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Crop and Weed Detection Dataset

130023614
Tagagriculture
Taskobject detection
Release YearMade in 2020
LicenseCC0 1.0
Download79 MB

Introduction #

Panara Utsav, Pandya Raviraj, Mohit Rayja

The objective of the Crop and Weed Detection Data with Bounding Boxes dataset is to create a system that accurately identifies and treats weeds, thus minimizing the risk of pesticide contamination on crops and reducing pesticide waste. The dataset comprises 1300 images featuring sesame crops and various weed types, each meticulously categorized. These images are presented in a 512x512 color format. Weeds are a common challenge in agriculture as they consume essential resources meant for crops, decreasing crop yields. To address this concern, farmers often resort to pesticide use, but this approach can inadvertently lead to pesticide residues on crops, potentially endangering consumer health.

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Dataset LinkHomepageDataset LinkGitHub

Summary #

Crop and Weed Detection Data with Bounding Boxes is a dataset for an object detection task. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 1300 images with 2072 labeled objects belonging to 2 different classes including weed and crop.

Images in the Crop and Weed Detection dataset have bounding box annotations. All images are labeled (i.e. with annotations). There is 1 split in the dataset: data (1300 images). The dataset was released in 2020.

Dataset Poster

Explore #

Crop and Weed Detection dataset has 1300 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 Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
OpenSample annotation mask from Crop and Weed DetectionSample image from Crop and Weed Detection
πŸ‘€
Have a look at 1300 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.

Search
Rows 1-2 of 2
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
weedβž”
rectangle
667
860
1.29
40.05%
cropβž”
rectangle
635
1212
1.91
58.96%

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 1-2 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
crop
rectangle
1212
31.56%
99.02%
0.19%
20px
3.91%
511px
99.8%
224px
43.82%
22px
4.3%
511px
99.8%
weed
rectangle
860
31.13%
94.23%
0.65%
43px
8.4%
509px
99.41%
255px
49.79%
38px
7.42%
511px
99.8%

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 2072 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 2072
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”
crop
rectangle
agri_0_6447.jpeg
512 x 512
325px
63.48%
487px
95.12%
60.38%
2βž”
weed
rectangle
agri_0_2697.jpeg
512 x 512
235px
45.9%
277px
54.1%
24.83%
3βž”
crop
rectangle
agri_0_5666.jpeg
512 x 512
454px
88.67%
482px
94.14%
83.48%
4βž”
weed
rectangle
agri_0_1993.jpeg
512 x 512
230px
44.92%
470px
91.8%
41.24%
5βž”
weed
rectangle
agri_0_1993.jpeg
512 x 512
135px
26.37%
128px
25%
6.59%
6βž”
weed
rectangle
agri_0_629.jpeg
512 x 512
155px
30.27%
189px
36.91%
11.18%
7βž”
weed
rectangle
agri_0_3534.jpeg
512 x 512
202px
39.45%
208px
40.62%
16.03%
8βž”
crop
rectangle
agri_0_6387.jpeg
512 x 512
105px
20.51%
46px
8.98%
1.84%
9βž”
crop
rectangle
agri_0_6387.jpeg
512 x 512
96px
18.75%
41px
8.01%
1.5%
10βž”
crop
rectangle
agri_0_6387.jpeg
512 x 512
95px
18.55%
84px
16.41%
3.04%

License #

Crop and Weed Detection data with bounding boxes is under CC0 1.0 license.

Source

Citation #

If you make use of the Crop and Weed Detection data with bounding boxes data, please cite the following reference:

@misc{Crop and Weed Detection data with bounding boxes,
  authors="Panara Utsav, Pandya Raviraj, Mohit Rayja",
  title="Crop and Weed Detection data with bounding boxes",
  year={2020},
  url="https://www.kaggle.com/datasets/ravirajsinh45/crop-and-weed-detection-data-with-bounding-boxes"
}

Source

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

@misc{ visualization-tools-for-crop-weed-detection-dataset,
  title = { Visualization Tools for Crop and Weed Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/crop-weed-detection } },
  url = { https://datasetninja.com/crop-weed-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset Crop and Weed 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='Crop and Weed 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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