Datasets:

Project-AgML
/

watermelon_disease_classification

dataset_info:
  - config_name: augmented
    features:
      - name: image
        dtype: image
      - name: label
        dtype:
          class_label:
            names:
              '0': Anthracnose
              '1': Downy_Mildew
              '2': Healthy
              '3': Mosaic_Virus
    splits:
      - name: train
        num_bytes: 4217352397
        num_examples: 5775
    download_size: 4426618662
    dataset_size: 4217352397
  - config_name: raw
    features:
      - name: image
        dtype: image
      - name: label
        dtype:
          class_label:
            names:
              '0': Anthracnose
              '1': Downy_Mildew
              '2': Healthy
              '3': Mosaic_Virus
    splits:
      - name: train
        num_bytes: 2231509933
        num_examples: 1155
    download_size: 2385746959
    dataset_size: 2231509933
configs:
  - config_name: augmented
    data_files:
      - split: train
        path: augmented/train-*
  - config_name: raw
    default: true
    data_files:
      - split: train
        path: raw/train-*
license: cc-by-4.0
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K

Watermelon Disease Classification

A dataset for disease classification of Watermelon. The dataset contains raw and augmented versions.
The raw dataset contains 1,155 images.
Images per class:

Anthracnose: 155
Downy_Mildew: 380
Healthy: 205
Mosaic_Virus: 415

The augmented dataset contains 5,775 images.
Images per class:

Anthracnose: 775
Downy_Mildew: 1,900
Healthy: 1,025
Mosaic_Virus: 2,075

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

Citation

@article{nakib2024comprehensive,
  title={Comprehensive watermelon disease recognition dataset},
  author={Nakib, Mohammad Imtiaz and Mridha, MF},
  journal={Data in Brief},
  volume={53},
  pages={110182},
  year={2024},
  publisher={Elsevier}
}

Nakib, Mohammad Imtiaz ; Mridha, Firoz (2023), “Watermelon Disease Recognition Dataset”, Mendeley Data, V1, doi: 10.17632/ntzym554jp.1