Datasets:
File size: 1,625 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': diseased
'1': early
- name: source
dtype: string
- name: cultivar
dtype: string
splits:
- name: train
num_bytes: 2135684930
num_examples: 424
download_size: 2135737421
dataset_size: 2135684930
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- n<1K
---
# Wheat Mosaic Classification Multispectral
This dataset provides real multispectral images of wheat plants in field environments across South Africa, collected during August and October 2023. Captured using a specially adapted Canon EOS 800D DSLR, the images focus on early detection of Wheat Stripe Mosaic Virus, depicting plants at various stages of disease progression. The dataset contains 424 images across 2 classes: diseased, early.
Images per class:
- diseased: 252
- early: 172
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{desilva2025early,
title={Early detection of Wheat Stripe Mosaic Virus using multispectral imaging with deep-learning},
author={De Silva, Malithi and Brown, Dane},
journal={Ecological Informatics},
volume={87},
pages={103088},
year={2025},
publisher={Elsevier}
}
```
De Silva, Malithi (2025), “Wheat_dataset”, Mendeley Data, V1, doi: 10.17632/kgmcb5w5tk.1
*This dataset was reformatted from its original format to match HuggingFace standards.*
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