fish with high accuracy, overcoming the contamination risk and time-consuming nature of conventional dissection-based methods.
A Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
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.
Deep-Feature-Based Approach to Marine Debris Classification
This study applied deep learning to classify marine debris from images, demonstrating that feature-based neural network approaches can effectively distinguish plastic types and other debris categories to support automated ocean monitoring.
Aquatic Trash Detection and Classification: a Machine Learning and Deep Learning Perspective
This review examines machine learning and deep learning approaches for detecting and classifying aquatic trash in waterways, evaluating how computer vision algorithms trained on underwater and surface imagery can automate pollution monitoring for faster, more scalable ocean cleanup.
Detection and identification of environmental faunal proxies in digital images and video footage from northern Norwegian fjords and coastal waters using deep learning object detection algorithms
Researchers developed deep learning object detection algorithms to automate the detection and identification of environmental faunal proxies in digital images and video footage from Norwegian fjords and coastal waters, as part of the ICT+ ocean surveying project at UiT The Arctic University of Norway. The preliminary work aimed to automate identification of objects ranging from foraminifera and microplastics at the micrometre scale to boulders and shipwrecks at the metre scale, replacing labour-intensive manual processing.
Application of Pattern Recognition and Computer Vision Tools to Improve the Morphological Analysis of Microplastic Items in Biological Samples
Researchers developed and validated an open-source image analysis procedure for measuring morphological characteristics of microplastic items identified in fish organ samples, using manually set edge points in digital microscope images and comparison against commercial MotiConnect software. The proposed workflow enabled accurate calculation of shape descriptors such as length, width, and item area, offering a cost-effective alternative for routine laboratory microplastic morphological analysis.
Automatic Counting and Classification of Microplastic Particles
Researchers developed an automatic system for counting and classifying microplastic particles in marine samples, applying image analysis techniques to address the growing problem of plastic debris entering the food chain via marine species ingestion.
Automatic Detection of Microplastics in the Aqueous Environment
Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
Behavioural economics in fisheries: A systematic review protocol
This systematic review protocol outlines a methodology for synthesizing evidence on how behavioral economics mechanisms influence marine fisher decision-making, developed in collaboration with the ICES Working Group on Maritime Systems. The protocol aims to identify which nudges and incentive structures most effectively promote sustainable fishing practices. Understanding fisher behavior is relevant to marine microplastic pollution, as fishing gear and nets are among the largest sources of ocean plastic debris.
Battle Models: Inception ResNet vs. Extreme Inception for Marine Fish Object Detection
Not relevant to microplastics — this paper compares two deep learning models (Inception ResNet and Xception) for detecting and classifying marine fish species in underwater images, with no connection to plastic pollution.
Hydrodynamic modulation of microplastic bioaccumulation in edible fish: Integrating biomarker networks, machine learning, and food safety perspectives
Scientists found that fish in faster-moving water absorb much more microplastic pollution than fish in still water, and these fish also showed more tissue damage and health problems. This matters because many of the fish we eat live in rivers and streams with flowing water, which means they could contain higher levels of harmful microplastics than previous studies suggested. The research shows we may be underestimating how much plastic pollution is getting into our seafood.
Microplastics assessment in Arabian Sea fishes: accumulation, characterization, and method development
Researchers assessed microplastic accumulation in Arabian Sea fish species, developing optimized digestion protocols and characterizing polymer types to trace contamination sources, finding widespread microplastic ingestion across multiple commercially important fish species.
AI-Driven Framework Development for Predictive Classification of Microplastic Concentration of Aquatic Systems in the United States
Researchers compared four machine learning models—logistic regression, random forest, support vector machine, and a neural network—for predicting microplastic density in US coastal waters across three regions. The support vector machine performed best with 93.94% average accuracy, demonstrating the potential of AI-driven tools for microplastic monitoring.
Data Study Group Final Report: Centre for Environment, Fisheries and Aquaculture Science
Machine learning was applied to the challenge of automatically classifying plankton species from underwater images collected by fisheries monitoring systems. The AI classifier could identify dozens of plankton categories with high accuracy, reducing the need for time-consuming manual identification. Automated plankton monitoring improves understanding of marine food web health and ecosystem responses to environmental change.
High-resolution, non-invasive animal tracking and reconstruction of local environment in aquatic ecosystems
Researchers developed an open-source, affordable framework using computer vision and tracking algorithms to monitor animal behavior in aquatic environments at high resolution. The tool enables detailed behavioral studies across a wide range of species without requiring expensive commercial equipment.
Relevance of Data Analytics in Sustainable Fisheries Management: an Evidence-based Study
This paper is not about microplastics; it argues for using data analytics tools to improve fisheries management and combat marine water quality decline in coastal communities.