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
Uwe RascherProfessor of Quantitative Crop PhysiologyVerified email at fz-juelich.de
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
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
Mark Müller-LinowInstitute of Plant Sciences, IBG-2, Forschungszentrum JülichVerified email at fz-juelich.de
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
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	359	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	349	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	340	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	288	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	282	2016
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	266	2009
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
Optimale Operatoren in der digitalen Bildverarbeitung H Scharr Universitätsbibliothek, 2000	248*	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	241	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	236	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	200	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	140	2011
Machine Learning for Plant Phenotyping Needs Image Processing SA Tsaftaris, M Minervini, H Scharr Trends in Plant Science 1481, 2016	134	2016
Optimal filters for extended optical flow H Scharr Complex Motion, 14-29, 2007	125	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	122	2005
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	112	2017
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
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	102	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	93	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	87	2015

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