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pretty_name: LeafNet
dataset_info:
- config_name: train
splits:
- name: train
num_examples: 121337
configs:
- config_name: train
data_files:
- split: train
path: train-*.parquet
license: cc-by-4.0
task_categories:
- image-text-to-text
- visual-question-answering
language:
- en
tags:
- vqa
- agriculture
- vision-language
- computer-vision
size_categories:
- 100K<n<1M
---
# LeafNet
LeafNet is a large-scale multimodal dataset for plant disease diagnosis,
introduced in ["LeafNet: A Large-Scale Dataset and Comprehensive Benchmark
for Foundational Vision-Language Understanding of Plant Diseases"](https://arxiv.org/abs/2602.13662)
(arXiv:2602.13662). The full dataset comprises 186,000 leaf images across
22 crop species and 97 classes (43 fungal diseases, 8 bacterial diseases,
2 mould/oomycete diseases, 6 viral diseases, 3 mite-induced diseases, plus
healthy leaves), each paired with an expert-curated symptom description.
The accompanying LeafBench VQA benchmark evaluates models on six tasks
(crop identification, healthy/diseased classification, disease
identification, symptom recognition, pathogen classification, and
scientific nomenclature); closed-source models such as GPT-4o reached up
to 72% accuracy, while the domain fine-tuned SCOLD model reached 99.15% on
disease identification.
## Notes
The public dataset is just ~70% subset of the full 186,000-image dataset (the remainder is held out
by the original authors).
## Layout
Single config (`train`), matching every other AgML-standardized dataset's
storage format with metadata parquet at the repo root, image shards under
`images/`:
```
LeafNet_P/
train-0000-of-0001.parquet # 121,337 rows: images, id, messages, raw_metadata
images/
image-train-000-of-003.zip
image-train-001-of-003.zip
image-train-002-of-003.zip
path_to_shard.parquet # 121,337 rows: path -> shard_file
```
`images` holds `{"bytes": None, "path": ...}` per row's single image.
`raw_metadata` keeps the original source `file_name` path for provenance, the verbatim `caption`.
`messages` is a single-turn conversion, since the source data is an
image-captioning dataset (one caption per image, no original question
field): a fixed instruction prompt (mentioned below) asking the model to describe the leaf's
condition, with the original caption as the assistant's answer was used to convert it to conversation.
```
USER_PROMPT = "Describe the condition of this plant leaf, including any visible disease and its symptoms."
```
## Usage
```python
from agml import loadImageTextToTextDataset
ds, store = loadImageTextToTextDataset("Project-AgML/LeafNet", token=HF_TOKEN)
print(ds) # DatasetDict({'train': ...})
ds["train"][0] # images decoded lazily on access
```
## Citation
If you use this dataset, please cite the original LeafNet paper:
```bibtex
@misc{nguyenquoc2026leafnet,
title = {LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases},
author = {Nguyen Quoc, Khang and Dao, Phuong D. and Quach, Luyl-Da},
year = {2026},
eprint = {2602.13662},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2602.13662}
}
```
The dataset also underlies the SCOLD vision-language model:
```bibtex
@article{NGUYENQUOC2025130084,
title = {A Vision-Language Foundation Model for Leaf Disease Identification},
journal = {Expert Systems with Applications},
pages = {130084},
year = {2025},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2025.130084},
author = {Khang {Nguyen Quoc} and Lan Le {Thi Thu} and Luyl-Da Quach},
}
```
---
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. |