---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-nc-4.0
task_categories:
- image-text-to-text
language:
- en
size_categories:
- 100K<n<1M
dataset_info:
- config_name: default
  features:
  - name: images
    list:
      image:
        decode: true
  - name: id
    dtype: string
  - name: messages
    list:
    - name: role
      dtype: string
    - name: content
      list:
      - name: type
        dtype: string
      - name: text
        dtype: string
  - name: origin_dataset
    dtype: string
  - name: raw_metadata
    dtype: string
  splits:
  - name: train
    num_examples: 145261
---

# CDDM (Crop Disease Domain Multimodal) Dataset

CDDM is a large-scale multimodal benchmark dataset built to advance vision-language
models for crop disease diagnosis. It pairs 137,000 crop disease images with over 1
million instruction-following question-answer conversations covering disease
identification, causes, symptoms, and prevention/treatment strategies.

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python
library. Standardized to the HF `image_text_to_text` format with a single conversational
`messages` schema, converted to **Parquet** with image bytes embedded directly.

## Dataset Construction

The image data was compiled from two sources:

| Source | Images | Description |
|---|---|---|
| Web Data | 62,000 | Public agricultural datasets (Kaggle) plus web-crawled disease images |
| Private Data | 75,000 | Original images collected via field surveys across multiple farms and orchards |

All images were annotated by agricultural experts with crop category, disease category,
and appearance description. The dataset spans **16 crop categories** and **60 crop disease categories**; 
48 categories contain 500+ images each, with the remaining 7 containing 200–500 images.

Two instruction-following data types were generated using GPT-4 prompting:
- **Crop Disease Diagnosis QA** — over 1 million multi-turn QA pairs per image, covering
  crop/disease identification, including deliberately-crafted negative-answer questions
  to counter models' tendency toward false-positive diagnoses. Avg. question length: 6.11
  words; avg. answer length: 8.92 words.
- **Crop Disease Knowledge QA** — QA pairs generated from expert-curated disease
  knowledge text (symptoms, pathogen characteristics, transmission, prevention/control).
  Avg. question length: 9.69 words; avg. answer length: 130.41 words.

A held-out test set of 3,000 images (not included in training data) was used by the
original authors for benchmark evaluation.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("Project-AgML/CDDM")
first = ds["train"][0]

# Access an image — decoded to PIL automatically
img = first["images"][0]
img.show()
```

## Schema

Every record shares the SAME columns so heterogeneous AgML datasets concatenate cleanly:
`id`, `images` (embedded image bytes), `messages`, `origin_dataset`, and `raw_metadata`.

`raw_metadata` is a JSON-encoded string holding source fields not folded into `messages`
(here: `file_names` pointing to the original image path, and annotated `crop_category` /
`disease_category` where available); restore it with `json.loads(row["raw_metadata"])`.
Image placeholders in `messages` align 1:1 with the `images` column.

## Citation

```bibtex
@inproceedings{liu2024cddm,
      title={A Multimodal Benchmark Dataset and Model for Crop Disease Diagnosis},
      author={Liu, Xiang and Liu, Zhaoxiang and Hu, Huan and Chen, Zezhou and Wang, Kohou and Wang, Kai and Lian, Shiguo},
      booktitle={Computer Vision -- ECCV 2024},
      pages={157--170},
      year={2025},
      publisher={Springer Nature Switzerland},
      address={Cham},
      isbn={978-3-031-73016-0},
      doi={10.1007/978-3-031-73016-0_10}
}

Liu, Xiang; Liu, Zhaoxiang; Hu, Huan; Chen, Zezhou; Wang, Kohou; Wang, Kai; Lian, Shiguo (2025), "A Multimodal Benchmark Dataset and Model for Crop Disease Diagnosis", ECCV 2024, pp. 157-170
```

## License

Released by the original authors (China Unicom AI Innovation Center) as an open-source
initiative for agricultural multimodal research. Original source and download instructions:
https://github.com/UnicomAI/UnicomBenchmark/tree/main/CDDMBench. This license
information is for reference only and does not constitute legal advice — refer to the
original repository for the authoritative license terms.