---
configs:
  - config_name: default
    data_files:
      - "metadata.jsonl"
      - "images/**"
license: cc-by-nc-4.0
task_categories:
  - image-text-to-text
size_categories:
  - 1K<n<10K
---

# AgroBench

AgroBench is a vision-language model (VLM) benchmark for agriculture, annotated by expert agronomists. It covers seven agricultural topics spanning 203 crop categories and 682 disease categories, with 4,342 question-answer examples pairing images with multiple-choice questions across tasks such as crop identification, disease diagnosis, pest identification, and weed identification.

This dataset has been standardized to the HF `image_text_to_text` format: one conversational `messages` schema, imagefolder-native images, and (if present) a `text_only` parquet config.

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

## Usage

```python
from datasets import load_dataset

# Single image folder -> one default config
ds = load_dataset("Project-AgML/AgroBench")

# Stream without downloading
ds = load_dataset("Project-AgML/AgroBench", streaming=True)
```

Every record shares the SAME columns so heterogeneous datasets concatenate cleanly: id, file_names (1..N images), messages, origin_dataset, and raw_metadata. raw_metadata is a JSON-encoded string holding every original source field that was NOT already folded into messages/file_names (the question, answer, options, and image paths are omitted to avoid duplication), preserved verbatim, or {} if none remain; restore them with json.loads(row["raw_metadata"]). Using a JSON string (not a native struct) is what lets concatenate_datasets([...]) work across datasets whose raw fields differ in type. Multi-image rows return images as a list aligned to the {"type": "image"} placeholders in messages.

# Citation

```bibtex
@InProceedings{Shinoda_2025_ICCV,
  author    = {Shinoda, Risa and Inoue, Nakamasa and Kataoka, Hirokatsu and Onishi, Masaki and Ushiku, Yoshitaka},
  title     = {AgroBench: Vision-Language Model Benchmark in Agriculture},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month     = {October},
  year      = {2025},
  pages     = {7634-7644}
}

Shinoda, Risa; Inoue, Nakamasa; Kataoka, Hirokatsu; Onishi, Masaki; Ushiku, Yoshitaka (2025), "AgroBench: Vision-Language Model Benchmark in Agriculture", Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 7634-7644
```