---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Bacterial leaf Blight
          '1': Dry_leaf
          '2': Healthy
          '3': Root_images
          '4': Septoria_Brown_Spot
          '5': Vein Necrosis
  splits:
  - name: train
    num_bytes: 2839590431
    num_examples: 1176
  download_size: 2848562521
  dataset_size: 2839590431
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
---

# Soybean Leaf Disease Classification

A dataset for disease classification of soybean leaves. The dataset contains 1,176 images across 6 classes: Bacterial leaf Blight, Dry_leaf, Healthy, Root_images, Septoria_Brown_Spot, Vein Necrosis.  
Images per class:
- Bacterial leaf Blight: 226
- Dry_leaf: 230
- Healthy: 288
- Root_images: 10
- Septoria_Brown_Spot: 284
- Vein Necrosis: 138

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

## Citation

```bibtex
@article{kotwal2024india,
  title={An India soyabean dataset for identification and classification of diseases using computer-vision algorithms},
  author={Kotwal, Jameer and Kashyap, Ramgopal and Pathan, Mohd Shafi},
  journal={Data in Brief},
  volume={53},
  pages={110216},
  year={2024}
}
```

Kotwal, Jameer ; kashyap, Ramgopal (2023), “   An India soyabean leaf dataset”, Mendeley Data, V1, doi: 10.17632/bshkvgbzpt.1