Datasets:

Project-AgML
/

malabar_spinach_disease_classification

configs:
  - config_name: augmented
    data_files:
      - split: train
        path: augmented/train-*
  - config_name: raw
    data_dir: raw
    default: true
license: cc-by-4.0
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K
dataset_info:
  - config_name: augmented
    features:
      - name: image
        dtype: image
      - name: label
        dtype:
          class_label:
            names:
              '0': anthracnose_leaf_spot
              '1': healthy
              '2': straw_mite
    splits:
      - name: train
        num_bytes: 7866314601
        num_examples: 5868
    download_size: 7868310409
    dataset_size: 7866314601
  - config_name: raw
    features:
      - name: image
        dtype: image
      - name: label
        dtype:
          class_label:
            names:
              '0': anthracnose_leaf_spot
              '1': healthy
              '2': straw_mite
    splits:
      - name: train
        num_bytes: 709284060
        num_examples: 603
    download_size: 709322795
    dataset_size: 709284060

Malabar Spinach Disease Classification

A dataset for disease classification of Malabar Spinach leaves. The dataset contains raw and augmented versions.
The raw dataset contains 603 images.
Images per class:

anthracnose_leaf_spot: 214
healthy: 150
straw_mite: 239

The augmented dataset contains 5,868 images.
Images per class:

anthracnose_leaf_spot: 2,140
healthy: 1,500
straw_mite: 2,228

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

Citation

@article{rahman2025comprehensive,
  title={A comprehensive Malabar Spinach dataset for diseases classification},
  author={Rahman, Mushfiqur and Al Mamun, Md},
  journal={Data in Brief},
  volume={60},
  pages={111532},
  year={2025},
  publisher={Elsevier}
}

Rahman, Mushfiqur; Mukherjee, Anirban ; Shanto , Md Hasibul Hasan (2023), “Malabar Spinach dataset for diseases classification using deep learning approach”, Mendeley Data, V2, doi: 10.17632/n56pn9fncw.2