Datasets:
File size: 1,932 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: objects
struct:
- name: bbox
list:
list: float64
- name: categories
list:
class_label:
names:
'0': OptimalBunch
'1': DamagedBunch
- name: split
dtype: string
splits:
- name: train
num_bytes: 672763568
num_examples: 10010
download_size: 672934058
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 Condition Detection
This dataset provides real-world RGB images of grapevine bunches captured in a field environment in Vairão, Portugal, using a handheld Xiaomi Redmi Note 7 smartphone. It features high-resolution imagery collected under natural agricultural conditions for object detection tasks focused on grape cluster condition assessment. The images were acquired with a dual-camera smartphone setup, offering practical examples of field-based agricultural imaging for computer vision research. The dataset contains 10,010 images with 11,672 bounding box annotations across 2 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
```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.*
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