AgriBench

AgriBench is the first benchmark built to evaluate multimodal LLMs on agriculture tasks. This repo is its MM-LUCAS subset: built on top of the EU's LUCAS (Land Use/Cover Area frame Survey) land-cover survey, it pairs 1,784 landscape photos (1600×1200, taken across 27 EU countries) with microdata the survey already recorded — GPS location, country, capture date, land-cover and land-use taxonomy codes, and quality/aesthetic ratings.

Paper: AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models (arXiv:2412.00465, ECCV 2024 Workshops) Authors: Yutong Zhou, Masahiro Ryo Original repository: github.com/Yutong-Zhou-cv/AgriBench

Underlying LUCAS survey data:

• Semantic segmentation dataset of Land Use/Cover Area frame Survey (LUCAS) rural landscape Street View Images — Martinez-Sanchez, Hufkens, Kearsley, Naydenov, Czucz, van de Velde (Data in Brief, 2024)
• Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union — d'Andrimont et al. (Scientific Data, 2020)

About the paper

AgriBench turns the LUCAS microdata into four VQA tasks over the same 1,784 images:

• Land cover — 4-option multiple choice over LUCAS's land-cover classes (e.g. "Shrubland with sparse tree cover", "Broadleaved woodland").
• Land use — 4-option multiple choice over LUCAS's socio-economic land-use classes (e.g. "Kitchen garden", "Semi-natural and natural areas not in use").
• Aesthetic score — a 5-point qualitative rating of the photo itself (Bad / Poor / Fair / Good / Excellent).
• Quality score — a 5-point qualitative rating of image/capture quality (Bad / Poor / Fair / Good / Excellent).

License: CC BY 4.0.

Dataset Size

Config	Rows	Unique Images	Image Data
train	7,136	1,784	0.92GB (1 shard)

All four tasks share the exact same 1,784 images, so every image contributes 4 rows (one per task) rather than being split into separate configs.

Task Breakdown

Task	Mechanism	Rows
aesthetics_score	open-ended rating (Bad/Poor/Fair/Good/Excellent)	1,784
quality_score	open-ended rating (Bad/Poor/Fair/Good/Excellent)	1,784
land_cover	multiple-choice (4 options)	1,784
land_use	multiple-choice (4 options)	1,784

For the multiple-choice tasks, the assistant turn combines the answer key with its option text, e.g. "D. Broadleaved woodland", rather than the bare letter the source JSON gives. For the rating tasks, the source answer is already a plain-text label (e.g. "Poor"), used as-is.

Layout

train-0000-of-0001.parquet          # 7,136 rows
images/
  image-shared-000-of-001.zip       # image shard (ZIP_STORED, uncompressed)
  path_to_shard.parquet             # maps image SHA256 hash -> shard_file

Images are content-addressed by SHA256 hash and stored uncompressed in zip shards for random access via zipfile, capped at 5GB per shard (this dataset only needed one).

Schema

Column	Type	Description
images	list<struct<bytes, path>>	bytes is null; path is the SHA256 hash, looked up in images/path_to_shard.parquet
id	string	mmlucas_<task>_<n> (names the MM-LUCAS subset this repo standardizes)
messages	chat-style list	user turn: image + question (multiple-choice questions include lettered options inline); assistant turn: the answer, letter-prefixed for multiple-choice questions
raw_metadata	JSON string	task, image_path (original source path), gps_long, gps_lat, date, nuts0 (EU country code), Classes (free-text scene description); plus the task-specific field: Aesthetic Score, Quality Score, lc1/lc1_label, or lu1/lu1_label

Each row is one (image, task) pair, so every image appears across 4 rows.

Usage

Recommended, via the AgML python library:

from agml import loadImageTextToTextDataset

ds = loadImageTextToTextDataset("AgriBench")

Citations

@article{zhou2024agribench,
  title={AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models},
  author={Zhou, Yutong and Ryo, Masahiro},
  journal={arXiv preprint arXiv:2412.00465},
  year={2024}
}

@article{martinez2024semantic,
  title={Semantic segmentation dataset of Land Use/Cover Area frame Survey (LUCAS) rural landscape Street View Images},
  author={Martinez-Sanchez, Laura and Hufkens, Koen and Kearsley, Elizabeth and Naydenov, Dimitar and Cz{\'u}cz, B{\'a}lint and van de Velde, Marijn},
  journal={Data in Brief},
  volume={54},
  pages={110394},
  year={2024},
  publisher={Elsevier}
}

@article{d2020harmonised,
  title={Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union},
  author={d’Andrimont, Rapha{\"e}l and Yordanov, Momchil and Martinez-Sanchez, Laura and Eiselt, Beatrice and Palmieri, Alessandra and Dominici, Paolo and Gallego, Javier and Reuter, Hannes Isaak and Joebges, Christian and Lemoine, Guido and others},
  journal={Scientific data},
  volume={7},
  number={1},
  pages={352},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

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This dataset is indexed and structured on https://project-agml.github.io/ as part of the AgML python library. This dataset was reformatted from its original format to match HuggingFace's Imagefolder standards but requires an external module (agml) that processes and returns a HF Dataset object faster than HF module functions.