Unmanned aerial vehicle-based weed segmentation from multispectral imagery in an edge computing environment

Unmanned aerial vehicles (UAVs) equipped with computer vision techniques offer a practical and scalable solution for large-scale weed mapping in agricultural fields. Accurate pixel-level discrimination of soil, crops, and weeds from UAV imagery remains challenging due to severe class imbalance and the high visual similarity between crop and weed structures. Many existing segmentation approaches are either computationally demanding or depend on ensemble strategies, which restrict their applicability on edge or real-time platforms. To address these limitations, this study introduces a progressive receptive-field context-aware network (PRC-Net) designed for efficient deployment under constrained computational resources. The proposed progressive receptive-field fusion (PRF) module incrementally enlarges the receptive field across multiple scales, enabling improved identification of small crop and weed regions within predominantly soil backgrounds. A channel synergy-focused attention (CSA) mechanism is incorporated to selectively enhance informative spectral bands and their inter-channel relationships in multispectral data, thereby improving class separability. Furthermore, a lightweight context-aware feature extraction (LCFE) module establishes a compact encoder–decoder bottleneck that facilitates effective contextual refinement while reducing parameter redundancy and overfitting risk. Collectively, these components retain discriminative features of minority classes and enhance spectral segmentation quality with minimal computational overhead. PRC-Net is validated on the publicly available WeedMap (RedEdge-M, Sequoia) and Sesame Aerial datasets, achieving mean intersection over union (MIOU) values of 0.8397, 0.6520, and 0.6961, respectively. Experimental performance highlights that PRC-Net achieves superior performance and efficiency compared to existing methods, confirming its suitability for UAV-based weed detection in precision farming.