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Papers
61,005 resultsShowing papers similar to Design and Modeling of a Multi-camera-based Disease Detection Model
ClearA Review of Plant Disease Detection Systems for Farming Applications
This review surveys automated plant disease detection systems using technologies like image processing and machine learning for agricultural applications. While not directly about microplastics, improving crop health monitoring is relevant because microplastic contamination in agricultural soils can stress plants and reduce yields. Better disease detection tools could help farmers identify when environmental factors like soil pollution are contributing to crop problems.
Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean
Scientists used automated machine learning to detect microplastic contamination in soybean plants by analyzing changes in the plants' metabolism and antioxidant systems. The technology could identify microplastic-contaminated crops with high accuracy, even when pesticides were also present. This rapid detection method could help monitor food crop safety and identify fields where microplastic pollution threatens the food supply.
DFMA: an improved DeepLabv3+ based on FasterNet, multi-receptive field, and attention mechanism for high-throughput phenotyping of seedlings
Researchers developed an improved deep learning model called DFMA for automated measurement of plant seedling length, a key metric for assessing seed viability. The model achieved high accuracy on rice seedling and other plant datasets, outperforming existing approaches in generating detailed segmentation masks of seedling structures. While not directly about microplastics, the technology addresses agricultural phenotyping challenges relevant to understanding crop responses to environmental stressors.
Automated Plastic Waste Detection Using Advanced Deep Learning Frameworks
Researchers developed a deep learning system using advanced neural network frameworks for automated detection and classification of plastic waste from images, achieving high accuracy in identifying multiple plastic types to support environmental monitoring and waste sorting.
Microplastic detection in arable soil using a 3D Laser Scanning Confocal Microscope coupled with a Machine-Learning Algorithm
Researchers applied 3D laser scanning confocal microscopy coupled with a machine-learning algorithm for automated detection and quantification of microplastics from LDPE and PP mulch films in arable soil, addressing the lack of accurate quantification methods for agricultural MP contamination from plastic mulching and sewage sludge.
Microplastic detection in arable soil using a 3D Laser Scanning Confocal Microscope coupled with a Machine-Learning Algorithm
Researchers used 3D laser scanning confocal microscopy paired with machine learning to detect microplastics in agricultural soil. The method successfully identified low-density polyethylene particles from mulching films, providing a faster and more precise tool for tracking plastic contamination in farmland.
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.
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
Researchers applied dual deep learning models (YOLOv8, YOLOv11, and several CNN architectures) to detect and classify microplastics in water, finding that these AI approaches could accurately identify plastic types across both aquatic and non-aquatic datasets.
Application of hyperspectral and deep learning in farmland soil microplastic detection
Hyperspectral imaging combined with deep learning was applied to detect and classify microplastics in farmland soil, offering a non-destructive, rapid alternative to time-consuming chemical extraction methods. The model achieved high classification accuracy across polymer types, demonstrating the potential for field-deployable microplastic monitoring in agricultural settings.
Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology
Researchers developed a method using hyperspectral imaging and machine learning to rapidly detect and classify different types of microplastics in farmland soil. The technology achieved high accuracy in identifying common plastic types like polyethylene and polypropylene in soil samples. Better detection tools like this are essential for monitoring microplastic contamination in agricultural land and understanding its potential impact on food safety.
Spatial prediction of physical and chemical properties of soil using optical satellite imagery: a state-of-the-art hybridization of deep learning algorithm
Not relevant to microplastics — this study uses deep learning models combining satellite imagery and topographic data to predict soil chemical properties (pH, organic carbon, phosphorus, potassium) across a region of Iran, with no connection to microplastic pollution.
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.
Underwater Target Detection Utilizing Polarization Image Fusion Algorithm Based on Unsupervised Learning and Attention Mechanism
Not relevant to microplastics — this paper develops a deep-learning image fusion method to improve underwater target detection using polarization cameras, with no connection to plastic pollution.
Lightweight detection of microplastic foreign bodies in sun-dried green tea: An improved YOLOv8 neural network model based on deep learning
Researchers developed an improved deep learning model based on YOLOv8 to detect microplastic contaminants in sun-dried green tea during processing. The lightweight model was specifically designed to overcome limitations of the original architecture for identifying small, irregularly shaped plastic fragments among tea leaves. The study demonstrates that AI-powered visual inspection systems could help safeguard tea quality by rapidly identifying microplastic contamination during food production.