Data · dataset · 2025
soybeans-not-noted-cut
Listed in Teesside University Research Data Repository
This dataset contains images of grains classified into six categories based on different types of defects.
Description
The categories include good, moldy, burnt, pecky, scorched, and greenish. Each class is organized into separate folders, with images representing the specific conditions of each defect type.
The dataset was collected to train machine learning models for the classification and detection of grains in various quality conditions. The images are in JPG format and range in size from 500x550 to 900x850 pixels. Categories: Good: Grains with no visible defects or signs of deterioration.
Read the rest (3 more)
Moldy: Grains with visible mold or discoloration due to mold growth. Burnt: Grains showing signs of burning or damage due to high temperatures. Pecky: Grains damaged by insects or pests.
Scorched: Grains with superficial burning caused by improper processing. Greenish: Grains showing a green color due to factors like immaturity or weather exposure. Format: Images organized into folders by category.
Image Size: Ranges from 500x550 to 900x850 pixels. Number of Images: 7328.
Links
Where it is published
- DOI doi.org/10.17632/xnx9bfbh6b.1 ↗
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
- Artificial intelligence · Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Machine learning · Social Science
- Inferred from text
- Image 75%
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/xnx9bfbh6b.1 | 9 d 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:group:4602 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial Intelligence'] |
| 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:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | 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:social-science | 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 (75%) |
| 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 |