Data · dataset · 2025
Carambola Leaf & Fruit Dataset for Disease Detection and Classification
Listed in Teesside University Research Data Repository
This dataset is designed for the detection and classification of diseases affecting carambola (starfruit) leaves and fruits using image-based analysis.
Description
Captured using a smartphone, it consists of high-quality images covering both healthy and diseased samples. The dataset can be utilized for training deep learning models in plant disease detection, classification, and agricultural monitoring applications.
Original Dataset: - Number of images: 2,618 - Data format: .jpg Processed Dataset: - Number of images: 2,618 - Data format: .jpg Augmented Dataset: - Number of images: 15,000 - Data format: .jpg Augmentation Techniques: 1.Rotation, 2. Flipping, 3. Brightening, 4.
Read the rest (2 more)
Contrast Adjustment, 5. Blurring, 6. Shearing, 7.
Zooming, 8. Adding Noise Applications: - This dataset can be used for plant disease detection and classification, enabling agricultural monitoring using AI-based models. - It supports image-based plant health assessment and can be applied in precision farming and crop management. - It can be used to train deep learning models for improved disease detection and classification accuracy.
Links
Where it is published
- DOI doi.org/10.17632/f35jp46gms.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
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/f35jp46gms.1 | 11 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].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: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: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 |