Identification of Plastic Mulch in Cotton Fields Using UAV-Based Hyperspectral Data and Deep Learning Semantic Segmentation

Plastic mulching is widely used in arid and semi-arid cotton systems to improve soil hydrothermal conditions and water–nutrient use efficiency. However, residual mulch and its potential contribution to microplastic inputs pose growing environmental and soil-quality risks, highlighting the need for high-resolution and automated approaches to support plastic waste management, targeted retrieval, and precision field operations. Taking a mulched cotton field in Alar, Xinjiang, as the study area, this study proposes a novel plastic mulch extraction method that integrates Unmanned Aerial Vehicle (UAV)-based hyperspectral imagery with deep learning semantic segmentation. The Jeffries–Matusita (JM) distance was employed to select highly separable optimal bands and their combinations for discriminating plastic mulch, bare soil, and cotton canopy, which were then used to drive UNet, DeepLabV3+, and PSPNet models for plastic mulch mapping. The results indicate that the PSPNet model driven by the 402 nm single-band reflectance, Normalized Difference Index (NDI) (861 nm, 410 nm), and NDI (757 nm, 676 nm) achieved the best performance for plastic mulch identification (Intersection over Union (IoU) = 80.28%), significantly outperforming the RGB-based model (IoU = 76.51%). This study enables accurate, spatially explicit assessments of residual mulch, providing actionable evidence for plastic waste monitoring and management, while supporting sustainable agriculture and precision farmland management.