% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@ARTICLE{Scharr:817744,
author = {Scharr, Hanno and Dee, Hannah and French, Andrew P. and
Tsaftaris, Sotirios A.},
title = {{S}pecial issue on computer vision and image analysis in
plant phenotyping},
journal = {Machine vision and applications},
volume = {27},
number = {5},
issn = {1432-1769},
address = {Berlin},
publisher = {Springer},
reportid = {FZJ-2016-04387},
pages = {607 - 609},
year = {2016},
note = {Editorial of the special issue},
abstract = {Plant phenotyping is the identification of effects on the
phenotype (i.e., the plant appearance and behavior) as a
result of genotype differences (i.e., differences in the
genetic code) and the environment. Previously, the process
of taking phenotypic measurements has been laborious,
costly, and time-consuming. In recent years, noninvasive,
imaging-based methods have become more common. These images
are recorded by a range of capture devices from small
embedded camera systems to multi-million Euro smart
greenhouses, at scales ranging from microscopic images of
cells, to entire fields captured by UAV imaging.},
cin = {IBG-2},
ddc = {004},
cid = {I:(DE-Juel1)IBG-2-20101118},
pnm = {582 - Plant Science (POF3-582)},
pid = {G:(DE-HGF)POF3-582},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000379708400001},
doi = {10.1007/s00138-016-0787-1},
url = {https://juser.fz-juelich.de/record/817744},
}