Data · dataset · 2022
Diseases of maize in the field
Listed in UP Research Data Repository
<p>In this dataset lies 2355 images of maize leaves with different diseases.
Description
The images were taken over a variety of times and locations in South Africa. The diseases labelled herein are:</p> <ul> <li>Grey Leaf Spot (GLS)</li> <li>Northern Corn Leaf Blight (NCLB)</li> <li>Common Rust (CR)</li> <li>Southern Rust (SR)</li> <li>Phaeosphaeria Leaf Spot (PLS)</li> </ul> <p>The data contains a realistic representation of field conditions where it shows images of leaves destroyed by bugs, protein deficiencies, leaves with hands occluding them, different lighting conditions, some leaves are wet, backgrounds vary wildly, anthers, bird droppings, several simultaneous and sometimes even overlapping diseases.The Readme.txt or the file description of the parent folder gives more details as to how the images are stored and annotated. </p> <p><br></p>
Links
Where it is published
- DOI doi.org/10.25403/upresearchdata.20237613.v1 ↗
DOI / persistent id · from researchdata up ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from researchdata up ac za
Topics
- From keywords
- Computer Science & AI · Computer vision · Deep learning · Earth & Environmental Science · Life Sciences · Medicine & Health · Plant pathology
- Inferred from text
- Image 65%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| UP Research Data Repository | oai:figshare.com:article/20237613 | 19 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].anzsrc:field:310805 | mapping · researchdata up ac za | vocabulary-mapper@1.0.0 | keywords['Plant pathology'] |
| concepts[field].anzsrc:field:460304 | mapping · researchdata up ac za | vocabulary-mapper@1.0.0 | keywords['Computer vision'] |
| concepts[field].anzsrc:field:461103 | mapping · researchdata up ac za | vocabulary-mapper@1.0.0 | keywords['Deep learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · researchdata up ac za | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | /metadata/dc/description |
| license | source · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| title | source · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | /metadata/dc/title |