Datasets:

Project-AgML
/

MH_SoyaHealthVision_disease_classification_leaf

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Caterpillar_Semilooper_Pest
            '1': Frog_Leaf_Eye
            '2': Healthy
            '3': Mosaic
            '4': Rust
            '5': Spectoria_Brown_Spot
  splits:
    - name: train
      num_bytes: 6310902405
      num_examples: 2782
  download_size: 6127186815
  dataset_size: 6310902405
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

MH SoyaHealthVision Disease Classification Leaf

A dataset for disease classification of soybean leaves. The dataset contains 2,782 images across 6 classes: Caterpillar_Semilooper_Pest, Frog_Leaf_Eye, Healthy, Mosaic, Rust, Spectoria_Brown_Spot.
Images per class:

Caterpillar_Semilooper_Pest: 582
Frog_Leaf_Eye: 169
Healthy: 204
Mosaic: 707
Rust: 852
Spectoria_Brown_Spot: 268

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

Citation

@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