Data

QUT-HIA-DAF Capsicum Datasets

Queensland University of Technology
Halstead, Michael ; Denman, Simon ; Fookes, Clinton ; McCool, Chris
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25912/RDF_1610071192071&rft.title=QUT-HIA-DAF Capsicum Datasets&rft.identifier=10.25912/RDF_1610071192071&rft.publisher=Queensland University of Technology&rft.description=Datasets containing imagery of capsicums, captured under direct sunlight in a field condition and polytunnel environments. Intended for use training machine learning algorihtms for detection, classification and counting of fruit. Data is made available with the paper: Michael Halstead, Simon Denman, Clinton Fookes, Chris McCool, Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar, DICTA, 2020. &rft.creator=Halstead, Michael &rft.creator=Denman, Simon &rft.creator=Fookes, Clinton &rft.creator=McCool, Chris &rft.date=2020&rft.edition=1&rft.coverage=153.649789,-9.210063 137.995957,-9.210063 137.995957,-29.177898 153.649789,-29.177898 153.649789,-9.210063&rft_rights=© Queensland University of Technology, 2020.&rft_rights=Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/4.0/&rft_subject=Agricultural Robots&rft_subject=Computer Vision&rft_subject=Machine Learning&rft.type=dataset&rft.language=English Access the data
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Licence & Rights:

Open Licence view details
CC-BY

Creative Commons Attribution 3.0
http://creativecommons.org/licenses/by/4.0/

© Queensland University of Technology, 2020.

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The owners of the dataset request that users of the dataset cite the following paper:

Michael Halstead, Simon Denman, Clinton Fookes, Chris McCool, "Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar", DICTA, 2020.

Contact Information

Postal Address:
Dr Simon Denman
Ph: +61731389329

s.denman@qut.edu.au

Full description

Datasets containing imagery of capsicums, captured under direct sunlight in a field condition and polytunnel environments. Intended for use training machine learning algorihtms for detection, classification and counting of fruit.

Data is made available with the paper:

Michael Halstead, Simon Denman, Clinton Fookes, Chris McCool, "Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar", DICTA, 2020.

Data time period: 2017 to 31 12 2018

This dataset is part of a larger collection

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153.64979,-9.21006 137.99596,-9.21006 137.99596,-29.1779 153.64979,-29.1779 153.64979,-9.21006

145.822873,-19.1939805

Subjects
Agricultural Robots | Computer Vision | Machine Learning |

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