| --- |
| 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 |
