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

Arya Sabale¹, Devashri Kapse¹, Mahek Mehta¹

¹Department of Computer Science Engineering - Artificial Intelligence and Machine Learning, Vishwakarma Institute of Information Technology, Pune, India.

Scientific Data

|June 23, 2026

Summary

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This summary is machine-generated.

This study introduces a new dataset of Dutch rose (Rosa hybrida) petal images for machine learning. It enables automatic petal health evaluation and rose variety categorization, enhancing agricultural quality assessment.

Area of Science:

Horticulture and Agricultural Science
Computer Vision
Machine Learning

Background:

The Dutch rose (Rosa hybrida) is valued for its quality and vase life, with success in Indian agriculture and exports.
Limited machine learning research exists for roses compared to other plants, despite their scientific and industrial importance.

Purpose of the Study:

To create a high-resolution dataset of 1,995 Dutch rose petal images.
To support machine learning for automatic petal health evaluation and rose variety categorization.
To improve quality assessment in the agriculture and rose product industries.

Main Methods:

Collection of 1,995 high-resolution images of Rosa hybrida petals.
Categorization of images by petal color (red, yellow, white, pink, purple, orange, bi-color, multi-color).

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Inclusion of petal health status (fresh, dry, diseased).

Main Results:

A comprehensive dataset for agricultural machine learning tasks.
Foundation for developing advanced computer vision models for rose analysis.
Potential for enhanced accuracy in quality assessment.

Conclusions:

The dataset advances machine learning applications in agriculture, specifically for rose quality and variety assessment.
Modern computer vision and machine learning methods can significantly improve rose quality evaluation.
Benefits extend to the edible product and flavor development sectors.