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Project-AgML/MH_Weed16_weed_variety_classification

MH Weed16 Weed Variety Classification A dataset for variety classification of weeds. The dataset contains 19,141 images across 16 classes:Images per class: Asian_Pigeonwings_(Clitoria Ternatea): 678 Bilayat_(Mexicana_Argemone): 859 Choti_dudhi_(Euphorbia_hirta): 580 Digitaria_SP_(Digitaria Sanguinalis ): 1,561 Dwarf_cassia_(Chamaecrista pumila): 1,306 Gajar_gavat_(Parthenium hysterophorus): 1,543 Graceful_Sandmart_(Euphorbia hypericifolia): 262 Harali_(Cynodon_dactylon): 709… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/MH_Weed16_weed_variety_classification.

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MH Weed16 Weed Variety Classification

A dataset for variety classification of weeds. The dataset contains 19,141 images across 16 classes: Images per class:

—AsianPigeonwings(Clitoria Ternatea): 678
—Bilayat(MexicanaArgemone): 859
—Chotidudhi(Euphorbia_hirta): 580
—DigitariaSP(Digitaria Sanguinalis ): 1,561
—Dwarfcassia(Chamaecrista pumila): 1,306
—Gajargavat(Parthenium hysterophorus): 1,543
—GracefulSandmart(Euphorbia hypericifolia): 262
—Harali(Cynodondactylon): 709
—Kena(Commplinabenghalensio): 1,704
—LamberQuarterplant(Chenopodium ): 836
—Lavhala(CyperusRotundus): 1,177
—Little_Mallow(Malva parviflora): 2,100
—Motidudhi(Euphorbiageneculata_L): 3,002
—Obscuremorning glory(Ipomoea obscura): 1,637
—Punarnava _(Boerhaavia diffusa): 544
—Sicklepod_(Senna obtusifolia): 643

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

Citation

bibtex

@article{shinde2025indian,
  title={An Indian annotated weed dataset for computer vision tasks in precision farming},
  author={Shinde, Sayali and Attar, Vahida},
  journal={Data in Brief},
  volume={61},
  pages={111691},
  year={2025},
  publisher={Elsevier}
}

Shinde, Sayali; Attar, Dr. Vahida; Technological University Pune, COEP; Technology Innovation Hub, Indian Statistical Institute Kolkata, IDEAS (2025), “MH-Weed16:An Indian Multiclass Annotated Weed Dataset for Computer Vision Tasks ”, Mendeley Data, V2, doi: 10.17632/d3n3mgjjbv.2