Datasets:

Project-AgML
/

LeafBench

---
pretty_name: LeafBench
license: mit
task_categories:
- image-text-to-text
- visual-question-answering
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: train-0000-of-0001.parquet
dataset_info:
  features:
  - name: images
    list:
    - name: bytes
      dtype: 'null'
    - name: path
      dtype: string
  - name: id
    dtype: string
  - name: messages
    list:
    - name: role
      dtype: string
    - name: content
      list:
      - name: type
        dtype: string
      - name: text
        dtype: string
  - name: raw_metadata
    dtype: string
  splits:
  - name: train
    num_examples: 13950
tags:
- agriculture
- vision-language
- vqa
- agvqa
- leafnet
- leafbench
language:
- en
---
# LeafBench

LeafBench is a multiple-choice visual question answering benchmark for testing how well vision-language models understand plant diseases. It was built from LeafNet, a dataset of about 186,000 leaf images from 22 crop species in 97 classes, collected in 7 countries. Every question asks about the leaf in the image and gives a closed set of answers, so the model must pick one instead of writing free text.

The questions cover 6 tasks, from telling healthy and diseased leaves apart to naming the disease, the crop, the pathogen type, the pathogen's scientific name and the main symptom. The authors tested 12 vision-language models zero-shot. Healthy or diseased questions were easy, with the best models above 90%, but pathogen and scientific name questions stayed below 65% even for frontier models. On the dataset card, GPT-4o averages 72.78%, SCOLD (the authors' own model trained on LeafNet) 72.38% and Gemini 2.5 Pro 69.18%.

- Paper: [LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases](https://arxiv.org/abs/2602.13662)
- Original dataset: [enalis/LeafBench](https://huggingface.co/datasets/enalis/LeafBench)
- Image and caption dataset: [Project-AgML/LeafNet](https://huggingface.co/datasets/Project-AgML/LeafNet)
- Evaluation code: [EnalisUs/LeafBench](https://github.com/EnalisUs/LeafBench)
- License: the original dataset card is not consistent. Its metadata says `mit` and its text says CC BY 4.0. Access is gated, and users must agree to use the data for non-commercial purposes only (academic research or education).

## Tasks

| Task | `question_type` | Rows |
|---|---|---|
| Healthy-Diseased Classification | `HDC` | 2,580 |
| Disease Identification | `DC` | 2,580 |
| Crop Species Identification | `CSI` | 2,580 |
| Scientific Name Classification | `SNC` | 2,070 |
| Pathogen Classification | `PC` | 2,070 |
| Symptom Identification | `SI` | 2,070 |

The dataset card calls Disease Identification `DI`, but the data uses `DC`. Healthy-Diseased questions have two options (Yes or No). All other questions have four.

## Structure

```
train-0000-of-0001.parquet
images/
  image-train-000-of-001.zip
  path_to_shard.parquet
```

Each row has:

- `images`: list with one `{"bytes": null, "path": <sha256>}`. The path is the SHA256 of the image bytes and points into a zip shard through `images/path_to_shard.parquet`.
- `id`: `leafbench_train_<counter>`, for example `leafbench_train_0`.
- `messages`: one user turn (image, then question with options) and one assistant turn (answer).
- `raw_metadata`: JSON string with `question_id`, `question_type` and `raw_image_paths`.

Example:

```json
{
  "images": [{"bytes": null, "path": "37a342d681b968f2df2c18298560f39059aeed3fd6beae378addab30fd0076cd"}],
  "id": "leafbench_train_0",
  "messages": [
    {"role": "user", "content": [
      {"type": "image", "text": null},
      {"type": "text", "text": "Does the image show a diseased plant?\nA. Yes\nB. No"}
    ]},
    {"role": "assistant", "content": [{"type": "text", "text": "A. Yes"}]}
  ],
  "raw_metadata": "{\"question_id\": \"hdc::1\", \"question_type\": \"HDC\", \"raw_image_paths\": [\"f3ff804e-bca4-4a23-9af9-1f5b8c5c9980___JR_FrgE.S 3053.JPG\"]}"
}
```

