Apple from Orchard Environment

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 from Orchard Environment dataset have bounding box annotations. There are 9 (0% of the total) unlabeled images (i.e. without annotations). There are 4 splits in the dataset: HarvestingRobot2016 (1374 images), HarvestingRobot2017 (413 images), ArtificialLight (273 images), and CropLoadEstimation (239 images). The dataset was released in 2019 by the Washington State University.

Explore #

Apple 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 from Orchard Environment

Sample image from Apple from Orchard Environment

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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	image-792.png	960 x 1280	111px	11.56%	114px	8.91%	1.03%
2➔	apple rectangle	image-792.png	960 x 1280	125px	13.02%	117px	9.14%	1.19%
3➔	apple rectangle	image-792.png	960 x 1280	146px	15.21%	154px	12.03%	1.83%
4➔	apple rectangle	image-792.png	960 x 1280	181px	18.85%	123px	9.61%	1.81%
5➔	apple rectangle	image-792.png	960 x 1280	154px	16.04%	136px	10.62%	1.7%
6➔	apple rectangle	image-792.png	960 x 1280	147px	15.31%	125px	9.77%	1.5%
7➔	apple rectangle	image-792.png	960 x 1280	172px	17.92%	135px	10.55%	1.89%
8➔	apple rectangle	image-499.png	960 x 1280	105px	10.94%	81px	6.33%	0.69%
9➔	apple rectangle	image-499.png	960 x 1280	123px	12.81%	103px	8.05%	1.03%
10➔	apple rectangle	image-499.png	960 x 1280	116px	12.08%	110px	8.59%	1.04%

License #

ADD CUSTOM LICENSE MANUALLY

Source

Citation #

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

@dataset{Apple from Orchard Environment,
	author={Santosh Bhusal and Manoj Karkee and Qin Zhang},
	title={Apple Dataset Benchmark from Orchard Environment in Modern Fruiting Wall},
	year={2019},
	url={https://alliance-wsu.esploro.exlibrisgroup.com/esploro/outputs/dataset/Apple-Dataset-Benchmark-from-Orchard-Environment/99900502619401842}
}

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-benchmark-from-orchard-environment-dataset,
  title = { Visualization Tools for Apple from Orchard Environment Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/apple-benchmark-from-orchard-environment } },
  url = { https://datasetninja.com/apple-benchmark-from-orchard-environment },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2025 },
  month = { feb },
  note = { visited on 2025-02-11 },
}

Download #

Dataset Apple 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 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.

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