Datasets:

Project-AgML
/

PlantExpertVQA

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

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

@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.