MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
2019-09-11
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2015-06-01
2016-09-30
2016-09-30
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2019-09-07
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Title
MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
Published Date
2019-09-11
Authors
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Haeni, Nicolai
haeni001@umn.edu
haeni001@umn.edu
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Dataset
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Field Study Data
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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
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USDA NIFA MIN-98-G02
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Haeni, Nicolai; Roy, Pravakar; Isler, Volkan. (2019). MinneApple: A Benchmark Dataset for Apple Detection and Segmentation. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/8ecp-3r13.
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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)
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