Datasets:

Project-AgML
/

soybean_leaf_wilt_classification_2

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	11	10-87
	44	10-87
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	11	4-228
	11	10-87
	44	10-87
	22	4-228
	11	3-106
	11	5-27
	22	5-27
	22	4-228
	00	11-46
	00	5-180
	22	14-198
	00	15-29
	11	3-106
	11	4-228
	44	10-87
	00	5-27
	00	16-60
	00	4-228
	33	4-228
	00	15-29
	00	5-180
	11	10-87
	11	11-46

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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

@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, & 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.

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