The authors acknowledge funding from the Austrian Science Fund FWF via the Project Number P 25190-B16 (The roots of drought resistance). Establishment of the hyperspectral imaging infrastructure was supported financially by the Federal Government of Lower Austria (Land Nieder&#246;sterreich) via the project K3-F-282/001-2012. Additional funding for the sugar beet experiment was received from AGRANA Research &#38; Innovation Center GmbH (ARIC). The authors thank Craig Jackson for technical support during the experiment and English correction of the manuscript. We also acknowledge Markus Freudhofmaier who contributed to establishment the RGB imaging setup and Josef Schodl for construction of the rhizobox mounting.

<ol>
	<li>Kutschera, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Wurzelatlas mitteleurop&#228;ischer Ackerunkr&#228;uter und Kulturpflanzen. , (1960).</li><li>Kenrick, P., Strullu-Derrien, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+origin+and+early+evolution+of+roots.">The origin and early evolution of roots.</a> Plant Physiol. 166, 570-580 (2014).</li><li>Franklin, J., Serra-Diaz, J. M., Syphard, A. D., Regan, H. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+change+and+terrestrial+plant+community+dynamics.">Global change and terrestrial plant community dynamics.</a> PNAS. 113, 3725-3734 (2016).</li><li>MacDonald, G. K., Bennett, E. M., Potter, P. A., Ramankutty, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Agronomic+phosphorus+imbalances+across+the+world's+croplands.">Agronomic phosphorus imbalances across the world's croplands.</a> PNAS. 108, 3086-3091 (2011).</li><li>V&#246;r&#246;smarty, C. J., Green, P., Salisbury, J., Lammers, R. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+water+resources:+vulnerability+from+climate+change+and+population+growth.">Global water resources: vulnerability from climate change and population growth.</a> Science. 289, 284 (2000).</li><li>Lobell, D. B., Cassman, K. G., Field, C. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Crop+yield+gaps:+their+importance,+magnitudes,+and+causes.">Crop yield gaps: their importance, magnitudes, and causes.</a> Annu. Rev. Environ. Resour. 34, 179-204 (2009).</li><li>Angus, J. F., Van Herwaarden, A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increasing+water+use+and+water+use+efficiency+in+dryland+wheat.">Increasing water use and water use efficiency in dryland wheat.</a> Agron. J. 93, 290-298 (2001).</li><li>Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J., Garnier, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=50+year+trends+in+nitrogen+use+efficiency+of+world+cropping+systems:+the+relationship+between+yield+and+nitrogen+input+to+cropland.">50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland.</a> Environ. Res. Lett. 9 (10), 105011 (2014).</li><li>Lynch, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Roots+of+the+second+green+revolution.">Roots of the second green revolution.</a> Aust. J Bot. 55, 493-512 (2007).</li><li>Comas, L. H., Becker, S. R., Von Mark, V. C., Byrne, P. F., Dierig, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Root+traits+contributing+to+plant+productivity+under+drought.">Root traits contributing to plant productivity under drought.</a> Front. Plant Sci. 4, 442 (2013).</li><li>Metcalfe, D. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+method+for+extracting+plant+roots+from+soil+which+facilitates+rapid+sample+processing+without+compromising+measurement+accuracy.">A method for extracting plant roots from soil which facilitates rapid sample processing without compromising measurement accuracy.</a> New Phytol. 174, 697-703 (2007).</li><li>Kutschera, L., Lichtenegger, E., Sobotik, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Wurzelatlas der Kulturpflanzen gem&#228;&#223;igter Gebiete mit Arten des Feldgem&#252;sebaues. , (2009).</li><li>Gioia, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GrowScreen-PaGe,+a+non-invasive,+high-throughput+phenotyping+system+based+on+germination+paper+to+quantify+crop+phenotypic+diversity+and+plasticity+of+root+traits+under+varying+nutrient+supply.">GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system based on germination paper to quantify crop phenotypic diversity and plasticity of root traits under varying nutrient supply.</a> Funct. Plant Biol. 44, 76-93 (2017).</li><li>Watt, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+rapid,+controlled-environment+seedling+root+screen+for+wheat+correlates+well+with+rooting+depths+at+vegetative,+but+not+reproductive,+stages+at+two+field+sites.">A rapid, controlled-environment seedling root screen for wheat correlates well with rooting depths at vegetative, but not reproductive, stages at two field sites.</a> Ann. Bot. 112, 447-455 (2013).</li><li>Fiorani, F., Schurr, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Future+scenarios+for+plant+phenotyping.">Future scenarios for plant phenotyping.</a> Annu. Rev Plant Biol. 64, 267-291 (2013).</li><li>Metzner, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Direct+comparison+of+MRI+and+X-ray+CT+technologies+for+3D+imaging+of+root+systems+in+soil:+potential+and+challenges+for+root+trait+quantification.">Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: potential and challenges for root trait quantification.</a> Plant Methods. 