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End of preview. Expand in Data Studio
Tomato Adaptive Yolov8 Detection
Open-field tomato maturity detection (OF-Tomato-BD): 604 photographs of tomato plants on open-field farms in Gazipur, Bangladesh, taken under natural light at 4K (3840 x 2160 or 2160 x 3840) or 3472 x 3472 pixels, with 2,802 bounding boxes around fruit labelled by maturity stage: green (1,461), half_ripened (913) and full_ripened (428). This is the Bangladesh open-field part of the authors' Cross-Regional Tomato dataset, used in the MDPI Agriculture study on Tomato-Adaptive Attention YOLOv8; it keeps the authors' train (482), val (59) and test (63) split in a 'split' column. The dataset contains 604 images with 2,802 bounding box annotations across 3 categories.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
The original train/test/val split has been preserved in the split column.
Citation
@article{rahim2026tomato,
title={Tomato-Adaptive Attention YOLOv8 for Accurate and Interpretable Maturity Detection Across Diverse Environments},
author={Rahim, Umme Fawzia and Rahman, Md. Mushibur and Mineno, Hiroshi},
journal={Agriculture},
volume={16},
pages={1130},
year={2026},
publisher={MDPI}
}
The dataset itself can be cited as:
Rahim, U. F., & Rahman, M. M. (2026). OpenField-BD-Tomato-Maturity-Detection Dataset [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.20176021
This dataset was reformatted from its original format to match HuggingFace standards.
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