Hanno Scharr

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Ulrich SchurrForschungszentrum JülichVerified email at fz-juelich.de
Sotirios TsaftarisChair in Machine Learning and Computer Vision, The University of EdinburghVerified email at ed.ac.uk
Kerstin A. NagelJPPC - Jülich Plant Phenotyping Centre, Forschungszentrum JülichVerified email at fz-juelich.de
Achim WalterProfessor of Crop Science, ETH ZürichVerified email at usys.ethz.ch
Uwe RascherProfessor of Quantitative Crop PhysiologyVerified email at fz-juelich.de
Bernd JähneSenior Professor, Physics and Computer Science, Heidelberg UniversityVerified email at iwr.uni-heidelberg.de
Siegfried JahnkeForschungszentrum JülichVerified email at fz-juelich.de
Massimo MinerviniCET Electronics, IMT LuccaVerified email at imtlucca.it
Kai KrajsekJülich Supercomputing Centre (JSC) Forschungszentrum Jülich GmbHVerified email at fz-juelich.de
Fabio FioraniForschungszentrum Jülich, IBG2 Plant SciencesVerified email at fz-juelich.de
Michael FelsbergProfessor of Computer Vision, Linköping UniversityVerified email at liu.se
Marcus JansenLemnaTec GmbHVerified email at lemnatec.de
Per-Erik ForssénAssociate Professor in Electrical Engineering, Linköping UniversityVerified email at isy.liu.se
Dagmar van DusschotenSenior Scientist at IBG-2, Plant Sciences, Forschungszentrum JuelichVerified email at planet.nl
Mario Valerio GiuffridaUniversity of NottinghamVerified email at nottingham.ac.uk
Tobias SchuchertCoupaVerified email at coupa.com
Joachim WeickertProfessor of Mathematics and Computer Science, Saarland UniversityVerified email at mia.uni-saarland.de
Mark Müller-LinowInstitute of Plant Sciences, IBG-2, Forschungszentrum JülichVerified email at fz-juelich.de
Andrew P FrenchUniversity of NottinghamVerified email at nottingham.ac.uk
Andres Chavarria-KrauserHeidelberg UniversityVerified email at bioquant.uni-heidelberg.de

Hanno Scharr

Forschungszentrum Jülich, IAS-8

Verified email at fz-juelich.de - Homepage

Computer vision machine learning plant phenotyping


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Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants M Jansen, F Gilmer, B Biskup, KA Nagel, U Rascher, A Fischbach, ... Functional Plant Biology 36 (11), 902-914, 2009	353	2009
GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons KA Nagel, A Putz, F Gilmer, K Heinz, A Fischbach, J Pfeifer, M Faget, ... Functional Plant Biology 39 (11), 891-904, 2012	342	2012
A scheme for coherence-enhancing diffusion filtering with optimized rotation invariance J Weickert, H Scharr Journal of Visual Communication and Image Representation 13 (1-2), 103-118, 2002	339	2002
Leaf segmentation in plant phenotyping: a collation study H Scharr, M Minervini, AP French, C Klukas, DM Kramer, X Liu, I Luengo, ... Machine vision and applications 27, 585-606, 2016	279	2016
Finely-grained annotated datasets for image-based plant phenotyping M Minervini, A Fischbach, H Scharr, SA Tsaftaris Pattern recognition letters 81, 80-89, 2016	271	2016
Principles of filter design B Jähne, H Scharr, S Körkel, B Jähne, H Haußecker, P Geißler Handbook of computer vision and applications 2, 125-151, 1999	262	1999
Temperature responses of roots: impact on growth, root system architecture and implications for phenotyping KA Nagel, B Kastenholz, S Jahnke, D Van Dusschoten, T Aach, ... Functional Plant Biology 36 (11), 947-959, 2009	260	2009
Optimale Operatoren in der digitalen Bildverarbeitung H Scharr Universitätsbibliothek, 2000	246*	2000
Image analysis: the new bottleneck in plant phenotyping [applications corner] M Minervini, H Scharr, SA Tsaftaris IEEE signal processing magazine 32 (4), 126-131, 2015	234	2015
A stereo imaging system for measuring structural parameters of plant canopies B Biskup, H Scharr, U Schurr, UWE Rascher Plant, cell & environment 30 (10), 1299-1308, 2007	233	2007
Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of … A Walter, H Scharr, F Gilmer, R Zierer, KA Nagel, M Ernst, A Wiese, ... New Phytologist 174 (2), 447-455, 2007	198	2007
Recovery dynamics of growth, photosynthesis and carbohydrate accumulation after de-submergence: a comparison between two wetland plants showing escape and quiescence strategies FL Luo, KA Nagel, H Scharr, B Zeng, U Schurr, S Matsubara Annals of Botany 107 (1), 49-63, 2011	137	2011
Machine Learning for Plant Phenotyping Needs Image Processing SA Tsaftaris, M Minervini, H Scharr Trends in Plant Science 1481, 2016	127	2016
Optimal filters for extended optical flow H Scharr Complex Motion, 14-29, 2007	124	2007
Channel smoothing: Efficient robust smoothing of low-level signal features M Felsberg, PE Forssén, H Scharr IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (2), 209-222, 2005	123	2005
QTL analysis of early stage heterosis for biomass in Arabidopsis RC Meyer, B Kusterer, J Lisec, M Steinfath, M Becher, H Scharr, ... Theoretical and Applied Genetics 120, 227-237, 2010	108	2010
Arigan: Synthetic arabidopsis plants using generative adversarial network M Valerio Giuffrida, H Scharr, SA Tsaftaris Proceedings of the IEEE international conference on computer vision …, 2017	107	2017
The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool M Müller-Linow, F Pinto-Espinosa, H Scharr, U Rascher Plant methods 11, 1-16, 2015	99	2015
Annotated image datasets of rosette plants H Scharr, M Minervini, A Fischbach, SA Tsaftaris European conference on computer vision. Zürich, Suisse, 6-12, 2014	90	2014
HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging S Bergsträsser, D Fanourakis, S Schmittgen, MP Cendrero-Mateo, ... Plant methods 11, 1-17, 2015	82	2015

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