| --- |
| license: cc-by-nc-4.0 |
| configs: |
| - config_name: train |
| data_files: |
| - split: train |
| path: "data/train/*.parquet" |
| - config_name: test |
| data_files: |
| - split: test |
| path: "data/test/*.parquet" |
| - config_name: validation |
| data_files: |
| - split: validation |
| path: "data/val/*.parquet" |
| - config_name: default |
| data_files: |
| - split: train |
| path: "data/train/*.parquet" |
| - split: test |
| path: "data/test/*.parquet" |
| - split: validation |
| path: "data/val/*.parquet" |
| task_categories: |
| - image-text-to-text |
| language: |
| - en |
| size_categories: |
| - 100K<n<1M |
| dataset_info: |
| - config_name: train |
| 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_bytes: 47646918572 |
| num_examples: 535881 |
| - config_name: test |
| 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: test |
| num_bytes: 13564137567 |
| num_examples: 152921 |
| - config_name: validation |
| 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: validation |
| num_bytes: 6913653907 |
| num_examples: 76384 |
| - 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_bytes: 47646918572 |
| num_examples: 535881 |
| - name: test |
| num_bytes: 13564137567 |
| num_examples: 152921 |
| - name: validation |
| num_bytes: 6913653907 |
| num_examples: 76384 |
| download_size: 67867722531 |
| dataset_size: 68124710046 |
| --- |
| |
| # PlantExpertVQA |
|
|
| PlantExpertVQA is a large-scale visual question answering (VQA) dataset built to advance |
| vision-language models for agricultural decision-making and interactive plant disease |
| diagnosis. It is compiled from **45 open-source datasets** (including the widely-used |
| PlantVillage corpus) and comprises **765,186 expert-verified question-answer pairs** |
| grounded over **150,841 images**, spanning **38 crop species** and **89 disease |
| conditions**. |
|
|
| 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 |
|
|
| Questions are organized into **9 distinct categories** spanning **3 levels of cognitive |
| complexity**, generated via a two-stage pipeline (template-based QA synthesis from image |
| metadata, followed by multi-stage linguistic re-engineering) and iteratively validated by |
| domain botanists for scientific accuracy. |
|
|
| | Cognitive Level | Question Categories | |
| |---|---| |
| | **Level 1 — Foundational Perception & Identification** | Existence & Sanity Check, Plant Species Identification, General Health Assessment | |
| | **Level 2 — Detailed Analysis & Verification** | Visual Attribute Grounding, Detailed Verification | |
| | **Level 3 — Higher-Order Reasoning & Inference** | Specific Disease Identification, Comprehensive Description, Causal Reasoning, Counterfactual Reasoning | |
|
|
| The dataset also underwent linguistic diversification (359% question vocabulary growth |
| via expert-validated paraphrasing), structural rebalancing of skewed binary-answer |
| categories, and a two-phase automated + expert quality review pipeline (vagueness scoring, |
| semantic dissonance detection, relative simplicity checks) to remove low-value or |
| non-verifiable QA pairs. |
|
|
| ## Splits |
|
|
| Each split is hosted as its **own independent config**, so downloading one split never |
| triggers a download of the others. |
|
|
| | Config | Split | Rows | Size | |
| |---|---|---|---| |
| | `train` | `train` | 535,881 | ~47.6 GB | |
| | `test` | `test` | 152,921 | ~13.6 GB | |
| | `validation` | `validation` | 76,384 | ~6.9 GB | |
| | `default` | all three | 765,186 | ~67.9 GB | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load ONLY the train split — isolated download, no test/validation fetched |
| ds = load_dataset("Project-AgML/PlantExpertVQA", "train") |
| |
| # Load ONLY the test split |
| ds = load_dataset("Project-AgML/PlantExpertVQA", "test") |
| |
| # Load ONLY the validation split |
| ds = load_dataset("Project-AgML/PlantExpertVQA", "validation") |
| |
| # Load everything (all three splits combined under one DatasetDict) |
| ds = load_dataset("Project-AgML/PlantExpertVQA") # equivalent to "default" |
| |
| first = ds["train"][0] if "train" in ds else ds[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, `qa_id`, `image_id`, `crop`, |
| `disease`, `category`, `severity`, `answer_type`, `question_category`, `cognitive_level`, |
| and `dataset_source` — identifying which of the 45 compiled source datasets each QA pair |
| originated from); restore it with `json.loads(row["raw_metadata"])`. Image placeholders in |
| `messages` align 1:1 with the `images` column. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{sakib2025plantexpertvqa, |
| title={PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science}, |
| author={Sakib, Syed Nazmus and Haque, Nafiul and Hossain, Mohammad Zabed and Arman, Shifat E.}, |
| year={2025}, |
| eprint={2508.17117}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2508.17117} |
| } |
| |
| Sakib, Syed Nazmus; Haque, Nafiul; Hossain, Mohammad Zabed; Arman, Shifat E. (2025), "PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science", arXiv:2508.17117 |
| ``` |
|
|
| ## License |
|
|
| Released under **CC BY-NC 4.0**. Compiled from 45 open-source datasets, each retaining |
| attribution to its original source per the authors' documentation. This license |
| information is for reference only and does not constitute legal advice — refer to the |
| original repository for authoritative license terms: |
| https://huggingface.co/datasets/SyedNazmusSakib/PlantExpertVQA. |
