---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': diseased
          '1': healthy
  - name: species
    dtype: string
  - name: cultivar
    dtype: string
  splits:
  - name: train
    num_bytes: 7312118052
    num_examples: 4502
  download_size: 7312376792
  dataset_size: 7312118052
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
---
# Leafnet Plant Disease Classification

This dataset comprises real-world RGB images of plant leaves, capturing both diseased and healthy specimens for disease detection tasks. Collected under typical agricultural imaging conditions, it provides a practical resource for developing computer vision models in plant pathology applications. The dataset contains 4,502 images across 2 classes: diseased, healthy.  
Images per class:
- diseased: 2,225
- healthy: 2,277

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

## Citation

```bibtex
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```

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The dataset itself can be cited as:

Siddharth Singh Chouhan. (2022). *A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology* [Dataset]. Mendeley. https://doi.org/10.17632/HB74YNKJCN.5


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