### Changes from the original data

- Built from the 6 parquet files in `data/`. All 13,950 rows are kept in the same order. The source has one `test` split, which is stored here as `train`.
- Options come from the `A` to `D` columns and are added to the question as lines like `A. text`. Missing options (C and D in Healthy-Diseased questions) are left out. All text is kept exactly as written, including a leading space in the Scientific Name question and extra spaces in some options.
- Answers combine the answer letter and its option text, for example `A. Yes`.
- `raw_metadata` keeps `question_id` and `question_type`. The image's own path from the source parquet is kept in `raw_image_paths`. The question, options and answer are already in `messages`.
- The images were stored inside the source parquet files, once per question. They are moved into a zip shard and stored once, deduplicated by SHA256. The rows name 2,506 different image files, which hold 2,535 unique images, because some different images share a file name. The dataset card says 2,910 images and the paper says 2,570.
- The tiny subset in `leafbench_tiny.csv` (880 questions) is not included. All of its questions are also in the full benchmark.

## Usage

```python
from agml import loadImageTextToTextDataset
dataset = loadImageTextToTextDataset("Project-AgML/LeafBench")
sample = dataset["train"][0]
sample["images"]    # list of PIL images
sample["messages"]
```

## Citation

```bibtex
@article{quoc2026leafnet,
  title={LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases},
  author={Quoc, Khang Nguyen and Dao, Phuong D and Quach, Luyl-Da},
  journal={arXiv preprint arXiv:2602.13662},
  year={2026}
}
```

This dataset is indexed and structured on https://project-agml.github.io/ as part of Project AgML @ UC Davis. 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.

	---
	pretty_name: LeafBench
	license: mit
	task_categories:
	- image-text-to-text
	- visual-question-answering
	size_categories:
	- 10K<n<100K
	configs:
	- config_name: default
	data_files:
	- split: train
	path: train-0000-of-0001.parquet
	dataset_info:
	features:
	- name: images
	list:
	- name: bytes
	dtype: 'null'
	- name: path
	dtype: string
	- name: id
	dtype: string
	- name: messages
	list:
	- name: role
	dtype: string
	- name: content
	list:
	- name: type
	dtype: string
	- name: text
	dtype: string
	- name: raw_metadata
	dtype: string
	splits:
	- name: train
	num_examples: 13950
	tags:
	- agriculture
	- vision-language
	- vqa
	- agvqa
	- leafnet
	- leafbench
	language:
	- en
	---

	# LeafBench

	LeafBench is a multiple-choice visual question answering benchmark for testing how well vision-language models understand plant diseases. It was built from LeafNet, a dataset of about 186,000 leaf images from 22 crop species in 97 classes, collected in 7 countries. Every question asks about the leaf in the image and gives a closed set of answers, so the model must pick one instead of writing free text.

	The questions cover 6 tasks, from telling healthy and diseased leaves apart to naming the disease, the crop, the pathogen type, the pathogen's scientific name and the main symptom. The authors tested 12 vision-language models zero-shot. Healthy or diseased questions were easy, with the best models above 90%, but pathogen and scientific name questions stayed below 65% even for frontier models. On the dataset card, GPT-4o averages 72.78%, SCOLD (the authors' own model trained on LeafNet) 72.38% and Gemini 2.5 Pro 69.18%.

	- Paper: [LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases](https://arxiv.org/abs/2602.13662)
	- Original dataset: [enalis/LeafBench](https://huggingface.co/datasets/enalis/LeafBench)
	- Image and caption dataset: [Project-AgML/LeafNet](https://huggingface.co/datasets/Project-AgML/LeafNet)
	- Evaluation code: [EnalisUs/LeafBench](https://github.com/EnalisUs/LeafBench)
	- License: the original dataset card is not consistent. Its metadata says `mit` and its text says CC BY 4.0. Access is gated, and users must agree to use the data for non-commercial purposes only (academic research or education).

