Potato Plants Images - Dataset Ninja

Summary #

A Dataset of Multispectral Potato Plants Images is a dataset for an object detection task. It is used in the agricultural industry.

The dataset consists of 1920 images with 26414 labeled objects belonging to 2 different classes including healthy and stressed.

Images in the Potato Plants Images dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: RGB_Augmented (1560 images) and RGB_Images (360 images). The dataset was released in 2021 by the University of Idaho.

Explore #

Potato Plants Images dataset has 1920 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 Potato Plants Images

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Have a look at 1920 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
healthy➔ rectangle	1914	10642	5.56	32.07%
stressed➔ rectangle	1890	15772	8.34	40.24%

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
stressed rectangle	15772	4.84%	24.85%	0.24%	25px	3.33%	546px	72.8%	161px	21.46%	28px	3.73%	609px	81.2%
healthy rectangle	10642	5.78%	63.65%	0.21%	15px	2%	742px	98.93%	173px	23.05%	25px	3.33%	749px	99.87%

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 26414 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 26414

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➔	healthy rectangle	image1244.jpg	750 x 750	128px	17.07%	105px	14%	2.39%
2➔	healthy rectangle	image1244.jpg	750 x 750	160px	21.33%	153px	20.4%	4.35%
3➔	healthy rectangle	image1244.jpg	750 x 750	232px	30.93%	128px	17.07%	5.28%
4➔	healthy rectangle	image1244.jpg	750 x 750	129px	17.2%	114px	15.2%	2.61%
5➔	healthy rectangle	image1244.jpg	750 x 750	120px	16%	80px	10.67%	1.71%
6➔	healthy rectangle	image1244.jpg	750 x 750	249px	33.2%	95px	12.67%	4.21%
7➔	healthy rectangle	image1244.jpg	750 x 750	118px	15.73%	160px	21.33%	3.36%
8➔	healthy rectangle	image1244.jpg	750 x 750	129px	17.2%	251px	33.47%	5.76%
9➔	healthy rectangle	image1244.jpg	750 x 750	134px	17.87%	111px	14.8%	2.64%
10➔	stressed rectangle	image1244.jpg	750 x 750	110px	14.67%	208px	27.73%	4.07%

License #

ADD CUSTOM LICENSE MANUALLY

Source

Citation #

If you make use of the Potato Plants Images data, please cite the following reference:

@dataset{Potato Plants Images,
	author={Aleksandar Vakanski},
	title={A Dataset of Multispectral Potato Plants Images},
	year={2021},
	url={https://www.webpages.uidaho.edu/vakanski/Multispectral_Images_Dataset.html}
}

Source

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

@misc{ visualization-tools-for-potato-plants-images-dataset,
  title = { Visualization Tools for Potato Plants Images Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/potato-plants-images } },
  url = { https://datasetninja.com/potato-plants-images },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2025 },
  month = { feb },
  note = { visited on 2025-02-19 },
}

Download #

Dataset Potato Plants Images 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='Potato Plants Images', 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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