Datasets:

Project-AgML
/

CottonWeedID15_classification

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Carpetweeds
            '1': Crabgrass
            '2': Eclipta
            '3': Goosegrass
            '4': Morningglory
            '5': Nutsedge
            '6': PalmerAmaranth
            '7': Prickly Sida
            '8': Purslane
            '9': Ragweed
            '10': Sicklepod
            '11': SpottedSpurge
            '12': SpurredAnoda
            '13': Swinecress
            '14': Waterhemp
    - name: date
      dtype: string
    - name: device
      dtype: string
  splits:
    - name: train
      num_bytes: 7072218033
      num_examples: 5187
  download_size: 10352091364
  dataset_size: 7072218033
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-nc-4.0
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K

Cottonweedid15 Classification

This dataset contains real-world RGB images of 15 common weed species found in cotton agricultural fields, captured under natural field conditions. The images depict weeds growing in typical cotton crop environments, providing a practical resource for developing and evaluating computer vision models for weed identification in precision agriculture applications. The dataset contains 5,187 images across 15 classes: Carpetweeds, Crabgrass, Eclipta, Goosegrass, Morningglory, Nutsedge, PalmerAmaranth, Prickly Sida, Purslane, Ragweed, Sicklepod, SpottedSpurge, SpurredAnoda, Swinecress, Waterhemp.
Images per class:

Carpetweeds: 763
Crabgrass: 111
Eclipta: 254
Goosegrass: 216
Morningglory: 1,115
Nutsedge: 273
PalmerAmaranth: 689
Prickly Sida: 129
Purslane: 450
Ragweed: 129
Sicklepod: 240
SpottedSpurge: 234
SpurredAnoda: 61
Swinecress: 72
Waterhemp: 451

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

Citation

@article{chen2022performance,
  title={Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems},
  author={Chen, Dong and Lu, Yuzhen and Li, Zhaojian and Young, Sierra},
  journal={Computers and Electronics in Agriculture},
  volume={198},
  pages={107091},
  year={2022},
  publisher={Elsevier}
}

https://www.kaggle.com/datasets/yuzhenlu/cottonweedid15

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