Datasets:
File size: 2,992 Bytes
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-nc-4.0
task_categories:
- image-text-to-text
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: images
list:
image:
decode: false
- 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: 2118998617
num_examples: 4342
download_size: 2118414692
dataset_size: 2118998617
---
# 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 AgML datasets concatenate cleanly: `id`, `file_names` (one image per row), `messages`, `origin_dataset`, and `raw_metadata`. `raw_metadata` is a JSON-encoded string holding source fields not folded into `messages`/`file_names` (here: the original `source_image` filename and parsed `crop_prefix`); restore it with `json.loads(row["raw_metadata"])`. Image placeholders in `messages` align 1:1 with `file_names`. 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
``` |