https://github.com/cwfid/dataset
Crop/Weed Field Image Dataset
https://github.com/cwfid/dataset
agriculture annotations classification crop dataset discrimination mask paper segmentation weed
Last synced: 7 months ago
JSON representation
Crop/Weed Field Image Dataset
- Host: GitHub
- URL: https://github.com/cwfid/dataset
- Owner: cwfid
- Created: 2014-07-23T20:05:36.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2015-04-20T11:00:45.000Z (over 10 years ago)
- Last Synced: 2024-10-30T16:41:33.695Z (12 months ago)
- Topics: agriculture, annotations, classification, crop, dataset, discrimination, mask, paper, segmentation, weed
- Homepage:
- Size: 84.4 MB
- Stars: 134
- Watchers: 5
- Forks: 48
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- -awesome-agriculture - CWFID - Dataset comprising field images, vegetation segmentation masks and crop/weed plant type annotations. (Datasets, Machine Learning and AI)
README
A Crop/Weed Field Image Dataset
=============
The Crop/Weed Field Image Dataset (CWFID) accompanies the following publication: "Sebastian Haug, Jörn Ostermann: A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks, [CVPPP 2014](http://www.plant-phenotyping.org/CVPPP2014) Workshop, [ECCV 2014](http://eccv2014.org)"
This dataset comprises field [images](images), vegetation segmentation [masks](masks) and crop/weed plant type [annotations](annotations). The paper provides details, e.g. on the field setting, acquisition conditions, image and ground truth data format.
You can download the complete dataset here: [Download CWFID](http://github.com/cwfid/dataset/releases).
Paper
-----
Paper available [here](http://rd.springer.com/chapter/10.1007%2F978-3-319-16220-1_8).
Bibtex:
```
@inproceedings{haug15,
author={Haug, Sebastian and Ostermann, J{\"o}rn},
title={A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks},
year={2015},
booktitle={Computer Vision - ECCV 2014 Workshops},
doi={10.1007/978-3-319-16220-1_8},
url={http://dx.doi.org/10.1007/978-3-319-16220-1_8},
pages={105--116},
}
```
Use
---
All data is subject to copyright and may only be used for non-commercial research. In case of use please cite our publication.
Contact Sebastian Haug (sebastian.haug@de.bosch.com) for any questions.