	## Tasks

	\| Task \| `question_type` \| Rows \|
	\|---\|---\|---\|
	\| Healthy-Diseased Classification \| `HDC` \| 2,580 \|
	\| Disease Identification \| `DC` \| 2,580 \|
	\| Crop Species Identification \| `CSI` \| 2,580 \|
	\| Scientific Name Classification \| `SNC` \| 2,070 \|
	\| Pathogen Classification \| `PC` \| 2,070 \|
	\| Symptom Identification \| `SI` \| 2,070 \|

	The dataset card calls Disease Identification `DI`, but the data uses `DC`. Healthy-Diseased questions have two options (Yes or No). All other questions have four.

	## Structure

	```
	train-0000-of-0001.parquet
	images/
	image-train-000-of-001.zip
	path_to_shard.parquet
	```

	Each row has:

	- `images`: list with one `{"bytes": null, "path": <sha256>}`. The path is the SHA256 of the image bytes and points into a zip shard through `images/path_to_shard.parquet`.
	- `id`: `leafbench_train_<counter>`, for example `leafbench_train_0`.
	- `messages`: one user turn (image, then question with options) and one assistant turn (answer).
	- `raw_metadata`: JSON string with `question_id`, `question_type` and `raw_image_paths`.

	Example:

	```json
	{
	"images": [{"bytes": null, "path": "37a342d681b968f2df2c18298560f39059aeed3fd6beae378addab30fd0076cd"}],
	"id": "leafbench_train_0",
	"messages": [
	{"role": "user", "content": [
	{"type": "image", "text": null},
	{"type": "text", "text": "Does the image show a diseased plant?\nA. Yes\nB. No"}
	]},
	{"role": "assistant", "content": [{"type": "text", "text": "A. Yes"}]}
	],
	"raw_metadata": "{\"question_id\": \"hdc::1\", \"question_type\": \"HDC\", \"raw_image_paths\": [\"f3ff804e-bca4-4a23-9af9-1f5b8c5c9980___JR_FrgE.S 3053.JPG\"]}"
	}
	```

	### Changes from the original data

	- Built from the 6 parquet files in `data/`. All 13,950 rows are kept in the same order. The source has one `test` split, which is stored here as `train`.
	- Options come from the `A` to `D` columns and are added to the question as lines like `A. text`. Missing options (C and D in Healthy-Diseased questions) are left out. All text is kept exactly as written, including a leading space in the Scientific Name question and extra spaces in some options.
	- Answers combine the answer letter and its option text, for example `A. Yes`.
	- `raw_metadata` keeps `question_id` and `question_type`. The image's own path from the source parquet is kept in `raw_image_paths`. The question, options and answer are already in `messages`.
	- The images were stored inside the source parquet files, once per question. They are moved into a zip shard and stored once, deduplicated by SHA256. The rows name 2,506 different image files, which hold 2,535 unique images, because some different images share a file name. The dataset card says 2,910 images and the paper says 2,570.
	- The tiny subset in `leafbench_tiny.csv` (880 questions) is not included. All of its questions are also in the full benchmark.

	## Usage

	```python
	from agml import loadImageTextToTextDataset

	dataset = loadImageTextToTextDataset("Project-AgML/LeafBench")

	sample = dataset["train"][0]
	sample["images"] # list of PIL images
	sample["messages"]
	```

	## Citation

	```bibtex
	@article{quoc2026leafnet,
	title={LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases},
	author={Quoc, Khang Nguyen and Dao, Phuong D and Quach, Luyl-Da},
	journal={arXiv preprint arXiv:2602.13662},
	year={2026}
	}
	```

	This dataset is indexed and structured on https://project-agml.github.io/ as part of Project AgML @ UC Davis. 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.