Project-AgML/carrot_weed_segmentation

Carrot Weed Segmentation

This dataset provides real-world RGB imagery capturing carrot cultivation fields with co-occurring weed species, focusing on the visual distinction between crop and weed vegetation. The images were collected in natural agricultural environments using standard RGB imaging equipment, offering a realistic context for training and evaluating semantic segmentation models in precision agriculture applications. The dataset contains 20 images with pixel-level mask annotations.

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

Citation

@article{bosilj2020transfer,
  title={Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture},
  author={Bosilj, Petra and Aptoula, Erchan and Duckett, Tom and Cielniak, Grzegorz},
  journal={Journal of Field Robotics},
  volume={37},
  number={1},
  pages={7--19},
  year={2020},
  publisher={Wiley Online Library}
}

Petra Bosilj, Erchan Aptoula, Tom Duckett, and Grzegorz Cielniak: “Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture”, Journal of Field Robotics (2019)

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