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Papers
20 resultsShowing papers similar to Data Study Group Final Report: Centre for Environment, Fisheries and Aquaculture Science
ClearDigital Image Identification of Plankton Using Regionprops and Bagging Decision Tree Algorithm
Researchers developed a digital image classification system using machine learning to identify and count plankton from microscopy images. The method reduced the time and subjectivity of manual identification while maintaining accuracy. Automated plankton identification could also be adapted to distinguish microplastics from biological particles in environmental water samples.
Plankton classification with high-throughput submersible holographic microscopy and transfer learning
Researchers used underwater holographic microscopes and transfer learning — an AI technique that applies knowledge from one task to another — to automatically classify diverse plankton species from images, including rare forms. The system shows promise for large-scale, automated ocean monitoring without needing constant human 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.
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 Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.
Detection of Microplastics Using Machine Learning
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.
Automatic Identification and Classification of Marine Microplastic Pollution Based on Deep Learning and Spectral Imaging Technology
Researchers developed an AI system combining deep learning with multispectral imaging to automatically identify and classify marine microplastics, using a feature-selection method called ReliefF to reduce noise in complex ocean samples. The approach achieved high accuracy and offers a scalable solution for large-scale ocean microplastic monitoring that outperforms traditional manual inspection.
Rapid Classification of Microplastics by Using the Application of a Convolutional Neural Network
Researchers used convolutional neural networks (deep learning) to automatically classify microplastic particles in microscopy images into four categories: fragments, pellets, films, and fibers. The models achieved high classification accuracy, reducing the time and labor needed for manual identification. Automated AI classification could greatly accelerate large-scale microplastic monitoring programs.
Machine learning for microalgae detection and utilization
This review assessed machine learning applications for microalgae detection, classification, and utilization in aquaculture and bioproduction, finding that deep learning approaches achieve the highest accuracy for species identification from microscopy images. The authors highlighted ML as an enabling technology for automating microalgae monitoring and optimizing production in industrial bioreactors.
AqUavplant Dataset: A High-Resolution Aquatic Plant Classification and Segmentation Image Dataset Using UAV
Researchers created a high-resolution image dataset of 31 aquatic plant species in Bangladesh using drone photography, designed to help train machine learning models for automated plant mapping. The dataset includes detailed segmentation masks that can help identify individual species, track plant growth, and monitor the spread of invasive species. This tool could support conservation efforts by making it easier to monitor aquatic plant biodiversity across large areas.
Application of a convolutional neural network for automated multiclass identification of field-collected microplastics and diatom algae from optical microscopy images
Researchers developed and evaluated a convolutional neural network model using transfer learning to automatically classify field-collected microplastics and diatom algae from optical microscopy images, using a dataset of real microplastics sampled from a freshwater reservoir. The model achieved automated multi-class identification, including detection of diatom frustules that survive hydrogen peroxide processing, addressing challenges posed by the lack of standardised microplastic analysis protocols.
Underwater Image Detection for Cleaning Purposes; Techniques Used for Detection Based on Machine Learning
Researchers reviewed machine learning techniques for underwater image detection to support water pollution cleanup, focusing on convolutional neural networks and region-based CNN methods for identifying surface mucilage and debris. The study evaluated supervised classification algorithms as the most effective approach for automated aquatic waste detection systems.
Estimating 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.
Deep Learning Based Approach to Classify Saline Particles in Sea Water
Researchers developed a deep learning classification approach to identify saline particles in seawater images, demonstrating high accuracy in distinguishing salt crystals from other particles, with potential application to automated water quality monitoring systems.
SMACC: A System for Microplastics Automatic Counting and Classification
Researchers developed an automated computer vision system (SMACC) that uses image analysis to count and classify plastic particles in beach samples, demonstrating that machine learning can substantially reduce the time and effort required for large-scale beach microplastic monitoring.
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.
Role of AI Technique for Controlling Micro Plastic on Marine Eco System
This paper developed a machine learning system using Support Vector Machine (SVM) algorithms to classify microplastic density in ocean water based on oceanographic sensor data, achieving 93% accuracy. The system is proposed as a scalable, automated alternative to labor-intensive manual microplastic sampling in marine environments. AI-driven monitoring tools like this could make it practical to track plastic pollution across vast ocean areas where manual surveys are infeasible.
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.
Machine learning in marine ecology: an overview of techniques and applications
This overview examines how machine learning techniques are being applied across marine ecology, covering data types from satellite imagery and acoustics to underwater images and genomic data. Researchers built a database of roughly 1,000 publications to map which techniques work best for different marine research questions. The study highlights that growing data volumes and computing power are making machine learning an increasingly essential tool for understanding ocean ecosystems.
Raman Spectroscopy Enhanced By Machine Learning For Effective Microplastic Detection In Aquatic Systems
Researchers explored combining Raman spectroscopy with machine learning techniques to improve microplastic detection and classification in aquatic systems. The study found that deep learning models, particularly convolutional neural networks, achieved high classification accuracy and significantly reduced reliance on labor-intensive manual spectral analysis for real-time environmental monitoring.