Datasets:

Project-AgML
/

apple_leaf_disease_classification

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Alternaria
            '1': Apple_Mosaic
            '2': Healthy
  splits:
    - name: train
      num_bytes: 64844454
      num_examples: 5612
  download_size: 66907339
  dataset_size: 64844454
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

Apple Leaf Disease Classification

A dataset for image classification of Apple Leaf Disease Classification. The dataset contains 7,505 images across 3 classes: Alternaria, Apple_Mosaic, Healthy.
Images per class:

Alternaria: 2,523
Apple_Mosaic: 2,523
Healthy: 2,459

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

Citation

@article{yatoo2024indigenous,
  title={An indigenous dataset for the detection and classification of apple leaf diseases},
  author={Yatoo, Arshad Ahmad and Sharma, Amit},
  journal={Data in Brief},
  volume={53},
  pages={110165},
  year={2024},
  publisher={Elsevier}
}

Yatoo, Arshad; Sharma, Amit (2024), “Indigenous Dataset for Apple Leaf Disease Detection and Classification”, Mendeley Data, V3, doi: 10.17632/9m2dcb5mmr.3