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Project-AgML/DeepWeeds_classification

Deepweeds Classification This dataset comprises real-world RGB images capturing various weed species in agricultural field environments. Collected under natural outdoor conditions, the images provide a diverse visual representation of weeds for computer vision applications in precision agriculture. The dataset contains 17,509 images across 9 classes: 0, 1, 2, 3, 4, 5, 6, 7, 8.Images per class: 0: 1,125 1: 1,064 2: 1,031 3: 1,022 4: 1,062 5: 1,009 6: 1,074 7: 1,016 8: 9,106… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/DeepWeeds_classification.

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Deepweeds Classification

This dataset comprises real-world RGB images capturing various weed species in agricultural field environments. Collected under natural outdoor conditions, the images provide a diverse visual representation of weeds for computer vision applications in precision agriculture. The dataset contains 17,509 images across 9 classes: 0, 1, 2, 3, 4, 5, 6, 7, 8. Images per class:

—0: 1,125
—1: 1,064
—2: 1,031
—3: 1,022
—4: 1,062
—5: 1,009
—6: 1,074
—7: 1,016
—8: 9,106

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

Citation

bibtex

@article{olsen2019deepweeds,
  title={DeepWeeds: A multiclass weed species image dataset for deep learning},
  author={Olsen, Alex and Konovalov, Dmitry A and Philippa, Bronson and Ridd, Peter and Wood, Jake C and Johns, Jamie and Banks, Wesley and Girgenti, Benjamin and Kenny, Owen and Whinney, James and others},
  journal={Scientific reports},
  volume={9},
  number={1},
  pages={2058},
  year={2019},
  publisher={Nature Publishing Group UK London}
}

https://github.com/AlexOlsen/DeepWeeds

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