| --- |
| 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> |
|
|
