Apple Dataset Benchmark from Orchard Environment

Introduction #

Santosh Bhusal, Manoj Karkee, Qin Zhang

The Apple Dataset Benchmark from Orchard Environment in Modern Fruiting Wal consists of 2,298 images containing pictures of apple trees bearing fruit. These images were taken during research related to robotic harvesting and yield estimation at Washington State University. The images were captured over multiple years, at various times of the day, and feature different apple varieties. Various sensors and modern fruiting wall architecture were employed in this research.

Homepage

Summary #

Apple Dataset Benchmark from Orchard Environment in Modern Fruiting Wall is a dataset for an object detection task. It is used in the agricultural industry.

The dataset consists of 2299 images with 15439 labeled objects belonging to 1 single class (apple).

Images in the Apple Dataset Benchmark from Orchard Environment dataset have bounding box annotations. There are 9 (0% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Alternatively, the dataset could be split into 4 image sets: harvesting robot 2016 (1374 images), harvesting robot 2017 (413 images), artificial light (273 images), and crop load estimation (239 images). The dataset was released in 2019 by the Washington State University.

Explore #

Apple Dataset Benchmark from Orchard Environment dataset has 2299 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 Apple Dataset Benchmark from Orchard Environment

Sample image from Apple Dataset Benchmark from Orchard Environment

👀

Have a look at 2299 images

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

Class balance #

There are 1 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 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
apple➔ rectangle	2290	15439	6.74	6.1%

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 1-1 of 1

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
apple rectangle	15439	0.93%	6.38%	0%	4px	0.42%	631px	27.6%	127px	10.58%	4px	0.31%	753px	25.3%

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 15439 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 15439

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➔	apple rectangle	HarvestingRobot2016_image-312.png	960 x 1280	113px	11.77%	126px	9.84%	1.16%
2➔	apple rectangle	HarvestingRobot2016_image-312.png	960 x 1280	131px	13.65%	124px	9.69%	1.32%
3➔	apple rectangle	HarvestingRobot2016_image-312.png	960 x 1280	121px	12.6%	131px	10.23%	1.29%
4➔	apple rectangle	HarvestingRobot2016_image-312.png	960 x 1280	114px	11.88%	107px	8.36%	0.99%
5➔	apple rectangle	HarvestingRobot2016_image-312.png	960 x 1280	114px	11.88%	110px	8.59%	1.02%
6➔	apple rectangle	HarvestingRobot2016_image-312.png	960 x 1280	122px	12.71%	116px	9.06%	1.15%
7➔	apple rectangle	CropLoadEstimation_image-28.png	1992 x 1494	150px	7.53%	142px	9.5%	0.72%
8➔	apple rectangle	CropLoadEstimation_image-28.png	1992 x 1494	138px	6.93%	113px	7.56%	0.52%
9➔	apple rectangle	CropLoadEstimation_image-28.png	1992 x 1494	146px	7.33%	118px	7.9%	0.58%
10➔	apple rectangle	CropLoadEstimation_image-28.png	1992 x 1494	133px	6.68%	132px	8.84%	0.59%

License #

Apple Dataset Benchmark from Orchard Environment in Modern Fruiting Wall is under OpenAccess license.

Source

Citation #

If you make use of the Apple Dataset Benchmark from Orchard Environment data, please cite the following reference:

@misc{BhusalSantosh2019ADBf,
  title = {Apple Dataset Benchmark from Orchard Environment in Modern Fruiting Wall},
  abstract = {Dataset comprises 2,298 images. These zipped files contain images of apple trees with fruits captured during robotic harvesting and yield estimation studies at Washington State University from multiple years, different time of the day and multiple varieties using multiple sensors with modern fruiting wall architecture},
  author = {Bhusal, Santosh and Karkee, Manoj and Zhang, Qin},
  keywords = {Apples;Robotic Harvesting},
  language = {eng},
  publisher = {Washington State University},
  year = {2019},
}

Source

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

@misc{ visualization-tools-for-apple-dataset-benchmark-from-orchard-environment-dataset,
  title = { Visualization Tools for Apple Dataset Benchmark from Orchard Environment Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/apple-dataset-benchmark-from-orchard-environment } },
  url = { https://datasetninja.com/apple-dataset-benchmark-from-orchard-environment },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Dataset Apple Dataset Benchmark from Orchard Environment 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='Apple Dataset Benchmark from Orchard Environment', 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:

. . .

Disclaimer #

Our gal from the legal dep told us we need to post this:

Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.

You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.