11, 1 (2015).</li><li>Choat, B., Badel, E., Burlett, R., Delzon, S., Cochard, H., Jansen, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-invasive+measurement+of+vulnerability+to+drought+induced+embolism+by+X-ray+microtomography.">Non-invasive measurement of vulnerability to drought induced embolism by X-ray microtomography.</a> Plant Physiol. 170, 273-282 (2015).</li><li>Metzner, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Direct+comparison+of+MRI+and+X-ray+CT+technologies+for+3D+imaging+of+root+systems+in+soil:+potential+and+challenges+for+root+trait+quantification.">Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: potential and challenges for root trait quantification.</a> Plant Methods. 11, 1 (2015).</li><li>Le Mari&#233;, C., Kirchgessner, N., Marschall, D., Walter, A., Hund, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rhizoslides:+paper-based+growth+system+for+non-destructive,+high+throughput+phenotyping+of+root+development+by+means+of+image+analysis.">Rhizoslides: paper-based growth system for non-destructive, high throughput phenotyping of root development by means of image analysis.</a> Plant Methods. 10, 1 (2014).</li><li>Bengough, A. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gel+observation+chamber+for+rapid+screening+of+root+traits+in+cereal+seedlings.">Gel observation chamber for rapid screening of root traits in cereal seedlings.</a> Plant Soil. 262, 63-70 (2004).</li><li>Nagel, K. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GROWSCREEN-Rhizo+is+a+novel+phenotyping+robot+enabling+simultaneous+measurements+of+root+and+shoot+growth+for+plants+grown+in+soil-filled+rhizotrons.">GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons.</a> Funct. Plant Biol. 39, 891-904 (2012).</li><li>Price, A. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Upland+rice+grown+in+soil-filled+chambers+and+exposed+to+contrasting+water-deficit+regimes:+I.+Root+distribution,+water+use+and+plant+water+status.">Upland rice grown in soil-filled chambers and exposed to contrasting water-deficit regimes: I. Root distribution, water use and plant water status.</a> Field Crops Res. 76, 11-24 (2002).</li><li>Vadez, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Root+hydraulics:+the+forgotten+side+of+roots+in+drought+adaptation.">Root hydraulics: the forgotten side of roots in drought adaptation.</a> Field Crops Res. 165, 15-24 (2014).</li><li>Dane, J. H., Hopmans, J. W., Dane, J. H., Topp, G. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pressure+plate+extractor.">Pressure plate extractor.</a> Methods of soil analysis. Part 4. Physical methods. , 688-690 (2002).</li><li>W&#246;sten, J. H. M., Pachepsky, Y. A., Rawls, W. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pedotransfer+functions:+bridging+the+gap+between+available+basic+soil+data+and+missing+soil+hydraulic+characteristics.">Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics.</a> J. Hydrol. 251, 123-150 (2001).</li><li>Passioura, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+perils+of+pot+experiments.">The perils of pot experiments.</a> Funct. Plant Biol. 33, 1075-1079 (2006).</li><li>Dorrepaal, R., Malegori, C., Gowen, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tutorial:+Time+series+hyperspectral+image+analysis.">Tutorial: Time series hyperspectral image analysis.</a> J. Near Infrared Spec. 24, 89-107 (2016).</li><li>Kim, D. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+sensitive+image-derived+indices+of+water-stressed+plants+using+hyperspectral+imaging+in+SWIR+and+histogram+analysis.">Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis.</a> Scie rep. 5, (2015).</li><li>Humpl&#237;k, J. F., Laz&#225;r, D., Husi&#269;kov&#225;, A., Sp&#237;chal, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+phenotyping+of+plant+shoots+using+imaging+methods+for+analysis+of+plant+stress+responses-a+review.">Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses-a review.</a> Plant Methods. 11, 29 (2015).</li><li>Lobell, D. B., Asner, G. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Moisture+effects+on+soil+reflectance.">Moisture effects on soil reflectance.</a> Soil Sci. Soc. Am. J. 66, 722-727 (2002).</li><li>Esquerre, C., Gowen, A. A., Burger, J., Downey, G., O'Donnell, C. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Suppressing+sample+morphology+effects+in+near+infrared+spectral+imaging+using+chemometric+data+pre-treatments.">Suppressing sample morphology effects in near infrared spectral imaging using chemometric data pre-treatments.</a> Chemometr. Intell. Lab. 117, 129-137 (2012).</li><li>Williams, P. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Localized+flux+maxima+of+arsenic,+lead,+and+iron+around+root+apices+in+flooded+lowland+rice.">Localized flux maxima of arsenic, lead, and iron around root apices in flooded lowland rice.</a> Environ Sci. Technol. 48, 8498-8506 (2014).</li><li>Cseresny&#233;s, I., Tak&#225;cs, T., V&#233;gh, K. R., Anton, A., Rajkai, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electrical+impedance+and+capacitance+method:+a+new+approach+for+detection+of+functional+aspects+of+arbuscular+mycorrhizal+colonization+in+maize.">Electrical impedance and capacitance method: a new approach for detection of functional aspects of arbuscular mycorrhizal colonization in maize.