Datasets:
File size: 1,695 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Caterpillar_Semilooper_Pest
'1': Frog_Leaf_Eye
'2': Healthy
'3': Mosaic
'4': Rust
'5': Spectoria_Brown_Spot
splits:
- name: train
num_bytes: 6310902405
num_examples: 2782
download_size: 6127186815
dataset_size: 6310902405
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
---
# MH SoyaHealthVision Disease Classification Leaf
A dataset for disease classification of soybean leaves. The dataset contains 2,782 images across 6 classes: Caterpillar_Semilooper_Pest, Frog_Leaf_Eye, Healthy, Mosaic, Rust, Spectoria_Brown_Spot.
Images per class:
- Caterpillar_Semilooper_Pest: 582
- Frog_Leaf_Eye: 169
- Healthy: 204
- Mosaic: 707
- Rust: 852
- Spectoria_Brown_Spot: 268
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 |