Data · dataset · 2026
Spiny Gourd Tri-Class Quality Dataset
Listed in Teesside University Research Data Repository
The "Spiny Gourd Tri-Class Quality Dataset" is a specialized, curated subset extracted and refined from the open-source "Nature's Best vs. Bruised: A Veggie Evaluation" dataset under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Description
This dataset focuses specifically on Spiny Gourd (Momordica dioica), a widely consumed vegetable, to facilitate automated quality assessment and edibility evaluation using Computer Vision (CV) and Deep Learning (DL) models.
Key Details of the Dataset: 1. Target Crop: Spiny Gourd (Scientific Name: Momordica dioica) 2. Classification Categories: The dataset is structured into three distinct physical quality classes: - Fresh: High-quality, unblemished vegetables suitable for consumption. - Damaged: Vegetables showing minor structural, mechanical, or surface defects. - Severely Damaged: Highly degraded or rotten specimens unsuitable for commercial sale/consumption. 3.
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Environmental Conditions: Images feature authentic natural progression captured under varied camera angles, non-uniform illumination conditions, and consistent background setups. Purpose & Academic Usage: This dataset is designed to address the lack of specialized single-crop quality benchmarks in agricultural computer vision. Researchers can utilize this dataset to train, validate, and benchmark deep learning architectures (e.g., CNNs, Vision Transformers) for real-time agricultural sorting, post-harvest quality inspection, and automated grading systems.
Attribution & License: Original Dataset: "Nature's Best vs. Bruised: A Veggie Evaluation" License: Creative Commons Attribution 4.0 International (CC BY 4.0) Modification
Note
Filtered specifically for Spiny Gourd samples, re-annotated, and structured into a three-class quality taxonomy.
Links
Where it is published
- DOI doi.org/10.17632/p728rgr8bg.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 · Computer vision · Deep learning · Earth & Environmental Science
- Inferred from text
- Image 65%
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/p728rgr8bg.1 | 15 h 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: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].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[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 |