title: Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions.
abstract: BACKGROUND
  Plant growth is a good indicator of crop performance and can be measured by different methods and on different spatial and temporal scales. In this study, we measured the canopy height growth of maize (Zea mays), soybean (Glycine max) and wheat (Triticum aestivum) under field conditions by terrestrial laser scanning (TLS). We tested the hypotheses whether such measurements are capable to elucidate (1) differences in architecture that exist between genotypes; (2) genotypic differences between canopy height growth during the season and (3) short-term growth fluctuations (within 24 h), which could e.g. indicate responses to rapidly fluctuating environmental conditions. The canopies were scanned with a commercially available 3D laser scanner and canopy height growth over time was analyzed with a novel and simple approach using spherical targets with fixed positions during the whole season. This way, a high precision of the measurement was obtained allowing for comparison of canopy parameters (e.g. canopy height growth) at subsequent time points.
  
  
  RESULTS
  Three filtering approaches for canopy height calculation from TLS were evaluated and the most suitable approach was used for the subsequent analyses. For wheat, high coefficients of determination (R(2)) of the linear regression between manually measured and TLS-derived canopy height were achieved. The temporal resolution that can be achieved with our approach depends on the scanned crop. For maize, a temporal resolution of several hours can be achieved, whereas soybean is ideally scanned only once per day, after leaves have reached their most horizontal orientation. Additionally, we could show for maize that plant architectural traits are potentially detectable with our method.
  
  
  CONCLUSIONS
  The TLS approach presented here allows for measuring canopy height growth of different crops under field conditions with a high temporal resolution, depending on crop species. This method will enable advances in automated phenotyping for breeding and precision agriculture applications. In future studies, the TLS method can be readily applied to detect the effects of plant stresses such as drought, limited nutrient availability or compacted soil on different genotypes or on spatial variance in fields.
authors.affiliation: Institute of Agricultural Sciences, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland.
authors.forename: Michael
authors.initials: M
authors.lastname: Friedli
authors.affiliation: Institute of Agricultural Sciences, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland.
authors.forename: Norbert
authors.initials: N
authors.lastname: Kirchgessner
authors.affiliation: Institute of Agricultural Sciences, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland.
authors.forename: Christoph
authors.initials: C
authors.lastname: Grieder
authors.affiliation: Institute of Agricultural Sciences, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland.
authors.forename: Frank
authors.initials: F
authors.lastname: Liebisch
authors.affiliation: Institute of Agricultural Sciences, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland.
authors.forename: Michael
authors.initials: M
authors.lastname: Mannale
authors.affiliation: Institute of Agricultural Sciences, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland.
authors.forename: Achim
authors.initials: A
authors.lastname: Walter
keywords: Canopy height growth; Laser scanning; Maize; Scan point cloud; Soybean; Wheat
references.citation: Sensors (Basel). 2013 Oct 29;13(11):14662-75
references.pmid: 24172283
references.citation: Funct Plant Biol. 2008 Dec;35(10):900-910
references.pmid: 32688841
references.citation: Plant Methods. 2015 Mar 04;11:14
references.pmid: 25767559
references.citation: Sensors (Basel). 2014 Jul 15;14(7):12670-86
references.pmid: 25029283
references.citation: Funct Plant Biol. 2015 Apr;42(4):387-396
references.pmid: 32480683
references.citation: Sensors (Basel). 2011;11(2):2177-94
references.pmid: 22319405
references.citation: J Exp Bot. 2010 Jun;61(6):1751-9
references.pmid: 20299442
references.citation: Funct Plant Biol. 2009 Nov;36(11):998-1005
references.pmid: 32688711
references.citation: Sensors (Basel). 2014 Jan 07;14(1):900-26
references.pmid: 24451465
references.citation: J Exp Bot. 2007;58(4):881-98
references.pmid: 17030540
references.citation: BMC Plant Biol. 2012 May 03;12:63
references.pmid: 22553969
references.citation: Plant Cell Environ. 2015 Mar;38(3):514-24
references.pmid: 25041284
references.citation: J Plant Res. 2005 Oct;118(5):343-6
references.pmid: 16136362
references.citation: Sensors (Basel). 2011;11(2):2166-2174
references.pmid: 22319403
references.citation: BMC Bioinformatics. 2013 Jul 27;14:238
references.pmid: 23890277
references.citation: Annu Rev Plant Biol. 2009;60:279-304
references.pmid: 19575584
references.citation: Sensors (Basel). 2014 Dec 16;14(12):24212-30
references.pmid: 25521383
references.citation: Sensors (Basel). 2015 Jun 09;15(6):13533-47
references.pmid: 26066990
references.citation: Plant Cell Environ. 2007 Oct;30(10):1299-308
references.pmid: 17727419
references.citation: Plant J. 2004 Apr;38(2):358-65
references.pmid: 15078337
references.citation: Crit Rev Biotechnol. 2014 Mar;34(1):16-30
references.pmid: 23586682