Datasets:
| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: objects | |
| struct: | |
| - name: bbox | |
| list: | |
| list: float64 | |
| - name: categories | |
| list: | |
| class_label: | |
| names: | |
| '0': Bunch | |
| - name: split | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 672763568 | |
| num_examples: 10010 | |
| download_size: 672933948 | |
| dataset_size: 672763568 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| license: cc-by-4.0 | |
| task_categories: | |
| - object-detection | |
| size_categories: | |
| - 10K<n<100K | |
| # Grapevine Bunch Detection | |
| This dataset provides real-world RGB images of grape bunches in vineyard fields located in Vairão, Portugal. Images were captured using a handheld smartphone in natural agricultural field conditions, offering diverse perspectives of grape clusters under typical vineyard environments. It serves as a practical resource for developing and evaluating object detection models in precision agriculture applications. The dataset contains 10,010 images with 11,672 bounding box annotations across 1 category. | |
| 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 | |
| ```bibtex | |
| @article{pinheiro2023deep, | |
| title={Deep Learning YOLO-Based Solution for Grape Bunch Detection and Assessment of Biophysical Lesions}, | |
| author={Pinheiro, Isabel and Moreira, Germano and Queirós da Silva, Daniel and Magalhães, Sandro and Valente, António and Moura Oliveira, Paulo and Cunha, Mário and Santos, Filipe}, | |
| journal={Agronomy}, | |
| volume={13}, | |
| pages={1120}, | |
| year={2023}, | |
| publisher={MDPI} | |
| } | |
| ``` | |
| *This dataset was reformatted from its original format to match HuggingFace standards.* | |