---
configs:
- config_name: raw
  default: true
  data_dir: raw
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Anthracnose
          '1': Canker
          '2': Dot
          '3': Healthy
          '4': Rust
          '5': Scab
          '6': Styler end root
  - name: plant_part
    dtype: string
- config_name: augmented
  data_dir: augmented
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Anthracnose
          '1': Canker
          '2': Dot
          '3': Healthy
          '4': Rust
          '5': Scab
          '6': Styler end root
  - name: plant_part
    dtype: string
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 10K<n<100K
---

# Guava Disease Classification

A dataset for disease classification of guava fruits and leaves. The dataset contains raw and augmented versions.  
The raw dataset contains 3,049 images.  
Images per class:
- Anthracnose: 500
- Canker: 192
- Dot: 219
- Healthy: 1,590
- Rust: 167
- Scab: 119
- Styler end root: 262

The augmented dataset contains 20,344 images.  
Images per class:
- Anthracnose: 4,026
- Canker: 1,344
- Dot: 1,533
- Healthy: 8,724
- Rust: 1,169
- Scab: 1,190
- Styler end root: 2,358


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

## Citation

```bibtex
@article{shihab2025image,
  title={Image dataset for classification of diseases in guava fruits and leaves},
  author={Shihab, Montasir Rahman and Saim, Nafiu Islam and Mojumdar, Mayen Uddin and Raza, Dewan Mamun and Siddiquee, Shah Md Tanvir and Noori, Sheak Rashed Haider and Chakraborty, Narayan Ranjan},
  journal={Data in Brief},
  volume={59},
  pages={111378},
  year={2025},
  publisher={Elsevier}
}
```

Shihab, Md Montsir Rahman; Saim, Nafiu Islam; Mojumdar, Mayen Uddin  (2024), “Image Dataset for Detection and classification of Diseases of Guava Fruits and Leaves ”, Mendeley Data, V1, doi: 10.17632/fspx44mwfp.1