Datasets:

Project-AgML
/

blackgram_plant_leaf_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': anthracnose
            '1': healthy
            '2': leaf_crinckle
            '3': powdery_mildew
            '4': yellow_mosaic
  splits:
    - name: train
      num_bytes: 121519312
      num_examples: 1007
  download_size: 121530545
  dataset_size: 121519312

Blackgram Plant Leaf Disease Classification

A dataset for disease classification of Blackgram leaves. The dataset contains 1,007 images across 5 classes: anthracnose, healthy, leaf_crinckle, powdery_mildew, yellow_mosaic.
Images per class:

anthracnose: 230
healthy: 221
leaf_crinckle: 152
powdery_mildew: 180
yellow_mosaic: 224

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

Citation

@article{talasila2022black,
  title={Black gram Plant Leaf Disease (BPLD) dataset for recognition and classification of diseases using computer-vision algorithms},
  author={Talasila, Srinivas and Rawal, Kirti and Sethi, Gaurav and Mss, Sanjay and others},
  journal={Data in Brief},
  volume={45},
  pages={108725},
  year={2022},
  publisher={Elsevier}
}

Talasila, Srinivas; Rawal, Kirti; Sethi, Gaurav; MSS, Sanjay; M, Surya Prakash Reddy (2022), “Blackgram Plant Leaf Disease Dataset”, Mendeley Data, V3, doi: 10.17632/zfcv9fmrgv.3