Project-AgML/roseleafset_disease_classification

Roseleafset Disease Classification

RoseLeafSet is a set of close-up smartphone photos of rose leaves collected in rose nurseries around Savar, Dhaka, Bangladesh, between October 30 and November 6, 2024, labelled into four foliar conditions: black spot, dry leaf, healthy leaf and leaf holes. Images were taken with a Vivo IQOO Z9x phone at 3264 x 2448 pixels. The default raw config holds the 3,113 original photos (class-imbalanced: 1,288 black spot, 324 dry leaf, 818 healthy, 683 leaf holes); the augmented config holds the authors' class-balanced augmented set of 2,500 images per class (10,000 total), derived from contrast-enhanced, padded versions of the originals. The dataset contains raw and augmented versions.
The raw dataset contains 3,113 images.
Images per class:

• Black Spot: 1,288
• Dry Leaf: 324
• Healthy Leaf: 818
• Leaf Holes: 683

The augmented dataset contains 10,000 images.
Images per class:

• Black Spot: 2,500
• Dry Leaf: 2,500
• Healthy Leaf: 2,500
• Leaf Holes: 2,500

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

Citation

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The dataset itself can be cited as:

Jinia, J. T., Rahman, M. S., Mojumdar, M. U., & Bijoy, M. H. I. (2025). RoseLeafSet: Real-World Leaf Image Dataset for AI-Based Agricultural Solutions [Dataset]. Mendeley Data. https://doi.org/10.17632/9G668BFHY5.3

This dataset was reformatted from its original format to match HuggingFace standards.