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Identification of Farmland Mulching Film Types Based on UAV Multispectral Imagery and Analysis of Its Recycling Application: A Case Study of the Hetao Irrigation District
Original title: 基于无人机多光谱影像的农田覆膜类型识别及回收应用分析——以河套灌区为例
Summary
This isn't a health study — it's an engineering paper about using drone imagery to map plastic mulch film on farmland (a common farming technique to control weeds and retain moisture). This matters for human health indirectly: leftover plastic film in soil breaks down into microplastics that can enter crops, water, and eventually our food supply, so better tools to track and recover this plastic could help reduce that contamination over time.
The spatial distribution of plastic-mulched farmland is fundamental for verifying mulch input, preventing residual film pollution, organizing mechanized recovery, and planning resource-oriented recycling facilities. To address fragmented field patterns, coexisting mulch colors, interference from post-irrigation water and greenhouse structures, and the unclear engineering adaptability of semantic segmentation models in the Hetao Irrigation District, this study used five-band UAV multispectral orthomosaics acquired from 2022 to 2024. A five-class classification system was established, including non-cultivated land, other mulched farmland, black narrow-mulched farmland, unmulched farmland, and white narrow-mulched farmland. Ten representative semantic segmentation models were evaluated at spatial resolutions of 0.08, 1.35, 2.7, and 5.4 m. To approximate operational use, only radiometrically corrected original five-band reflectance was used as model input, without additional spectral indices, texture features, or external environmental variables.