AgriTaxon: Can Large Multimodal Models Identify What They See in Agriculture?

Xin Zeng, Benfeng Xu, Shancheng Fang, Huarui Wu

🌐 Project Page · 💻 GitHub · 📎 Supplementary Material

Overview

AgriTaxon is the first benchmark for open-ended taxonomic naming in agriculture. It tests whether Large Multimodal Models (LMMs) can correctly name a species from its image—without predefined options.

The benchmark spans four agricultural domains:

Track	Species	Source
🌿 Crops	1,971	FAO Ecocrop (via Wikidata P4753)
🐄 Livestock	178	FAO DAD-IS (via Wikidata P3380)
🐛 Pests	3,485	EPPO (via Wikidata P3031)
🌾 Weeds	1,798	EPPO (via Wikidata P3031)
Total	7,432

Every label is linked to authoritative FAO and EPPO databases via Wikidata, forming a traceable authority chain.

Key Features

• 7,432 species across four agricultural domains with entity-level species/breed labels
• Authority-grounded labels: every entity linked to FAO/EPPO via Wikidata QIDs
• Two evaluation protocols: multiple-choice (with semantically hard negatives) and open-ended naming
• LLM-as-a-Judge scoring with 98% expert agreement for alias validation
• AgriTaxon-Hard: 1,052-sample diagnostic subset where ≤2 of 14 frontier models answer correctly
• Reveals a striking seeing-without-naming gap: best model reaches 83% on multiple-choice but drops to 33% on open-ended naming

Dataset Structure

├── images/
│   ├── crop/          (1,971 images)
│   ├── livestock/     (178 images)
│   ├── pest/          (3,485 images)
│   └── weed/          (1,798 images)
├── annotations/
│   ├── {track}.jsonl          (open-ended: qid, label, image, track, source, wiki, KB_ID)
│   └── {track}_mc.jsonl       (multiple-choice: includes distractor options)
├── splits/
│   ├── hard.json              (AgriTaxon-Hard: 1,052 QIDs + selection criteria)
│   └── hard_model_accuracy.json
└── metadata.json

Annotation Fields

Each {track}.jsonl entry contains:

Field	Type	Description
qid	string	Wikidata QID (e.g., Q12345)
label	string	Canonical species name
image	string	Relative path to image file
track	string	One of: crop, livestock, pest, weed
source	string	Wikidata property used for sourcing
enwiki	string	English Wikipedia article title (optional)
zhwiki	string	Chinese Wikipedia article title (optional)
ecocropID / faoID / eppoCode	string	Authority database identifier (track-dependent)

Each {track}_mc.jsonl additionally includes distractor options for multiple-choice evaluation.

Image Sources

All images are sourced from Wikimedia Commons under Creative Commons licenses (predominantly CC BY and CC BY-SA). Each image retains its original license. Images with a shorter dimension below 224px have been filtered out; images with max dimension above 4096px have been resized.

Usage

# Download the dataset
pip install huggingface_hub
huggingface-cli download Xin1818/AgriTaxon --repo-type dataset --local-dir AgriTaxon

import json

# Load open-ended annotations for a track
samples = []
with open("AgriTaxon/annotations/crop.jsonl", "r") as f:
    for line in f:
        samples.append(json.loads(line))

print(f"Loaded {len(samples)} crop species")
print(samples[0])

# Load multiple-choice annotations
mc_samples = []
with open("AgriTaxon/annotations/crop_mc.jsonl", "r") as f:
    for line in f:
        mc_samples.append(json.loads(line))

# Load AgriTaxon-Hard split
with open("AgriTaxon/splits/hard.json", "r") as f:
    hard = json.load(f)
print(f"AgriTaxon-Hard: {len(hard)} samples")

Evaluation Protocols

Multiple-Choice

Each question presents 4 options: 1 correct answer + 3 semantically hard negatives generated from text embeddings of taxonomically close species.

Open-Ended Naming

Models must produce the species name from scratch without any options. Evaluated with:

• Exact Match (EM): after normalization (lowercasing, punctuation removal)
• Acc (LLM-as-a-Judge): GPT-5 Mini validates whether predictions that fail EM are valid aliases (common names, taxonomic synonyms, spelling variants), achieving 98% expert agreement

Main Results

Accuracy (%) on AgriTaxon, ranked by open-ended Acc.

Model	MC Mean	OE EM	OE Acc	Hard
gemini-3-pro-preview	82.5	44.0	51.2	9.0
doubao-seed-2-0-pro	79.4	44.1	48.6	6.0
gemini-3-flash-preview	83.0	33.4	48.1	8.7
doubao-seed-2-0-lite	77.6	37.6	44.0	5.6
kimi-k2.5	73.7	30.0	38.0	2.1
gpt-5	78.6	29.6	37.6	8.4
glm-4.6v	65.1	22.7	30.1	6.2
qwen3-vl-235b-a22b	68.4	21.7	27.5	2.2
gpt-5-mini	71.6	22.0	27.4	7.7
qwen3.5-397b-a17b	71.9	21.7	26.8	9.2
qwen3-vl-30b-a3b	59.9	16.0	22.5	2.2
glm-4.6v-flashx	60.3	15.2	19.6	5.8
qwen3.5-35b-a3b	67.8	11.3	17.4	4.2
claude-haiku-4-5	60.4	11.2	14.7	4.7

Licensing

• Benchmark metadata & annotations: CC BY 4.0
• Images: sourced from Wikimedia Commons under their respective Creative Commons licenses (predominantly CC BY and CC BY-SA)
• Source code & prompts: MIT License
• Authority identifiers: Wikidata QIDs under CC0; FAO and EPPO identifiers used for reference linking only