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Perrenial Plants Detection Dataset

392141
Tagagriculture
Taskobject detection
Release YearMade in 2021
LicenseMIT
Download5 GB

Introduction #

Benedikt Geisler

The author of the Perrenial Plants Detection dataset explores automating weed removal in perennial plant cultivation, which currently involves physically demanding manual labor. They aim to answer two key questions: 1) Can optical RGB inspection combined with advanced machine learning reliably detect weeds? 2) Does providing a plant center keypoint in the dataset aid in automatic center identification?

Over seven weeks, images were collected at a southern German plant nursery using a Fuji X-T2 camera, resulting in 392 high-resolution images (24MP, 4000x6000px). The dataset includes sections for weed classification and keypoint detection, with some image overlap.

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Dataset LinkHomepage

Summary #

Perrenial Plants Detection is a dataset for an object detection task. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 392 images with 1159 labeled objects belonging to 14 different classes including not identified, weed, stellaria media, and other: veronica arvensis, cirsium arvense, poa anua, matricaria chamomilla, taraxacum officinale, sagina subulata, woods, cardamine hirsuta, elymus repens, portulaca oleracea, and artiplex oblongifolia.

Images in the Perrenial Plants Detection dataset have bounding box annotations. There are 2 (1% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (294 images), val (77 images), and test (21 images). The dataset was released in 2021.

Here are the visualized examples for the classes:

Explore #

Perrenial Plants Detection dataset has 392 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.

OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
OpenSample annotation mask from Perrenial Plants DetectionSample image from Perrenial Plants Detection
πŸ‘€
Have a look at 392 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 14 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.

Search
Rows 1-10 of 14
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
not identifiedβž”
rectangle
103
173
1.68
2.62%
weedβž”
rectangle
99
262
2.65
0.57%
stellaria mediaβž”
rectangle
90
189
2.1
4.51%
veronica arvensisβž”
rectangle
71
130
1.83
4.94%
cirsium arvenseβž”
rectangle
70
77
1.1
5.99%
poa anuaβž”
rectangle
42
121
2.88
4.62%
matricaria chamomillaβž”
rectangle
35
70
2
8.65%
taraxacum officinaleβž”
rectangle
16
18
1.12
5.72%
woodsβž”
rectangle
9
16
1.78
2.03%
sagina subulataβž”
rectangle
9
91
10.11
1.39%

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.

Search
Rows 1-10 of 14
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
rectangle
262
0.22%
3.58%
0%
26px
0.65%
854px
21.35%
180px
4.43%
26px
0.43%
786px
22.74%
stellaria media
rectangle
189
2.15%
24.56%
0.04%
107px
2.67%
2370px
59.25%
583px
14.59%
84px
1.4%
2805px
46.75%
not identified
rectangle
173
1.56%
9.14%
0.15%
151px
3.77%
1393px
34.83%
536px
13.39%
177px
2.95%
1670px
27.83%
veronica arvensis
rectangle
130
2.71%
36.58%
0.05%
83px
2.08%
3579px
89.47%
632px
15.76%
144px
2.4%
2453px
40.88%
poa anua
rectangle
121
1.61%
13.9%
0.12%
153px
3.83%
1662px
41.55%
549px
13.72%
146px
2.43%
2871px
47.85%
sagina subulata
rectangle
91
0.14%
1.62%
0.01%
44px
1.1%
699px
17.48%
140px
3.49%
39px
0.65%
628px
10.47%
cirsium arvense
rectangle
77
5.52%
52.69%
0.07%
161px
4.03%
2976px
86.11%
861px
21.76%
110px
1.83%
3172px
86.98%
matricaria chamomilla
rectangle
70
4.34%
18.6%
0.24%
205px
5.12%
2343px
58.58%
948px
23.69%
270px
4.5%
1905px
31.75%
taraxacum officinale
rectangle
18
5.09%
32.1%
0.04%
116px
2.9%
2342px
58.55%
801px
20.03%
89px
1.48%
3289px
54.82%
woods
rectangle
16
1.14%
6.16%
0.03%
88px
2.2%
1181px
29.52%
403px
10.08%
77px
1.28%
1251px
20.85%

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.

Spatial Heatmap

Objects #

Table contains all 1159 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.

Search
Rows 1-10 of 1159
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
stellaria media
rectangle
DSCF8556.JPG
4000 x 6000
288px
7.2%
322px
5.37%
0.39%
2βž”
stellaria media
rectangle
DSCF8556.JPG
4000 x 6000
814px
20.35%
1157px
19.28%
3.92%
3βž”
cirsium arvense
rectangle
DSCF8717.JPG
4000 x 6000
1055px
26.38%
776px
12.93%
3.41%
4βž”
cirsium arvense
rectangle
DSCF8822.JPG
4000 x 6000
1064px
26.6%
1159px
19.32%
5.14%
5βž”
not identified
rectangle
DSCF8595.JPG
4000 x 6000
322px
8.05%
409px
6.82%
0.55%
6βž”
cirsium arvense
rectangle
DSCF8646.JPG
4000 x 6000
609px
15.22%
596px
9.93%
1.51%
7βž”
not identified
rectangle
DSCF8592.JPG
4000 x 6000
330px
8.25%
513px
8.55%
0.71%
8βž”
not identified
rectangle
DSCF8592.JPG
4000 x 6000
297px
7.42%
326px
5.43%
0.4%
9βž”
weed
rectangle
DSCF8882.JPG
4000 x 6000
121px
3.02%
96px
1.6%
0.05%
10βž”
weed
rectangle
DSCF8882.JPG
4000 x 6000
54px
1.35%
51px
0.85%
0.01%

License #

Perrenial Plants Detection is under MIT license.

Citation #

If you make use of the Perrenial Plants Detection data, please cite the following reference:

@dataset{Perrenial Plants Detection,
  author={Benedikt Geisler},
  title={Perrenial Plants Detection},
  year={2021},
  url={https://www.kaggle.com/datasets/benediktgeisler/perrenial-plants-detection}
}

Source

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

@misc{ visualization-tools-for-perrenial-plants-dataset,
  title = { Visualization Tools for Perrenial Plants Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/perrenial-plants } },
  url = { https://datasetninja.com/perrenial-plants },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-21 },
}

Download #

Dataset Perrenial Plants Detection 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='Perrenial Plants Detection', 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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