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" (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

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:

@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:

@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},
}

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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.