Project-AgML/WPDv2_BBCH_classification

Wpdv2 Bbch Classification

This dataset provides real-world RGB images of agricultural weeds annotated using the BBCH growth stage classification system. It captures diverse weed species under typical field conditions relevant to precision agriculture applications. The images serve as a foundation for developing computer vision models focused on weed identification in crop environments. The dataset contains 3,920 images across 5 classes: Amaranthus_retroflexus (AMARE), Amaranthus_tuberculatus (AMATU), Chenopodium_album (CHEAL), Echinochloa_crus-galli (ECHCG), Setaria_faberi (SETFA).
Images per class:

• Amaranthus_retroflexus (AMARE): 934
• Amaranthus_tuberculatus (AMATU): 409
• Chenopodium_album (CHEAL): 832
• Echinochloa_crus-galli (ECHCG): 768
• Setaria_faberi (SETFA): 977

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

Citation

@article{fontaine2026taxonomical,
  title={Taxonomical loss for weed seedlings image classification},
  author={Fontaine, Hans-Olivier and Foucher, Samuel and Fallon, Edith and Simard, Marie-Jos{\'e}e and Lord, Etienne},
  journal={Scientific Reports},
  volume={16},
  number={1},
  pages={3837},
  year={2026},
  publisher={Nature Publishing Group UK London}
}

https://github.com/etiennelord/TaxonomicalLoss

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