Evidence map›Paper›PMID 42337298›Full record

ArticleScientific data2026

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

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

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In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Arya SabaleDepartment of Computer Science Engineering - Artificial Intelligence and Machine Learning, Vishwakarma Institute of Information Technology, Pune, India.
Devashri KapseDepartment of Computer Science Engineering - Artificial Intelligence and Machine Learning, Vishwakarma Institute of Information Technology, Pune, India.
Mahek MehtaDepartment of Computer Science Engineering - Artificial Intelligence and Machine Learning, Vishwakarma Institute of Information Technology, Pune, India.
Rutuja Rajendra PatilDepartment of Computer Engineering, MIT, Academy of Engineering, Alandi, Pune, India.
Gagandeep KaurDepartment of Computer Engineering, Mukesh Patel School of Technology, Management and Engineering, SVKM's NMIMS, Shirpur, India.
Ghanshyam G TejaniDepartment of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 600077, India.ORCID http://orcid.org/0000-0001-9106-0313
Harshada Vishal MhetreDepartment of Electronics & Communication Engineering, Bharati Vidyapeeth (Deemed University) College of Engineering, Pune, India.
Anuradha YenkikarDepartment of Computer Science Engineering - Artificial Intelligence, Vishwakarma Institute of Technology, Pune, India.
Amol V PatilDepartment of Computer Science Engineering-Artificial Intelligence, Vishwakarma Institute of Technology, Pune, India.
A S VeerendraManipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. veerendra.babu@manipal.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Deep LearningFlowersPigmentationRosaColorNetherlands

Identifiers

PMID42337298
PMCPMC13493834

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.