Rob2Pheno Annotated Tomato Image Dataset
doi:10.4121/13173422.v3
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future.
For a link that will always point to the latest version, please use
doi: 10.4121/13173422
doi: 10.4121/13173422
Datacite citation style:
Afonso, Manya; Fonteijn, Hubert; Polder, G. (Gerrit); Wehrens, Ron; Lensink, Dick et. al. (2021): Rob2Pheno Annotated Tomato Image Dataset. Version 3. 4TU.ResearchData. dataset. https://doi.org/10.4121/13173422.v3
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Dataset of RGBD images of tomato plants in a production greenhouse (Enza Zaden BV) obtained using Realsense cameras, and object instance level ground truth annotations of the fruit for using object detectors such as MaskRCNN or YOLACT. These data were used to obtain the results reported in our paper Tomato Fruit Detection and Counting in Greenhouses using Deep Learning, Frontiers in Plant Science, 2020.
history
- 2021-02-25 first online
- 2021-03-01 published, posted
publisher
4TU.ResearchData
associated peer-reviewed publication
Tomato Fruit Detection and Counting in Greenhouses using Deep Learning
funding
- Foundation TKI Horticulture & Propagation Materials
organizations
Mathematical & Statistical Methods, Wageningen University & ResearchENZA Zaden
DATA
files (7)
- 1,430 bytesMD5:
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README.txt - 33,498,084 bytesMD5:
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Depth.tar.gz - 161,797,185 bytesMD5:
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RGB.tar.gz - 340,491 bytesMD5:
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train_1class.JSON - 340,567 bytesMD5:
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train_2class.JSON - 157,162 bytesMD5:
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val_1class.JSON - 157,238 bytesMD5:
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val_2class.JSON -
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