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Papers
20 resultsShowing papers similar to Identification of Plastic Mulch in Cotton Fields Using UAV-Based Hyperspectral Data and Deep Learning Semantic Segmentation
ClearUAV 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.
Evaluation of residual plastic film pollution in pre-sowing cotton field using UAV imaging and semantic segmentation
Researchers proposed a UAV-based imaging method combined with a modified U-Net semantic segmentation model to evaluate residual plastic film pollution in pre-sowing cotton fields, collecting images from different heights under varying weather conditions to accurately map mulch film remnants.
Large Scale Agricultural Plastic Mulch Detecting and Monitoring with Multi-Source Remote Sensing Data: A Case Study in Xinjiang, China
Satellite imagery was used to monitor plastic mulch film coverage across large agricultural areas in China, mapping both spatial extent and temporal changes. Accurately tracking plastic mulch use is important because agricultural film residues are a major source of microplastic contamination in farmland soils.
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.
Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging
Researchers combined short-wave infrared hyperspectral imaging with machine learning algorithms to detect low concentrations of polyamide and polyethylene microplastics in soil samples, achieving accurate classification with implications for fast, non-destructive screening of agricultural land for plastic contamination.
A Preliminary Study on the Utilization of Hyperspectral Imaging for the On-Soil Recognition of Plastic Waste Resulting from Agricultural Activities
Researchers explored the use of near-infrared hyperspectral imaging to detect and identify plastic waste in agricultural soils. They developed a classification model that could distinguish different types of plastic from soil and assess the degradation state of the material. The study demonstrates that hyperspectral imaging combined with chemometric analysis offers a rapid, non-destructive approach for monitoring plastic contamination in agricultural environments.
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.
Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil
Researchers applied hyperspectral imaging technology combined with machine learning to rapidly screen and classify microplastics in farmland soil samples, demonstrating an efficient non-destructive identification method for soil microplastic contamination.
Duration- and area-dependent influences of plastic film mulch on soil microplastics abundance
Researchers conducted a field campaign combined with remote sensing to investigate how the duration and coverage area of plastic film mulching affect microplastic abundance in agricultural soils in northern China's agro-pastoral ecotone, finding that microplastic concentrations ranged from 41.7 to 787.5 items per kilogram and positively correlated with mulching duration.
A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data
Researchers applied a deep learning semantic segmentation model (ResUNet50 based on U-Net architecture) to UAV orthophotos to automatically map floating plastic debris, achieving F1-scores of 0.86-0.92 for specific plastic types including oriented polystyrene, nylon, and PET. Classification accuracy decreased with lower spatial resolution, with 4 mm resolution providing optimal performance for distinguishing plastic types.
Plastic film residues on cropland: monitoring soil contamination through optical remote sensing
Researchers used optical remote sensing to monitor plastic film residues on agricultural cropland, demonstrating that satellite-based methods can detect surface plastic contamination across large areas. The study provides a scalable approach for tracking agricultural plastic residues — a major secondary microplastic source in soils — without the labor intensity of field sampling.
Hyperspectral detection of soil microplastics via multimodal feature fusion and a dual-path attention residual convolutional network
A hyperspectral imaging approach combined with multimodal deep learning was developed to detect microplastics in soil, achieving high accuracy in identifying plastic particles against complex soil backgrounds. The method offers a faster, less destructive alternative to traditional chemical extraction and spectroscopy for soil monitoring.
Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils.
Researchers combined hyperspectral imaging with machine learning algorithms to detect polyethylene and polyamide microplastics in soil samples. This rapid detection approach could support large-scale soil monitoring for microplastic contamination, which is important given that agricultural soils may accumulate plastics from mulch films, irrigation water, and sewage sludge.
New Workflow of Plastic-Mulched Farmland Mapping using Multi-Temporal Sentinel-2 data
Researchers used multi-temporal satellite imagery to map plastic-mulched farmland in China, providing a tool for monitoring the environmental risk of agricultural plastic use. Plastic mulch is a significant source of microplastic contamination in agricultural soils when film residues break down over time.
Time Series approach to map areas of Agricultural Plastic Waste generation
Researchers applied a time-series remote sensing approach to map the spatial distribution of agricultural plastic waste generation across extensive agricultural landscapes, using satellite imagery to detect plastic-mulched farmlands and other agri-plastics to address the lack of comprehensive plasticulture data needed for effective waste management and land-use policy.
Combining YOLOv7-SPD and DeeplabV3+ for Detection of Residual Film Remaining on Farmland
Researchers developed a hybrid computer vision method combining YOLOv7-SPD object detection and DeepLabV3+ image segmentation to identify and quantify plastic film residues left in farmland soil. The improved model achieved 93.72% average precision and 87.62% recall for detection, with image segmentation reaching 91.55% mean IoU, demonstrating strong potential for automating agricultural residue management.
Microplastic contamination in cotton soils following long-term mulching: A field study for the Xinjiang production and construction corps in China
Researchers investigated microplastic accumulation across agricultural soils in Xinjiang, China — a major mulch film use region — finding that microplastic abundance positively correlates with mulching duration and that geographical and social factors drive north-south differences in contamination levels.
Cropland Microplastics in Xinjiang: Unveiling Distribution and Impact of Mulching Film Residues
This study assessed microplastic distribution and the contribution of agricultural mulching film residues across croplands in Xinjiang, China, finding widespread polyethylene microplastic contamination that correlates with mulch film use intensity and poses risks to soil health and food safety.
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.
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.