CamelinaWeed: an expert-agronomist-annotated UAV RGB and multispectral dataset for weed and crop monitoring in Camelina sativa.

This dataset provides UAV-based RGB and multispectral imagery for crop monitoring, weed mapping, and field-level analysis in Camelina sativa cultivation. Data were collected from three agricultural fields in Thessaloniki and Chalkidiki, Greece, during summer 2025 and winter 2025-2026, capturing variability across locations, seasons, crop growth stages, UAV platforms, flight altitudes, spatial resolutions, illumination conditions, and sensing modalities. The dataset includes 3023 manually annotated RGB UAV images with human expert-generated polygon annotations of weed instances. The annotation scheme includes both coarse weed categories, such as broadleaf, narrowleaf, and generic weed classes, and fine-grained species-level labels, supporting classification, object detection, semantic and instance segmentation, hierarchical learning, and weed distribution analysis. In addition, the dataset provides RGB and multispectral UAV imagery, the raw RGB and multispectral images used for orthomosaic reconstruction, and both RGB and multispectral orthomosaic products in GeoTIFF format. The data were acquired using DJI Phantom 4 Pro and DJI Mavic 3 M UAV platforms at different flight altitudes, resulting in multiple ground sampling distances and image resolutions. This dataset is intended to support the development, benchmarking, and validation of computer vision and precision agriculture methods under realistic field conditions. To the best of our knowledge, it is among the first publicly available UAV datasets specifically focused on weed monitoring and field analysis in Camelina sativa crops.