CWFID - Dataset Ninja

Introduction #

Released 2015-04-20 ·Sebastian Haug, Jörn Ostermann

The authors introduce CWFID: A Crop/Weed Field Image Dataset as a standardized dataset for addressing tasks such as distinguishing between crops and weeds, conducting single plant phenotyping, and other computer vision challenges in precision agriculture. These images were captured in an organic carrot farm during the early true leaf growth stage of the carrot plants, utilizing the autonomous field robot Bonirob. The dataset encompasses scenarios involving both intra- and inter-row weeds, where weeds and crops share similar size and grow in close proximity.

Each image in the dataset is accompanied by a ground truth vegetation segmentation mask, and the authors have manually annotated the plant type (crop or weed) for further reference. They also present preliminary findings for solving the crop/weed classification phenotyping problem and introduce evaluation methodologies to facilitate comparisons between various approaches.

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Homepage

Research Paper

Summary #

CWFID: A Crop/Weed Field Image Dataset is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the agricultural industry, and in the biological research.

The dataset consists of 60 images with 492 labeled objects belonging to 2 different classes including crop and weed.

Images in the CWFID dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2015 by the Bosch Research and Leibniz Universitat Hannover.

Here is the visualized example grid with animated annotations:

Explore #

CWFID dataset has 60 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

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Have a look at 60 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-2 of 2

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
crop➔ mask	60	162	2.7	1.61%
weed➔ mask	59	330	5.59	5.85%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-2 of 2

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
weed mask	330	1.05%	7.19%	0.03%	29px	3%	682px	70.6%	186px	19.28%	36px	2.78%	600px	46.3%
crop mask	162	0.6%	1.98%	0.08%	42px	4.35%	272px	28.16%	137px	14.2%	39px	3.01%	308px	23.77%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 492 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 492

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	weed mask	003_image.png	966 x 1296	176px	18.22%	163px	12.58%	1.08%
2➔	weed mask	003_image.png	966 x 1296	238px	24.64%	282px	21.76%	1.73%
3➔	weed mask	003_image.png	966 x 1296	130px	13.46%	323px	24.92%	1.14%
4➔	crop mask	003_image.png	966 x 1296	84px	8.7%	206px	15.9%	0.3%
5➔	crop mask	003_image.png	966 x 1296	248px	25.67%	236px	18.21%	1.7%
6➔	weed mask	003_image.png	966 x 1296	165px	17.08%	104px	8.02%	0.37%
7➔	weed mask	003_image.png	966 x 1296	107px	11.08%	114px	8.8%	0.39%
8➔	weed mask	003_image.png	966 x 1296	167px	17.29%	166px	12.81%	1.13%
9➔	weed mask	011_image.png	966 x 1296	160px	16.56%	97px	7.48%	0.41%
10➔	weed mask	011_image.png	966 x 1296	319px	33.02%	224px	17.28%	1.26%

License #

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.

Source

Citation #

If you make use of the CWFID data, please cite the following reference:

@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},
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-cwfid-dataset,
  title = { Visualization Tools for CWFID Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/cwfid } },
  url = { https://datasetninja.com/cwfid },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Dataset CWFID can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='CWFID', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

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Disclaimer #

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