Data · dataset · 2025
Sapodilla Leaf Disease Detection and Classification Dataset
Listed in Teesside University Research Data Repository
This dataset has been created to support the detection and classification of diseases affecting sapodilla leaves using image-based machine learning methods.
Description
Images were captured under real-world outdoor conditions with a smartphone, representing a variety of healthy and diseased leaves. The dataset provides a strong foundation for building AI models aimed at plant disease diagnosis, agricultural automation, and precision farming for sapodilla crops.
Original Dataset: -Number of images: 1,778 -Data format: .jpg Processed Dataset: -Number of images: 1,778 -Data format: .jpg Augmented Dataset: -Number of images: 14,000 -Data format: .jpg Augmentation Techniques: 1. Rotation, 2. Horizontal Flipping, 3.
Read the rest (3 more)
Vertical Flipping, 4. Brightness Enhancement, 5. Contrast Variation, 6.
Image Blurring, 7. Shearing, 8. Zooming, 9.
Noise Injection Applications: -Enables AI-based early detection of sapodilla leaf and fruit diseases to support better farm management. -Useful for creating automated classification systems for tropical fruit crops. -Supports research in precision farming by enabling image-based plant health assessment models. -Provides a robust base for improving disease detection accuracy in real-world agricultural settings.
Links
Where it is published
- DOI doi.org/10.17632/7jnk7rv3cs.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
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/7jnk7rv3cs.2 | 6 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 (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 |