Data · dataset · 2023
Comprehensive lychee leaf data set: Digital images for analysis and classification
Listed in Teesside University Research Data Repository
The lychee leaves in the investigated dataset were carefully selected for a variety of research and analysis uses.
Description
The 4,129 high-resolution images in this dataset each show intricate lychee foliage details. The photographs' standardization at a resolution of 1080 x 1920 pixels makes them uniform and easy to use for a range of applications.
Each of the three sets of leaves in this large dataset indicates a distinct aspect of lychee health and vitality. 1. Diseased Lychee Leaves (Class I): Total Count: 3,539 images Subcategories: This extensive class further subdivides into six subcategories, each representing a specific lychee leaf disease: -Entomosporium Leaf Spot on Woody Ornamentals - Leaf Blight Lychee Leaf Diseases - Lychee Algal Spot in Non-Direct Sunlight - Lychee Anthracnose on Cloudy Day - Lychee Leaf Mites in Direct Sunlight - Lychee Mayetiola After Raining 2.
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Dry Lychee Leaves (Class II): Total Count: 476 images Description: This category comprises images of lychee leaves in a desiccated state, reflecting the natural progression of foliage as it transitions from vigor to senescence. The dry lychee leaves may showcase characteristic features such as wilting, discoloration, and a withered appearance. 3. Healthy Lychee Leaves (Class III): Total Count: 114 images Description: These pristine specimens exemplify the ideal state of lychee leaves, exhibiting vibrant green hues, uniform textures, and the absence of any visible blemishes.
They serve as a benchmark for assessing the well-being of lychee foliage.
Links
Where it is published
- DOI doi.org/10.17632/xf7ryhs3cd.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
- Computer Science & AI · Earth & Environmental Science · Engineering · Life Sciences
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/xf7ryhs3cd.1 | 20 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[disease].local:disease:disease | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| 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:life-sciences | 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 |