Datasets:
File size: 1,506 Bytes
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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 |