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Project-AgML
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DeepWeeds_classification

image imagewidth (px) 256 256	label class label 9 classes	species stringclasses 9 values
	88	Negative
	22	Parkinsonia
	22	Parkinsonia
	33	Parthenium
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	00	Chinee apple
	33	Parthenium
	88	Negative
	88	Negative
	88	Negative
	22	Parkinsonia
	55	Rubber vine
	88	Negative
	33	Parthenium
	88	Negative
	00	Chinee apple
	88	Negative
	55	Rubber vine
	88	Negative
	11	Lantana
	11	Lantana
	66	Siam weed
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	00	Chinee apple
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	11	Lantana
	33	Parthenium
	00	Chinee apple
	88	Negative
	77	Snake weed
	44	Prickly acacia
	22	Parkinsonia
	88	Negative
	88	Negative
	11	Lantana
	88	Negative
	88	Negative
	77	Snake weed
	77	Snake weed
	88	Negative
	66	Siam weed
	22	Parkinsonia
	77	Snake weed
	44	Prickly acacia
	88	Negative
	22	Parkinsonia
	88	Negative
	22	Parkinsonia
	55	Rubber vine
	88	Negative
	88	Negative
	88	Negative
	66	Siam weed
	77	Snake weed
	88	Negative
	00	Chinee apple
	77	Snake weed
	88	Negative
	88	Negative
	22	Parkinsonia
	00	Chinee apple
	88	Negative
	88	Negative
	33	Parthenium
	88	Negative
	11	Lantana
	22	Parkinsonia
	88	Negative
	33	Parthenium
	44	Prickly acacia
	00	Chinee apple
	33	Parthenium
	22	Parkinsonia
	88	Negative
	88	Negative
	11	Lantana
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	88	Negative
	11	Lantana
	44	Prickly acacia

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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

@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.

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