MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
2019-09-11
Loading...
Persistent link to this item
Statistics
View StatisticsKeywords
Collection period
2015-06-01
2016-09-30
2016-09-30
Date completed
2019-09-07
Date updated
Time period coverage
Geographic coverage
Source information
Journal Title
Journal ISSN
Volume Title
Title
MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
Published Date
2019-09-11
Authors
Author Contact
Haeni, Nicolai
haeni001@umn.edu
haeni001@umn.edu
Type
Dataset
Experimental Data
Field Study Data
Experimental Data
Field Study Data
Abstract
We present a new dataset with the goal of advancing the state-of-the-art in fruit detection, segmentation, and counting in orchard environments. We hope to achieve this by providing a large variety of high-resolution images acquired in orchards, together with human annotations of the fruits on the trees. Objects are labeled using polygon masks for each object instance to aid in precise object detection, localization or segmentation. Additionally, we provide data for patch-based counting of clustered fruits. Our dataset contains over 40'000 annotated object instances in 1000 images.
Description
The counting and detection data are used to solve two different subproblems of the larger problem that is yield estimation.
Yield estimation relies on accurate detection of the fruit. Since fruit can be clustered together, it is necessary to use a separate algorithm for counting (in most cases).
We provide data to develop and test algorithms for both subproblems.
Referenced by
Häni, N, Roy, P, Isler, V. A comparative study of fruit detection and counting methods for yield mapping in apple orchards. Journal of Field Robotics. 2019; 1– 20.
https://doi.org/10.1002/rob.21902
N. Häni, P. Roy, and V. Isler, “MinneApple: A Benchmark Dataset for Apple Detection and Segmentation,” IEEE Robotics and Automation Letters, pp. 1–1, 2020,
https://doi.org/10.1109/LRA.2020.2965061
https://doi.org/10.1002/rob.21902
N. Häni, P. Roy, and V. Isler, “MinneApple: A Benchmark Dataset for Apple Detection and Segmentation,” IEEE Robotics and Automation Letters, pp. 1–1, 2020,
https://doi.org/10.1109/LRA.2020.2965061
Related to
Replaces
Publisher
Collections
Funding information
USDA NIFA MIN-98-G02
item.page.sponsorshipfunderid
item.page.sponsorshipfundingagency
item.page.sponsorshipgrant
Previously Published Citation
Suggested citation
Haeni, Nicolai; Roy, Pravakar; Isler, Volkan. (2019). MinneApple: A Benchmark Dataset for Apple Detection and Segmentation. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/8ecp-3r13.
View/Download file
| File View/Open | Description | Size |
|---|---|---|
| MinneApple Data README.txt | Description of data | 5.17 KB |
| counting.tar.gz | Patch-based fruit counting dataset (2,875 JPG files and 67,990 PNG files) | 976.89 MB |
| detection.tar.gz | Fruit detection and segmentation dataset (1,671 PNG files) | 1.7 GB |
| test_data.zip | Contains test labels for counting, detection, and segmentation. Made available on 2022-09-12 | 1.17 GB |
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.