enalis/LeafNet

The PlantVillage dataset, with over 54,000 images spanning 14 plant species and 26 disease types, has been widely used for leaf disease classification. However, it is limited in both scale and diversity. To address these limitations, we developed LeafNet, a large-scale dataset designed to support foundation models for leaf disease diagnosis. We introduce LeafNet comprises over 186,000 images from 22 crop species, covering 43 fungal diseases, 8 bacterial diseases, 2 mould (oomycete) diseases, 6 viral diseases, and 3 mite-induced diseases, categorized into 97 classes. The dataset was meticulously collected and processed to minimize intra-class variations while ensuring clarity by maintaining a consistent imaging distance. The disease symptom descriptions were curated from reputable sources, including UME, NIH, and published studies, providing high-quality annotations to support AI-driven plant pathology research.

Notes

This is part of LeafNet dataset is public for training with ~ 70% of the whole dataset

BibTeX

If you found our work useful in your research, please consider citing our work at: Quoc, Khang Nguyen, Lan Le Thi Thu, and Luyl-Da Quach. "A Vision-Language Foundation Model for Leaf Disease Identification." arXiv preprint arXiv:2505.07019 (2025).

@article{quoc2025vision,
  title={A Vision-Language Foundation Model for Leaf Disease Identification},
  author={Quoc, Khang Nguyen and Thu, Lan Le Thi and Quach, Luyl-Da},
  journal={arXiv preprint arXiv:2505.07019},
  year={2025}
}