Data · dataset · 2025
PhulMedic: A Bangladeshi Medicinal Flower Dataset
Listed in Teesside University Research Data Repository
Dataset Overview: This dataset consists of 5,140 high-resolution images of five medicinally significant flower species commonly found in Bangladesh, intended for machine learning, deep learning, and computer vision research.
Description
Data Source: Data collection was carried out between May and July 2025 at the Daffodil International University (DIU) campus in Dhaka. Images were captured using various smartphone cameras, including Samsung J5 Prime, Samsung Galaxy M31, and Xiaomi Redmi 12.
To ensure environmental diversity and practical applicability, each sample was photographed under varying natural lighting conditions, from multiple angles, and against different backgrounds. Dataset Composition: For each flower sample, 20 distinct images were taken. The dataset includes 50 samples of Hibiscus (1,000 images), 51 samples of Crape Jasmine (1,020 images), 51 samples of Marigold (1,020 images), 51 samples of Madagascar Periwinkle (1,020 images), and 54 samples of Wedelia (1,080 images), totaling 5,140 images.
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Applications: # Medicinal flower detection and classification. # Automated flora identification systems. # Herbal medicine research and documentation. # Machine learning and computer vision model development.
Links
Where it is published
- DOI doi.org/10.17632/92n7gtrkhx.2 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Computer vision · Deep learning · Earth & Environmental Science · Life Sciences · Machine learning · Medicine & Health
- Inferred from text
- Image 65%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/92n7gtrkhx.2 | 10 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:field:460304 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Computer Vision'] |
| concepts[field].anzsrc:field:461103 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Deep Learning'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |