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<p>The robust Dutch rose, also known as the <i>Rosa hybrida</i> is distinguished by its vibrant colors, superior product quality, and extended vase life. These rose varieties, originating from Netherlands, have proven highly successful in Indian agricultural conditions and the international export industry. The dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals. The primary purpose of this dataset is to support machine learning activities in agriculture and specifically for tasks such as automatic petal health evaluation and rose variety categorization. Although the rose flower is scientifically rich and has a wide range of industrial uses, it has not been given much attention in machine learning, especially when compared to other plant-based datasets. This study adds to the accuracy of quality assessment through the use of modern computer vision and machine learning methods, thus helping the agriculture sector, rose-based edible product making, and flavor development industries.</p>

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RoseVisuals: A Multi-Class Dutch Rose Petal Images Dataset for Automated Health and Pigmentation Classification via Deep Learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

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  • Arya Sabale,
  • Devashri Kapse,
  • Mahek Mehta,
  • Rutuja Rajendra Patil,
  • Gagandeep Kaur,
  • Ghanshyam G. Tejani,
  • Harshada Vishal Mhetre,
  • Anuradha Yenkikar,
  • Amol V. Patil,
  • A. S. Veerendra

摘要

The robust Dutch rose, also known as the Rosa hybrida is distinguished by its vibrant colors, superior product quality, and extended vase life. These rose varieties, originating from Netherlands, have proven highly successful in Indian agricultural conditions and the international export industry. The dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals. The primary purpose of this dataset is to support machine learning activities in agriculture and specifically for tasks such as automatic petal health evaluation and rose variety categorization. Although the rose flower is scientifically rich and has a wide range of industrial uses, it has not been given much attention in machine learning, especially when compared to other plant-based datasets. This study adds to the accuracy of quality assessment through the use of modern computer vision and machine learning methods, thus helping the agriculture sector, rose-based edible product making, and flavor development industries.