Project-AgML/WE3DS_weed_segmentation

We3ds Weed Segmentation

This dataset provides real-world RGB imagery focused on weed segmentation within agricultural fields. It captures diverse weed and crop patterns under natural field conditions, offering a practical resource for developing and evaluating semantic segmentation models in precision agriculture applications. The dataset contains 2,568 images with pixel-level mask annotations.

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

The original train/test/val split has been preserved in the split column.

Citation

@article{kitzler2023we3ds,
  title={WE3DS: An RGB-D image dataset for semantic segmentation in agriculture},
  author={Kitzler, Florian and Barta, Norbert and Neugschwandtner, Reinhard W and Gronauer, Andreas and Motsch, Viktoria},
  journal={Sensors},
  volume={23},
  number={5},
  pages={2713},
  year={2023},
  publisher={MDPI}
}

Kitzler, F., Barta, N., Neugschwandtner, R. W., Gronauer, A., & Motsch, V. (2023). WE3DS: An RGB-D image dataset for semantic segmentation in agriculture [Dataset]. In Sensors (Vol. 23, Issue 5, p. 2713). Zenodo. https://doi.org/10.5281/zenodo.7457983

This dataset was reformatted from its original format to match HuggingFace standards.