Datasets:

Project-AgML
/

Luffa_disease_classification

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Alternaria
            '1': Angular Spot
            '2': Fresh
            '3': Holed
            '4': Mosaic Virus
  splits:
    - name: train
      num_bytes: 1007582708
      num_examples: 1227
  download_size: 990926240
  dataset_size: 1007582708
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

Luffa Disease Classification

This dataset provides real field images of luffa plants with disease symptoms, collected in Bangladesh using a handheld smartphone during October 2023. Captured in natural agricultural environments, the images offer a practical resource for developing computer vision models focused on crop disease classification. The dataset contains 1,227 images across 5 classes: Alternaria, Angular Spot, Fresh, Holed, Mosaic Virus.
Images per class:

Alternaria: 270
Angular Spot: 238
Fresh: 256
Holed: 222
Mosaic Virus: 241

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

Citation

@article{sheikh2024luffafolio,
  title={LuffaFolio: A Multidimensional Image Dataset of Smooth Luffa},
  author={Sheikh, Md Ripon and Islam, Md. Masudul and Himel, Galib Muhammad Shahriar},
  journal={Data in Brief},
  volume={53},
  pages={110149},
  year={2024},
  publisher={Elsevier}
}

This dataset was reformatted from its original format to match HuggingFace standards.