Datasets:
dataset_info:
features:
- name: image
dtype: image
- name: objects
struct:
- name: bbox
list:
list: float64
- name: categories
list:
class_label:
names:
'0': sugar_beet
'1': weed
- name: source_image
dtype: string
splits:
- name: train
num_bytes: 5713810792
num_examples: 18593
download_size: 5713217193
dataset_size: 5713810792
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
Sugar Beet Weed Detection
PaimioSB2023 is a field dataset for detecting sugar beet seedlings and weeds, collected in 2023 on a 2-hectare research sugar beet field in Hevonpää, Finland, where a commercial FarmDroid FD20 robot was evaluated for seeding and weeding. RGB images from a GoPro action camera mounted 2 m above the ground on the robot, a Parrot Anafi drone flying at 4 m, and a mobile phone were cut into 1280 x 1280 tiles, and each tile was manually annotated with bounding boxes for two classes: sugar beet and weed. The source_image column identifies the original photo each tile came from, so tiles from the same photo can be kept in the same split. The dataset contains 18,593 images with 338,171 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{palva2024assessment,
title={Assessment of the Performance of a Field Weeding Location-Based Robot Using YOLOv8},
author={Palva, Reetta and Kaila, Eerikki and García-Pascual, Borja and Bloch, Victor},
journal={Agronomy},
volume={14},
pages={2215},
year={2024},
publisher={MDPI}
}
The dataset itself can be cited as:
Bloch, V. (2024). PaimioSB2023 dataset for sugar beet and weeds discrimination [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10716274
This dataset was reformatted from its original format to match HuggingFace standards.