Crop and Weed Detection - Dataset Ninja

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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GitHub

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.

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.

Sample annotation mask from Crop and Weed Detection

Sample image from Crop and Weed Detection

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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.

Rows 1-2 of 2

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
weed➔ rectangle	667	860	1.29	40.05%
crop➔ 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.

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.

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.

Rows 1-10 of 2072

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➔	crop rectangle	agri_0_6447.jpeg	512 x 512	325px	63.48%	487px	95.12%	60.38%
2➔	weed rectangle	agri_0_2697.jpeg	512 x 512	235px	45.9%	277px	54.1%	24.83%
3➔	crop rectangle	agri_0_5666.jpeg	512 x 512	454px	88.67%	482px	94.14%	83.48%
4➔	weed rectangle	agri_0_1993.jpeg	512 x 512	230px	44.92%	470px	91.8%	41.24%
5➔	weed rectangle	agri_0_1993.jpeg	512 x 512	135px	26.37%	128px	25%	6.59%
6➔	weed rectangle	agri_0_629.jpeg	512 x 512	155px	30.27%	189px	36.91%	11.18%
7➔	weed rectangle	agri_0_3534.jpeg	512 x 512	202px	39.45%	208px	40.62%	16.03%
8➔	crop rectangle	agri_0_6387.jpeg	512 x 512	105px	20.51%	46px	8.98%	1.84%
9➔	crop rectangle	agri_0_6387.jpeg	512 x 512	96px	18.75%	41px	8.01%	1.5%
10➔	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 = { nov },
  note = { visited on 2024-11-21 },
}

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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Disclaimer #

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