Project-AgML/rangeland_weeds_australia

Rangeland Weeds Australia

A dataset for image classification of rangeland weeds. The dataset contains 17,509 images across 9 classes: chinee_apple, lantana, negative, parkinsonia, parthenium, prickly_acacia, rubber_vine, siam_weed, snake_weed.
Images per class:

• chinee_apple: 1,125
• lantana: 1,064
• negative: 9,106
• parkinsonia: 1,031
• parthenium: 1,022
• prickly_acacia: 1,062
• rubber_vine: 1,009
• siam_weed: 1,074
• snake_weed: 1,016

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

Citation

@Article{Olsen2019,
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 Calvert, Brendan and Azghadi, Mostafa Rahimi and White, Ronald D.},
title={DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning},
journal={Scientific Reports},
year={2019},
month={Feb},
day={14},
volume={9},
number={1},
pages={2058},
issn={2045-2322},
doi={10.1038/s41598-018-38343-3},
url={https://doi.org/10.1038/s41598-018-38343-3}
}

https://github.com/AlexOlsen/DeepWeeds