BAWSeg: A UAV Multispectral Benchmark for Barley Weed Segmentation

TL;DR

This paper introduces VISA, a dual-stream neural network for precise UAV-based multispectral weed segmentation in barley fields, demonstrating superior accuracy and robustness across different conditions and a new four-year dataset.

Contribution

The paper presents VISA, a novel two-stream segmentation architecture that decouples radiance and index cues, and introduces BAWSeg, a comprehensive UAV multispectral dataset for barley weed mapping.

Findings

VISA achieves 75.6% mIoU and 63.5% weed IoU on BAWSeg.

VISA outperforms baseline models by 1.2 mIoU and 1.9 weed IoU.

VISA maintains high accuracy under cross-plot and cross-year evaluations.

Abstract

Accurate weed mapping in cereal fields requires pixel-level segmentation from UAV imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop--weed pixels, or on single-stream CNN and Transformer backbones that ingest stacked bands and indices, where radiance cues and normalized index cues interfere and reduce sensitivity to small weed clusters embedded in crop canopy. We propose VISA, a two-stream segmentation network that decouples these cues and fuses them at native resolution. The radiance stream learns from calibrated five-band reflectance using local residual convolutions, channel recalibration, spatial gating, and skip-connected decoding, which preserve fine textures, row boundaries, and small weed structures that are often weakened after ratio-based index compression. The index stream operates on vegetation-index maps with windowed self-attention to model local structure efficiently, state-space layers to propagate field-scale context without quadratic attention cost, and Slot Attention to form stable region descriptors that improve discrimination of sparse weeds under canopy mixing. To support supervised training and deployment-oriented evaluation, we introduce BAWSeg, a four-year UAV multispectral dataset collected over commercial barley paddocks in Western Australia, providing radiometrically calibrated blue, green, red, red edge, and near-infrared orthomosaics, derived vegetation indices, and dense crop, weed, and other labels with leakage-free block splits. On BAWSeg, VISA achieves 75.6% mIoU and 63.5% weed IoU with 22.8 M parameters, outperforming a multispectral SegFormer-B1 baseline by 1.2 mIoU and 1.9 weed IoU. Under cross-plot and cross-year protocols, VISA maintains 71.2% and 69.2% mIoU, respectively.