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| Main Author: | |
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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19247531 |
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Table of Contents:
- <p>This dataset presents a collection of <strong>1004 tomato images</strong> categorized into four quality grades: Grade One, Grade Two, Grade Three, and Rotten, with <strong>251 images per class</strong>. The images were collected from local markets in <span><span>Tangail District</span></span> under <strong>real-world conditions</strong>, including variations in lighting, background, and environment. All images were captured from a fixed distance using a smartphone camera to ensure consistency. The dataset is designed to support research in <strong>computer vision, image classification, and agricultural quality assessment</strong>, and can be used for developing automated tomato grading systems using machine learning and deep learning techniques.</p>