location City: Amin Model Town, Khagan, Ashulia, Savar, Dhaka Country: Bangladesh. Local location: Shumi Nursery, Shetu Nursery, Bismillah Nursery etc. Geographical Location: 23 ° 53′ 2″ N and 90 ° 19′ 28″ E. Data accessibility Repository name: Mendeley Data Data identification number: 10.17632/9g668bfhy5.3 Direct URL to data: https://data.mendeley.com/datasets/9g668bfhy5/3 Related research article None 1. Value of the Data • The dataset presented here, a collaborative effort of researchers and industry professionals, is suitable for training machine learning models for rose leaf disease classification and detection. This makes it a valuable resource for all of us, as we work together to dev
Open resource ↗Mendeley Data · 10.17632/9g668bfhy5.3 · lines:1-48Unverified paper record
RoseLeafSet: Real-world leaf image dataset for AI-based agricultural solutions.
Data in brief · 25 Sept 2025 · 10.1016/j.dib.2025.112108
Abstract
This study highlights the growing significance of flowers, especially roses, in the global agricultural market, where they are cultivated for both personal enjoyment and commercial purposes. Among these, roses are considered one of the most popular and widely cultivated flowers. However, rose cultivators often encounter substantial challenges due to diseases that affect the plants, which can lead to significant economic losses in the agricultural sector. Timely and accurate detection of these diseases is crucial to mitigating their impact, potentially saving millions of dollars in crop losses. The dataset utilized in this research consists of 10,000 high-quality images collected from an initial set of 3113 images taken from several rose gardens located in Amin Model Town, Khagan, Ashulia, and Savar, Bangladesh. The data collection process spanned from October 30 to November 6, 2024. These images are categorized into four distinct classes: Healthy Leaf, Black Spot, Leaf Hole, and Dry Leaf, representing various stages of disease development in rose plants. The images were captured using a Vivo IQOO Z9x phone, ensuring high resolution and detailed imagery necessary for research analysis. This dataset serves as a valuable resource for researchers and developers working on creating efficient algorithms for the early and accurate identification of rose leaf diseases. By leveraging machine learning and image processing techniques, these algorithms could significantly enhance disease detection and prevention, helping to safeguard crops and reduce economic losses in the agricultural sector.
Plant phenotyping relevance
バラ葉の病徴を画像データセットとして体系的に収集・分類し、植物病害状態の画像ベース推定を支援する研究であり、データセット構築が中心です。
titleRoseLeafSet: Real-world leaf image dataset for AI-based agricultural solutions.
abstractThese images are categorized into four distinct classes: Healthy Leaf, Black Spot, Leaf Hole, and Dry Leaf, representing various stages of disease development in rose plants.
abstractThis dataset serves as a valuable resource for researchers and developers working on creating efficient algorithms for the early and accurate identification of rose leaf diseases.
Code and data availability
The paper is a data descriptor for RoseLeafSet, a public rose leaf image dataset (3113 original images, augmented to 10,000) deposited on Mendeley Data with an explicit direct URL and DOI (10.17632/9g668bfhy5.3). This is a paper-specific, publicly available plant image dataset directly reproducing the paper's phenotypy
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