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Papers
20 resultsShowing papers similar to Evaluation of residual plastic film pollution in pre-sowing cotton field using UAV imaging and 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.
Identification of Plastic Mulch in Cotton Fields Using UAV-Based Hyperspectral Data and Deep Learning Semantic Segmentation
Plastic mulch film is widely used in agriculture to improve crop yields, but residual plastic in fields contributes to soil microplastic contamination, and identifying where it remains after harvest is difficult at scale. This study used drone-mounted hyperspectral cameras combined with deep-learning image analysis to map plastic mulch coverage in cotton fields in China, achieving up to 80% accuracy in distinguishing plastic from soil and crop canopy. Accurate mapping of residual mulch is a critical first step toward targeted plastic removal and reducing the flow of agricultural microplastics into soil and water.
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.
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.
Object Detection of Macroplastic Waste Using Unmanned Aerial Vehicles in Urban Canal
Researchers developed and tested an unmanned aerial vehicle-based system for detecting macroplastic waste along riverbanks and beaches using object detection algorithms. The system achieved reliable detection performance and offers a scalable tool for large-area plastic litter surveys.
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.
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.
Detection of Microplastics in Coastal Environments Based on Semantic Segmentation
Researchers developed a deep learning semantic segmentation approach for detecting microplastics on sandy beaches at the pixel level, evaluating 12 models including U-Net variants and transformer architectures under real-world conditions.
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.
UAV Approach for Detecting Plastic Marine Debris on the Beach: A Case Study in the Po River Delta (Italy)
UAV imaging was used to detect and map anthropogenic marine debris on beaches in the Po River Delta, Italy, testing different image processing strategies and demonstrating that centimeter-scale spatial resolution UAV surveys can efficiently locate macroplastics before they degrade into harder-to-remove microplastics.
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.
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.
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.
Large scale detection of plastic covered crops using multispectral and SAR satellite data
Researchers used satellite imagery combining optical and radar data to detect large-scale plastic covering of agricultural crops across wide geographic areas. The remote sensing approach could help monitor plasticulture practices and track the potential for plastic debris to enter nearby ecosystems.
A New Method for Detecting Plastic-Mulched Land Using GF-2 Imagery
Researchers developed a new remote sensing method for detecting plastic-mulched agricultural land using GF-2 satellite imagery by introducing a K-T transform component specifically enhanced for plastic identification. The method was combined with texture metrics and spectral bands in an object-oriented classification approach. The study demonstrates improved accuracy in mapping plastic mulch coverage, which is important for monitoring agricultural plastic waste that contributes to soil microplastic pollution.
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.
Reply on RC2
This paper is a peer-review response document (Reply on RC2) associated with a study using UAV remote sensing and proximal sensing to monitor plastic film residues on agricultural soils. The underlying research investigates using drone-based imaging to track macroplastic and potentially microplastic precursors in croplands, which could enable large-scale monitoring of plastic pollution from agricultural plastic use.
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.
Microplastic Pollution In Agricultural Lands And Its Environmental Impact Assessed Through Remote Sensing
Researchers combined field sampling and remote sensing to assess microplastic pollution in agricultural soils across three Indian locations, finding microplastics in both surface and subsurface layers and correlating pollution levels with land use patterns detectable by satellite imagery.