Literature DB >> 22257752

Imaging plants dynamics in heterogenic environments.

Fabio Fiorani1, Uwe Rascher, Siegfried Jahnke, Ulrich Schurr.   

Abstract

Noninvasive imaging sensors and computer vision approaches are key technologies to quantify plant structure, physiological status, and performance. Today, imaging sensors exploit a wide range of the electromagnetic spectrum, and they can be deployed to measure a growing number of traits, also in heterogenic environments. Recent advances include the possibility to acquire high-resolution spectra by imaging spectroscopy and classify signatures that might be informative of plant development, nutrition, health, and disease. Three-dimensional (3D) reconstruction of surfaces and volume is of particular interest, enabling functional and mechanistic analyses. While taking pictures is relatively easy, quantitative interpretation often remains challenging and requires integrating knowledge of sensor physics, image analysis, and complex traits characterizing plant phenotypes.
Copyright © 2011 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2012        PMID: 22257752     DOI: 10.1016/j.copbio.2011.12.010

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  27 in total

1.  Image-based high-throughput field phenotyping of crop roots.

Authors:  Alexander Bucksch; James Burridge; Larry M York; Abhiram Das; Eric Nord; Joshua S Weitz; Jonathan P Lynch
Journal:  Plant Physiol       Date:  2014-09-03       Impact factor: 8.340

2.  Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale.

Authors:  Stefan Paulus; Anne-Katrin Mahlein
Journal:  Gigascience       Date:  2020-08-01       Impact factor: 6.524

3.  Phenotyping for drought tolerance of crops in the genomics era.

Authors:  Roberto Tuberosa
Journal:  Front Physiol       Date:  2012-09-19       Impact factor: 4.566

4.  HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging.

Authors:  Sergej Bergsträsser; Dimitrios Fanourakis; Simone Schmittgen; Maria Pilar Cendrero-Mateo; Marcus Jansen; Hanno Scharr; Uwe Rascher
Journal:  Plant Methods       Date:  2015-01-16       Impact factor: 4.993

5.  Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize.

Authors:  M Zaman-Allah; O Vergara; J L Araus; A Tarekegne; C Magorokosho; P J Zarco-Tejada; A Hornero; A Hernández Albà; B Das; P Craufurd; M Olsen; B M Prasanna; J Cairns
Journal:  Plant Methods       Date:  2015-06-24       Impact factor: 4.993

6.  Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses - a review.

Authors:  Jan F Humplík; Dušan Lazár; Alexandra Husičková; Lukáš Spíchal
Journal:  Plant Methods       Date:  2015-04-17       Impact factor: 4.993

7.  Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions.

Authors:  Matheus Kuska; Mirwaes Wahabzada; Marlene Leucker; Heinz-Wilhelm Dehne; Kristian Kersting; Erich-Christian Oerke; Ulrike Steiner; Anne-Katrin Mahlein
Journal:  Plant Methods       Date:  2015-04-15       Impact factor: 4.993

8.  Limits of active laser triangulation as an instrument for high precision plant imaging.

Authors:  Stefan Paulus; Thomas Eichert; Heiner E Goldbach; Heiner Kuhlmann
Journal:  Sensors (Basel)       Date:  2014-02-05       Impact factor: 3.576

9.  Automated interpretation of 3D laserscanned point clouds for plant organ segmentation.

Authors:  Mirwaes Wahabzada; Stefan Paulus; Kristian Kersting; Anne-Katrin Mahlein
Journal:  BMC Bioinformatics       Date:  2015-08-08       Impact factor: 3.169

10.  Rapid determination of leaf area and plant height by using light curtain arrays in four species with contrasting shoot architecture.

Authors:  Dimitrios Fanourakis; Christoph Briese; Johannes Fj Max; Silke Kleinen; Alexander Putz; Fabio Fiorani; Andreas Ulbrich; Ulrich Schurr
Journal:  Plant Methods       Date:  2014-04-11       Impact factor: 4.993

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