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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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| _version_ | 1866901481326641152 |
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| author | S M Abdullah Al Shuaeb |
| author_facet | S M Abdullah Al Shuaeb |
| 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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19247531 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Graded Tomato Image Dataset for Quality Classification under Real-World Conditions S M Abdullah Al Shuaeb <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> |
| title | Graded Tomato Image Dataset for Quality Classification under Real-World Conditions |
| url | https://doi.org/10.5281/zenodo.19247531 |