---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Anthracnose
          '1': Anthracnose lesions
          '2': Black Rot
          '3': Downey mildew
          '4': Downy mildew
          '5': Eggplant Cercopora leaf spot
          '6': Eggplant begomovirus
          '7': Eggplant fresh leaf
          '8': Eggplant verticillium wilt
          '9': Fresh leaf
          '10': Fusarium wilt
          '11': Mosaic virus
          '12': Tomato Bacterial spot
          '13': Tomato Fresh leaf
          '14': Tomato leaf curl virus
          '15': Tomato spotted wilt
  - name: plant_type
    dtype:
      class_label:
        names:
          '0': Bitter Gourd
          '1': Bottle gourd
          '2': Cauliflower
          '3': Cucumber
          '4': Eggplant
          '5': Tomato
  splits:
  - name: train
    num_bytes: 812512223
    num_examples: 12786
  download_size: 958168710
  dataset_size: 812512223
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 10K<n<100K
---

# Plant Leaf Disease Classification

A dataset for disease classification of leaves from Bitter Gourd, Bottle Gourd, Tomato, Eggplant, Cauliflower, and Cucumber. The dataset contains 12,786 images across 16 classes:  
Images per class:
- Anthracnose: 601
- Anthracnose lesions: 535
- Black Rot: 560
- Downey mildew: 1,254
- Downy mildew: 1,076
- Eggplant Cercopora leaf spot: 723
- Eggplant begomovirus: 720
- Eggplant fresh leaf: 771
- Eggplant verticillium wilt: 730
- Fresh leaf: 2,122
- Fusarium wilt: 502
- Mosaic virus: 600
- Tomato Bacterial spot: 589
- Tomato Fresh leaf: 594
- Tomato leaf curl virus: 755
- Tomato spotted wilt: 654

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

## Citation

```bibtex
@article{hasan2024comprehensive,
  title={Comprehensive smart smartphone image dataset for plant leaf disease detection and freshness assessment from Bangladesh vegetable fields},
  author={Hasan, Mahamudul and Gani, Raiyan and Rashid, Mohammad Rifat Ahmmad and Tarin, Taslima Khan and Kamara, Raka and Mou, Mahbuba Yasmin and Rabbi, Sheikh Fajlay},
  journal={Data in Brief},
  volume={56},
  pages={110775},
  year={2024},
  publisher={Elsevier}
}
```

Rashid, Mohammad Rifat Ahmmad; Tarin, Taslima Khan ; Kamara, Raka ; Mou, Mahbuba Yasmin ; Rabbi, Sheikh Fajlay ; Hasan, Mahamudul (2024), “Plant Leaf Freshness and Disease Detection Dataset From Bangladesh”, Mendeley Data, V3, doi: 10.17632/n67gctmjyj.3