---
license: mit
task_categories:
- image-classification
- image-to-text
language:
- en
tags:
- agriculture
- plant-disease
- crop-disease
- nepal
- sharegpt
- multimodal
pretty_name: Nepali Crop Disease Dataset
size_categories:
- 10K<n<100K
---

# Nepali Crop Disease Dataset

A consolidated image dataset for crop identification and plant disease
detection, merged from multiple public sources and organized by crop, with
an accompanying **ShareGPT-format** conversational dataset for
vision-language model finetuning.

## Dataset Summary

Images are grouped **one folder per crop** (e.g. `tomato/`, `apple/`,
`maize/`), each labeled with both a **crop** and a **disease** (or
`healthy`). The corpus is **class-balanced by design**:

- Crops with fewer than 300 images were dropped entirely (9 crops kept out
  of 17 detected in the source pool).
- Every kept crop is capped at the **same per-class quota** (equal counts
  across crops — no crop dominates the dataset).
- Within each crop, images are sampled **round-robin across disease
  labels**, so a single disease can't dominate that crop's folder.
- Final split per crop: **4,443 train / 666 val / 666 test**, for
  **47,489 images total** across 9 crops.

Labels come from two sources, tracked per-image via a `label_source` field:

- `metadata` — parsed from existing `.jsonl`/`.csv`/`.json` annotation files
  shipped with a source dataset (higher precision).
- `foldername` — inferred by keyword-matching the original folder/file path
  when no structured metadata was available.

## Source Datasets

This dataset merges images from:

| Source | Link |
|---|---|
| Kaggle — Crop Disease Detection Dataset | https://www.kaggle.com/datasets/snikhilrao/crop-disease-detection-dataset |
| GitHub — PlantDoc-Dataset | https://github.com/pratikkayal/PlantDoc-Dataset |
| Hugging Face — bd-crop-vegetable-plant-disease-dataset | https://huggingface.co/datasets/Saon110/bd-crop-vegetable-plant-disease-dataset |

> Mendeley Data (`6243z8r6t6`) was originally scoped for inclusion but was
> dropped from this build.

**Please refer to each original source for its own license and citation
requirements** — this dataset does not override or supersede those terms.
If you are a maintainer of one of the source datasets and have concerns
about inclusion or attribution, please open a discussion on this repo.

## Dataset Structure

```
train/<crop>/*.jpg
val/<crop>/*.jpg
test/<crop>/*.jpg
labels_registry.jsonl
sharegpt.jsonl
zips/dataset_part_*.zip   # full archive, for bulk download
```

### `labels_registry.jsonl`

One row per image:

```json
{"image": "train/tomato/abc123.jpg", "split": "train", "crop": "tomato", "disease": "early blight", "label_source": "metadata"}
```

### `sharegpt.jsonl`

One conversation per image, formatted for VLM instruction finetuning:

```json
{
  "id": "0000001",
  "image": "train/tomato/abc123.jpg",
  "conversations": [
    {"from": "human", "value": "<image>\nWhat crop is shown in this image, and does it have any disease? If so, name the disease."},
    {"from": "gpt", "value": "This is a Tomato leaf affected by early blight."}
  ]
}
```

Healthy images get an explicit no-disease answer; images where the disease
label couldn't be confidently determined are marked as such rather than
guessed.

## How to Load

### As an image folder (classification)

```python
from datasets import load_dataset

ds = load_dataset(
    "imagefolder",
    data_dir="train",  # or point at an extracted local copy
)
```

### As ShareGPT conversations (VLM finetuning)

```python
from datasets import load_dataset

ds = load_dataset(
    "json",
    data_files="sharegpt.jsonl",
)
```

Note: `image` paths in `sharegpt.jsonl` are relative to the repo root
(e.g. `train/tomato/abc123.jpg`) — resolve them against your local copy of
the `train/val/test` folders (or the extracted `zips/` archive) before
loading images.

### From the zipped archive

```python
from huggingface_hub import hf_hub_download
import zipfile

local_zip = hf_hub_download(
    repo_id="w4ashabii/nepali_crop_data",
    repo_type="dataset",
    filename="zips/dataset_part_000.zip",
)
with zipfile.ZipFile(local_zip) as zf:
    zf.extractall("training_data")
```

## Known Limitations

- Crop and disease labels are extracted via keyword matching where source
  metadata wasn't available (`label_source: "foldername"`) — these may
  contain classification errors and should be spot-checked before use in
  high-stakes applications.
- Crops with fewer than 300 available images were dropped to keep the
  balancing meaningful — this dataset does **not** cover every crop present
  in the original source datasets, only the 9 that had enough images.
- Balancing is enforced at the **crop** level (equal images per crop) and
  approximately at the **disease** level within each crop (round-robin
  sampling) — it is not a strict guarantee of equal counts per individual
  disease across the whole dataset. Check `labels_registry.jsonl` if you
  need exact per-disease counts.
- Image quality, resolution, and photographic conditions vary across the
  source datasets.

## Citation

If you use this dataset, please cite the original source datasets listed
above in addition to this repository.