Datasets:
metadata
license: mit
language:
- en
tags:
- agriculture
- crop-disease
- wheat
- rice
- computer-vision
- plant-pathology
pretty_name: AgriDrone Crop Disease Dataset
size_categories:
- 10K<n<100K
AgriDrone — Crop Disease Detection Dataset
Full dataset collection used for training the AgriDrone crop-disease detection system (21-class YOLOv8n-cls classifier, 15 wheat + 6 rice diseases). 75,010 images, 24 folders, 23.6 GB.
Code repo: https://github.com/Ashut0sh-mishra/agri-drone
Folder layout
Training splits (5 folders — used to train the model)
| Folder | Files | Purpose |
|---|---|---|
train/ |
10,144 | Main training split |
val/ |
1,655 | Validation split |
test/ |
1,655 | Test split (used for the Config A/B/C ablation) |
train_orig/ |
4,987 | Original pre-augmentation training set |
val_orig/ |
1,247 | Original pre-augmentation validation set |
Wheat disease classes (11 folders)
| Folder | Files |
|---|---|
wheat_aphid/ |
300 |
wheat_blast/ |
300 |
wheat_healthy/ |
600 |
wheat_leaf_rust/ |
300 |
wheat_smut/ |
300 |
wheat_yellow_rust/ |
300 |
fusarium_head_blight/ |
300 |
leaf_blight/ |
300 |
powdery_mildew/ |
300 |
septoria/ |
300 |
tan_spot/ |
300 |
Rice disease datasets (3 folders)
| Folder | Files | Source |
|---|---|---|
Rice_Leaf_AUG/ |
3,829 | Augmented rice leaf set |
rice-diseases-v2/ |
10,346 | Roboflow rice disease v2 |
rice-diseases-zoa8l/ |
2,589 | Roboflow rice disease (zoa8l) |
External benchmarks (3 folders)
| Folder | Files | Description |
|---|---|---|
PDT dataset/ |
19,049 | Plant Disease Treatment dataset |
plantdoc/ |
196 | Original PlantDoc benchmark |
plantdoc-v3/ |
1,552 | PlantDoc v3 |
Raw detection set (1 folder)
| Folder | Files | Description |
|---|---|---|
data/ |
14,154 | Roboflow-sourced wheat detection dataset (bounding boxes) |
Usage
Python — download a subset
from huggingface_hub import snapshot_download
# Training splits only (enough to reproduce the 21-class model)
path = snapshot_download(
repo_id="ashu010/agridrone-data",
repo_type="dataset",
allow_patterns=["train/**", "val/**", "test/**"],
)
CLI — use the fetch script
From the GitHub repo:
python scripts/fetch_data.py --preset training # train/val/test splits
python scripts/fetch_data.py --preset wheat # all 11 wheat classes
python scripts/fetch_data.py --preset rice # 3 rice datasets
python scripts/fetch_data.py --preset external # PlantDoc + PDT
python scripts/fetch_data.py # everything (25 GB)
Direct image URL (for dashboards / web apps)
Every image has a public resolve URL — no auth needed:
https://huggingface.co/datasets/ashu010/agridrone-data/resolve/main/<folder>/<filename>
Example:
<img src="https://huggingface.co/datasets/ashu010/agridrone-data/resolve/main/wheat_aphid/wheat_aphid_0001.jpg">
Use these URLs directly in the AgriDrone frontend dashboard for live data display.
License
MIT for the curation layer. Individual source datasets retain their original licenses — see each folder for upstream attribution.
Citation
See the AgriDrone repo: https://github.com/Ashut0sh-mishra/agri-drone