File size: 2,485 Bytes
b70c854 77718e6 b70c854 77718e6 b70c854 77718e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | ---
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
``` |