README.md

---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': '0'
          '1': '1'
          '2': '2'
          '3': '3'
          '4': '4'
          '5': '5'
  - name: treatment_camera
    dtype: string
  splits:
  - name: train
    num_bytes: 698277360
    num_examples: 1892
  download_size: 698351475
  dataset_size: 698277360
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
---
# Soybean Leaf Wilt Classification 2

This dataset provides real-world RGB images of soybean leaves exhibiting varying degrees of wilt symptoms, collected in a field environment at Jackson Springs, North Carolina, during August and September 2020. Images were captured using a fixed platform to ensure consistent phenotypic observations under natural agricultural conditions. It supports computer vision research focused on disease detection and crop health assessment in soybean cultivation. The dataset contains 1,892 images across 6 classes: 0, 1, 2, 3, 4, 5.  
Images per class:
- 0: 524
- 1: 473
- 2: 369
- 3: 216
- 4: 206
- 5: 104

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

## Citation

```bibtex
@article{banerjee2025multiple,
  title={A Multiple Instance Learning Approach to Study Leaf Wilt in Soybean Plants},
  author={Banerjee, Sanjana and Ramos, Paula and Reberg-Horton, Chris and Mirsky, Steven and Locke, Anna and Lobaton, Edgar},
  journal={Agriculture},
  volume={15},
  pages={614},
  year={2025},
  publisher={MDPI}
}
```


The dataset itself can be cited as:

Sanjana Banerjee, Paula Ramos, Chris Reberg-Horton, Steven Mirsky, Anna Locke, &amp; Edgar Lobaton. (2023). *Study of Leaf Wilt in Soybean Plants* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.8256382


*This dataset was reformatted from its original format to match HuggingFace standards.*