This title appears in the Scientific Report :
2019
Please use the identifier:
http://hdl.handle.net/2128/23627 in citations.
Please use the identifier: http://dx.doi.org/10.1016/j.tplants.2018.10.016 in citations.
Sharing the Right Data Right: A Symbiosis with Machine Learning
Sharing the Right Data Right: A Symbiosis with Machine Learning
In 2014 plant phenotyping research was not benefiting from the machine learning (ML) revolution because appropriate data were lacking. We report the success of the first open-access data-set suitable for ML in image-based plant phenotyping suitable for machine learning, fuelling a true inte...
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Personal Name(s): | Tsaftaris, Sotirios A. (Corresponding author) |
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Scharr, Hanno (Corresponding author) | |
Contributing Institute: |
Pflanzenwissenschaften; IBG-2 |
Published in: | Trends in plant science, 24 (2019) 2, S. P99-102 |
Imprint: |
Amsterdam [u.a.]
Elsevier Science
2019
|
PubMed ID: |
30497879 |
DOI: |
10.1016/j.tplants.2018.10.016 |
Document Type: |
Journal Article |
Research Program: |
Innovative Synergisms |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1016/j.tplants.2018.10.016 in citations.
In 2014 plant phenotyping research was not benefiting from the machine learning (ML) revolution because appropriate data were lacking. We report the success of the first open-access data-set suitable for ML in image-based plant phenotyping suitable for machine learning, fuelling a true interdisciplinary symbiosis, increased awareness, and steep performance improvements on key phenotyping tasks. |