Datasets:
| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: objects | |
| struct: | |
| - name: bbox | |
| list: | |
| list: float64 | |
| - name: categories | |
| list: | |
| class_label: | |
| names: | |
| '0': weed | |
| splits: | |
| - name: train | |
| num_bytes: 12796353 | |
| num_examples: 358 | |
| download_size: 12800061 | |
| dataset_size: 12796353 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| license: cc-by-4.0 | |
| task_categories: | |
| - object-detection | |
| size_categories: | |
| - n<1K | |
| # Paddy Weed Target Detection | |
| This dataset provides real-world RGB images captured in rice paddy fields during the seedling stage, focusing on the detection of weeds within rice crops under natural field conditions. Images were collected using a handheld Canon IXUS 1000 HS camera with a telephoto lens, offering close-up field-level perspectives for agricultural computer vision applications. The dataset contains 358 images with 840 bounding box annotations across 1 category. | |
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. | |
| ## Citation | |
| ```bibtex | |
| @article{deng2023weed, | |
| title={Weed target detection at seedling stage in paddy fields based on YOLOX}, | |
| author={Deng, Xiangwu and Qi, Long and Liu, Zhuwen and Liang, Song and Gong, Kunsong and Qiu, Guangjun}, | |
| journal={PLOS ONE}, | |
| volume={18}, | |
| pages={e0294709}, | |
| year={2023}, | |
| publisher={Public Library of Science} | |
| } | |
| ``` | |
| *This dataset was reformatted from its original format to match HuggingFace standards.* | |