Datasets:

Project-AgML
/

black_gram_disease_classification

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Cercospora leaf spot
            '1': Healthy
            '2': Insect
            '3': Leaf Crinkle
            '4': Yellow Mosaic
  splits:
    - name: train
      num_bytes: 1789298506
      num_examples: 4038
  download_size: 1961483365
  dataset_size: 1789298506

Black Gram Disease Classification

A dataset for disease classification of Black Gram. The dataset contains 4,038 images across 5 classes: Cercospora leaf spot, Healthy, Insect, Leaf Crinkle, Yellow Mosaic. Images per class:

Cercospora leaf spot: 598
Healthy: 545
Insect: 408
Leaf Crinkle: 806
Yellow Mosaic: 1,681

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

Citation

@article{shoib2025idbgl,
  title={IDBGL: A unique image dataset of black gram (Vigna mungo) leaves for disease detection and classification},
  author={Shoib, Md Mehedi Hasan and Saeem, Shahnewaz and Tonima, Afia Benta Aziz and Mojumdar, Mayen Uddin},
  journal={Data in Brief},
  volume={59},
  pages={111347},
  year={2025},
  publisher={Elsevier}
}

Shoib, Md Mehedi Hasan; Saeem, Shahnewaz; Tonima, Afia Benta Aziz; Mojumdar, Mayen Uddin (2024), “Image Dataset for Disease Detection in Black Gram (Vigna mungo) Leaves: A Resource for Machine Learning Research”, Mendeley Data, V3, doi: 10.17632/z55yrbmn2d.3