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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.20176021 |
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| _version_ | 1866901770305798144 |
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| author | Rahim, Umme Fawzia Rahman, Md. Mushibur |
| author_facet | Rahim, Umme Fawzia Rahman, Md. Mushibur |
| contents | <p><strong>OpenField-BD-Tomato-Maturity-Detection</strong> is an object detection dataset developed for tomato maturity identification under natural open-field conditions in Bangladesh. The dataset contains <strong>600 tomato field images</strong> with annotated tomato objects belonging to three maturity classes: <strong>green</strong>, <strong>half-ripe</strong>, and <strong>fully-ripe</strong>.</p> <p>The images were collected from real tomato cultivation fields, where natural variations such as sunlight, shadow, complex background, leaf occlusion, fruit position, and different viewing angles are present. These field-level variations make the dataset suitable for developing and evaluating tomato detection models for practical agricultural applications.</p> <p>This dataset can be used for tomato maturity detection, ripeness monitoring, precision agriculture, smart farming, automated harvesting support, and computer vision-based crop analysis.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20176021 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | OpenField-BD-Tomato-Maturity-Detection Dataset Rahim, Umme Fawzia Rahman, Md. Mushibur Tomato maturity detection; Open-field tomato dataset; Bangladesh agriculture; Object detection; Ripeness classification; Precision agriculture <p><strong>OpenField-BD-Tomato-Maturity-Detection</strong> is an object detection dataset developed for tomato maturity identification under natural open-field conditions in Bangladesh. The dataset contains <strong>600 tomato field images</strong> with annotated tomato objects belonging to three maturity classes: <strong>green</strong>, <strong>half-ripe</strong>, and <strong>fully-ripe</strong>.</p> <p>The images were collected from real tomato cultivation fields, where natural variations such as sunlight, shadow, complex background, leaf occlusion, fruit position, and different viewing angles are present. These field-level variations make the dataset suitable for developing and evaluating tomato detection models for practical agricultural applications.</p> <p>This dataset can be used for tomato maturity detection, ripeness monitoring, precision agriculture, smart farming, automated harvesting support, and computer vision-based crop analysis.</p> |
| title | OpenField-BD-Tomato-Maturity-Detection Dataset |
| topic | Tomato maturity detection; Open-field tomato dataset; Bangladesh agriculture; Object detection; Ripeness classification; Precision agriculture |
| url | https://doi.org/10.5281/zenodo.20176021 |