Datasets:

Project-AgML
/

pomegranate_growth_detection

dataset_info:
  features:
    - name: image
      dtype: image
    - name: objects
      struct:
        - name: bbox
          list:
            list: float64
        - name: categories
          list:
            class_label:
              names:
                '0': bud
                '1': flower
                '2': early-fruit
                '3': mid-growth
                '4': mature
  splits:
    - name: train
      num_bytes: 887034006
      num_examples: 5857
  download_size: 832778925
  dataset_size: 887034006
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - object-detection
size_categories:
  - 1K<n<10K

Pomegranate Growth Detection

A dataset for object detection of Pomegranates as they grow. The dataset contains 5,857 images with 11,484 bounding box annotations across 5 categories.

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

Citation

@article{zhao2023dataset,
  title={A dataset of pomegranate growth stages for machine learning-based monitoring and analysis},
  author={Zhao, Jifei and Almodfer, Rolla and Wu, Xiaoying and Wang, Xinfa},
  journal={Data in brief},
  volume={50},
  pages={109468},
  year={2023},
  publisher={Elsevier}
}

Zhao, Jifei; Almodfer, Rolla (2023), “Pomegranate Images Dataset”, Mendeley Data, V5, doi: 10.17632/kgwsthf2w6.5