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001 | 859907 | ||
005 | 20230711153413.0 | ||
024 | 7 | _ | |2 doi |a 10.1016/j.tplants.2018.10.016 |
024 | 7 | _ | |2 ISSN |a 1360-1385 |
024 | 7 | _ | |2 ISSN |a 1878-4372 |
024 | 7 | _ | |2 pmid |a pmid:30497879 |
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024 | 7 | _ | |2 Handle |a 2128/23627 |
037 | _ | _ | |a FZJ-2019-00723 |
041 | _ | _ | |a English |
082 | _ | _ | |a 570 |
100 | 1 | _ | |0 P:(DE-HGF)0 |a Tsaftaris, Sotirios A. |b 0 |e Corresponding author |
245 | _ | _ | |a Sharing the Right Data Right: A Symbiosis with Machine Learning |
260 | _ | _ | |a Amsterdam [u.a.] |b Elsevier Science |c 2019 |
336 | 7 | _ | |2 DRIVER |a article |
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520 | _ | _ | |a 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. |
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700 | 1 | _ | |0 P:(DE-Juel1)129394 |a Scharr, Hanno |b 1 |e Corresponding author |u fzj |
773 | _ | _ | |0 PERI:(DE-600)2011003-0 |a 10.1016/j.tplants.2018.10.016 |g p. S1360138518302498 |n 2 |p P99-102 |t Trends in plant science |v 24 |x 1360-1385 |y 2019 |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/859907/files/PLANTS-D-18-00245_Post_Print.pdf |y OpenAccess |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/859907/files/PLANTS-D-18-00245_Post_Print.pdf?subformat=pdfa |x pdfa |y OpenAccess |
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