| --- |
| 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. |
