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End of preview. Expand in Data Studio
Sesame Crop Weed Detection
Sesame crop and weed detection: 1,300 RGB images (512 x 512 pixels) of sesame plants and weeds in the field with 2,072 hand-drawn bounding boxes in two classes, sesame (1,212) and weed (860), 1 to 19 boxes per image. The publisher photographed 589 scenes at 4000 x 3000 pixels, kept 546 after cleaning, resized them to 512 x 512 and expanded them to 1,300 images with Keras ImageDataGenerator augmentation before labelling, so many images are augmented variants of the same scene. Published on Kaggle (CC0) by ravirajsinh45 and used as a benchmark in the MDPI Agriculture study D2FNet. The dataset contains 1,300 images with 2,072 bounding box annotations across 2 categories.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{zhu2026d2fnet,
title={D2FNet: A Lightweight Dual-Driven Texture–Semantic Fusion Network for Fine-Grained Real-Time UAV Weed–Crop Detection},
author={Zhu, Chenghua and Wu, Siyan and Chao, Xingqi and Lao, Minxuan and Yang, Lei and Cai, Lihua},
journal={Agriculture},
volume={16},
pages={1067},
year={2026},
publisher={MDPI}
}
This dataset was reformatted from its original format to match HuggingFace standards.
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