Datasets:
File size: 3,377 Bytes
b560e35 1c84a7c b560e35 1c84a7c b560e35 1c84a7c b560e35 1c84a7c b560e35 1c84a7c b560e35 1c84a7c b560e35 1c84a7c b560e35 1c84a7c b560e35 1c84a7c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | ---
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](https://github.com/pratikkayal/PlantDoc-Dataset) 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
```python
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:
```bash
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:
```html
<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>
|