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Papers
20 resultsShowing papers similar to AqUavplant Dataset: A High-Resolution Aquatic Plant Classification and Segmentation Image Dataset Using UAV
ClearData 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.
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.
Machine learning based approaches for prompt diagnosis of aquatic plant ailments
Researchers applied Deep Belief Networks and Isolation Forests to diagnose aquatic plant diseases from observational data, achieving an average DBN accuracy of 86% and an Isolation Forest true positive rate of 91%. The study demonstrated that machine learning can improve early detection of aquatic plant ailments, which is relevant to monitoring aquatic ecosystem health in microplastic-contaminated environments.
SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements
Researchers developed SNOWED, an automatically constructed dataset of satellite imagery with labeled water edges, enabling deep learning models to accurately detect and monitor shoreline changes for environmental monitoring applications.
Mini Uav-based Litter Detection on River Banks
Researchers developed a drone-based litter detection system combining high-resolution mapping, deep learning object detection, and vision-based localization that locates riverbank litter with decimeter-level accuracy, enabling automated monitoring of plastic pollution in urban waterway areas.
Identification for the species of aquatic higher plants in the Taihu Lake basin based on hyperspectral remote sensing
Researchers developed a hyperspectral remote sensing method using a C4.5 decision tree algorithm to identify and map eight aquatic higher plant species in the Taihu Lake basin, addressing the challenge of distinguishing species with small spectral differences against dynamic water optical backgrounds. The approach enables large-scale, fine-resolution monitoring of aquatic plant distribution as an indicator of ecosystem health.
Digital 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.
Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images
Researchers applied machine learning to aerial multispectral images for automated detection of plastic litter in natural areas, demonstrating that combining spectral data with classification algorithms can effectively identify and monitor plastic waste pollution.
Robust and Fair Undersea Target Detection with Automated Underwater Vehicles for Biodiversity Data Collection
Researchers developed a robust and fair underwater target detection system for automated underwater vehicles (AUVs) to support marine biodiversity data collection, addressing unique challenges of subsea imaging compared to above-ground remote sensing.
High-Resolution Seagrass Species Mapping and Propeller Scars Detection in Tanjung Benoa, Bali through UAV Imagery
This paper is not directly about microplastics; it maps seagrass species distribution and propeller scar damage in Bali's coastal waters using drone imagery, demonstrating that UAV remote sensing can achieve high-accuracy seagrass habitat monitoring.
Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
Researchers developed a convolutional neural network-based algorithm to automatically detect and quantify floating marine macro-litter in aerial images, training it on 3,723 images and integrating it into a web application for practical monitoring use.
Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q)
Researchers developed APLASTIC-Q, a convolutional neural network system trained on very high-resolution aerial imagery from Cambodia, capable of detecting, classifying, and quantifying floating and washed-ashore plastic litter — providing a scalable tool for remote monitoring of aquatic plastic pollution.
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.
Amphitrite v1.0: An underwater database for marine debris and biodiversity
Researchers created Amphitrite v1.0, an underwater database of 16 labeled categories of marine debris and biodiversity images suitable for training computer vision models for automated marine litter detection, validated against the latest YOLO object recognition architectures.
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.
UAV imaging and deep learning based method for predicting residual film in cotton field plough layer
Researchers developed a method combining UAV imaging with three deep learning frameworks (LinkNet, FCN, and DeepLabv3) to segment and predict residual plastic film content in the plough layer of cotton fields, offering a lower-cost and higher-efficiency alternative to traditional manual sampling for agricultural plastic pollution monitoring.
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.
An innovative approach for microplastic sampling in all surface water bodies using an aquatic drone
Researchers adapted an aquatic drone to sample microplastics in surface water, finding it produced results comparable to the standard Manta net while offering better reproducibility and improved capture of smaller, lighter particles in both river and coastal environments.
Determine stormwater pond geometrics and hydraulics using remote sensing technologies: A comparison between airborne-LiDAR and UAV-photogrammetry field validation against RTK-GNSS
Researchers compared UAV-photogrammetry and airborne-LiDAR against RTK-GNSS ground truth for measuring stormwater pond geometry and hydraulics across six ponds. UAV-photogrammetry outperformed infrared airborne-LiDAR for wet ponds, while correction methods for vegetation penetration improved dry pond performance, establishing UAV photogrammetry as the preferred cost-effective approach for pond monitoring.
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.