Datasets:

Project-AgML
/

OPPD_plant_detection

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: objects
      struct:
        - name: bbox
          list:
            list: int64
        - name: categories
          list:
            class_label:
              names:
                '0': ALOMY
                '1': ANGAR
                '2': APESV
                '3': ARTVU
                '4': AVEFA
                '5': BROST
                '6': BRSNN
                '7': CAPBP
                '8': CENCY
                '9': CHEAL
                '10': CHYSE
                '11': CIRAR
                '12': CONAR
                '13': EPHHE
                '14': EPHPE
                '15': EROCI
                '16': FUMOF
                '17': GALAP
                '18': GERMO
                '19': LAPCO
                '20': LOLMU
                '21': LYCAR
                '22': MATCH
                '23': MATIN
                '24': MELNO
                '25': MYOAR
                '26': PAPRH
                '27': PLALA
                '28': PLAMA
                '29': POAAN
                '30': POLAV
                '31': POLCO
                '32': POLLA
                '33': POLPE
                '34': RUMCR
                '35': SENVU
                '36': SINAR
                '37': SOLNI
                '38': SONOL
                '39': STEME
                '40': THLAR
                '41': URTUR
                '42': VERAR
                '43': VERPE
                '44': VICHI
                '45': VIOAR
    - name: species
      dtype: string
    - name: location
      dtype: string
  splits:
    - name: train
      num_bytes: 49542868853
      num_examples: 7590
  download_size: 44700045707
  dataset_size: 49542868853
license: cc-by-nc-sa-4.0
task_categories:
  - object-detection
size_categories:
  - 1K<n<10K

Oppd Plant Detection

This dataset provides real-world RGB images of multiple plant species in agricultural field settings, designed for object detection research. Captured under natural lighting conditions, it offers a diverse collection of plant instances for computer vision applications in precision agriculture. The dataset contains 7,590 images with 315,041 bounding box annotations across 46 categories.

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

Citation

@article{leminen2020open,
  title={Open plant phenotype database of common weeds in Denmark},
  author={Leminen Madsen, Simon and Mathiassen, Solvejg Kopp and Dyrmann, Mads and Laursen, Morten Stigaard and Paz, Laura-Carlota and J{\o}rgensen, Rasmus Nyholm},
  journal={Remote Sensing},
  volume={12},
  number={8},
  pages={1246},
  year={2020},
  publisher={MDPI}
}

https://vision.eng.au.dk/open-plant-phenotyping-database/

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