---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Healthy
          '1': Mosaic
          '2': Rust
          '3': Semilooper_Pest
  splits:
  - name: train
    num_bytes: 4710615544
    num_examples: 2842
  download_size: 4442303174
  dataset_size: 4710615544
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- n<1K
---

# MH SoyaHealthVision Disease Classification UAV

A dataset for disease classification of soybean leaves. The dataset contains 2,842 images across 4 classes: Healthy, Mosaic, Rust, Semilooper_Pest.  
Images per class:
- Healthy: 280
- Mosaic: 772
- Rust: 1,000
- Semilooper_Pest: 790

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

## Citation

```bibtex
@article{shinde2025indian,
  title={An Indian UAV and leaf image dataset for integrated crop health assessment of soybean crop},
  author={Shinde, Sayali and Attar, Vahida},
  journal={Data in Brief},
  volume={60},
  pages={111517},
  year={2025},
  publisher={Elsevier}
}
```

Shinde, Sayali; Attar, Dr.Vahida; Technological University,Pune, COEP ; Technology Innovation Hub, Indian Statistical Institute Kolkata, IDEAS (2024), “MH-SoyaHealthVision: An Indian UAV and Leaf Image Dataset for Integrated Crop Health Assessment”, Mendeley Data, V1, doi: 10.17632/hkbgh5s3b7.1