Datasets:

Project-AgML
/

maize_weed_detection

dataset_info:
  features:
    - name: image
      dtype: image
    - name: objects
      struct:
        - name: bbox
          list:
            list: float64
        - name: categories
          list:
            class_label:
              names:
                '0': maize
                '1': weed
  splits:
    - name: train
      num_bytes: 816981868
      num_examples: 500
  download_size: 817005071
  dataset_size: 816981868
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - object-detection
size_categories:
  - n<1K

Maize Weed Detection

A dataset for detection of maize and weeds. The dataset contains 500 images with 5,764 bounding box annotations across 2 categories.

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

Citation

@article{olaniyi2023development,
  title={Development of maize plant dataset for intelligent recognition and weed control},
  author={Olaniyi, Olayemi Mikail and Salaudeen, Muhammadu Tajudeen and Daniya, Emmanuel and Abdullahi, Ibrahim Mohammed and Folorunso, Taliha Abiodun and Bala, Jibril Abdullahi and Nuhu, Bello Kontagora and Adedigba, Adeyinka Peace and Oluwole, Blessing Israel and Bankole, Abdullah Oreoluwa and others},
  journal={Data in Brief},
  volume={47},
  pages={109030},
  year={2023},
  publisher={Elsevier}
}

Olaniyi, Olayemi; Salaudeen, Muhammadu; Daniya, Emmanuel; Mohammed Abdullahi, Ibrahim; Folorunso, Taliha Abiodun; Bala, Jibril; Bello Kontagora, Nuhu; Adedigba, Adeyinka; Oluwole, Blessing ; Macarthy, Odunayo; Bankole, Abdullah (2022), “Maize-Weed Image Dataset ”, Mendeley Data, V2, doi: 10.17632/jjbfcckrsp.2

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