</a> Eur. J Soil Biol. 54, 25-31 (2013).</li><li>Brown, M., Lowe, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+panoramic+image+stitching+using+invariant+features.">Automatic panoramic image stitching using invariant features.</a> Int. J. Comput. Vision. 74, 59-73 (2007).</li><li>Jiang, Y., Li, C., Takeda, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nondestructive+detection+and+quantification+of+blueberry+bruising+using+near-infrared+(NIR)+hyperspectral+reflectance+imaging.">Nondestructive detection and quantification of blueberry bruising using near-infrared (NIR) hyperspectral reflectance imaging.</a> Scientific Reports. 6, 35679 (2016).</li><li>Chang, C. -. W., Laird, D. A., Mausbach, M. J., Hurburgh, C. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Near-infrared+reflectance+spectroscopy-principal+components+regression+analyses+of+soil+properties.">Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties.</a> Soil Sci. Soc. Am. J. 65, 480-490 (2001).</li><li>Leitner, D., Felderer, B., Vontobel, P., Schnepf, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recovering+root+system+traits+using+image+analysis+exemplified+by+two-dimensional+neutron+radiography+images+of+lupine.">Recovering root system traits using image analysis exemplified by two-dimensional neutron radiography images of lupine.</a> Plant Physiol. 164 (1), 24-35 (2014).</li></ol>

The authors have nothing to disclose.

The protocols provide two complementary approaches for soil grown root system imaging. A critical step for reliable experimental results is filling of the rhizoboxes that has to ensure an even and homogeneous substrate layer at the front glass to provide tight root-soil contact at the observation window and avoid air gaps. This is the main reason to use comparatively fine sieved soil of &#60; 2 mm: Larger aggregates result in higher surface morphology at the observation window with voids between aggregates. Besides a higher risk of root tip dehydration, this also requires more complex image processing techniques for water mapping31.
Modifications of the protocol therefore focus on improved and quick filling of rhizoboxes. Currently filling time is about 30 minutes per box. Furthermore use of rhizoboxes with two glass windows for imaging from both sides and modifications to optimize illumination homogeneity for better RGB images are tested. Further hardware extension might also consider integration of planar optodes32 as well as capacitance imaging33 into the rhizobox system. This however is beyond current upgrading activities.
Software modifications focus on automatic image registration to fuse the top and bottom RBG images34. For hyperspectral imaging advanced unsupervised feature extraction approaches28 as well as more sensitive supervised target detection methods such as SVMs35 are tested. Thereby the hyperspectral data potentially allow for the assessment of multiple soil, rhizosphere and root properties36. Furthermore it is intended to develop a (semi)automatized software for rhizobox root images based on a modified version of Root System Analyzer37 to quantify morphological (length, diameter, surface) as well as architectural traits (branching frequency, branching angles).
The main limitation of the protocol compared to 3D imaging approaches is the restriction to the surface visible root and rhizosphere properties. However it has been demonstrated that the visible root traits are a reliable proxy for the whole root system21. The rhizobox technique is easily combined with traditional destructive sampling (washing) at the end of dynamic growth imaging in order to validate the relation of visible vs. total root system traits. As this relation might vary among species21, destructive sampling is recommended to ensure reliable inference from visible traits for any new phenotyping series with a different crop species.
The key advantage of the protocol presented here is the combination of realistic growing conditions (soil), relatively high potential throughput for the temporally resolved RGB imaging and inference on root functionality (e.g. water uptake) via the chemometric root and rhizosphere data from hyperspectral imaging. Thereby the methods overcomes inference restrictions in high throughput seedling and non-soil root imaging methods14, while it partially allows deep phenotyping insights into functional processes with less experimental complexity and higher throughput compared to advanced 3D methods15.
In upcoming experiments the protocol will be used to study the effect of mycorrhiza on root system development and functionality of legumes as well as for phenotyping root characteristics of cover crop species in relation to soil structure, nitrogen and carbon cycling.

Roots provide several essential functions for plants such as storage of assimilates, anchorage of terrestrial plants in soil, and uptake and transport of water and nutrients1. From an evolutionary point of view, the formation of root axes is considered a fundamental precondition for the origin of land plants2. In spite of this important role, historically roots have occupied only a marginal position in biological research. In more recent times, however, there is increasing scientific interest in plant root systems as evidenced in Figure 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56251/56251fig1.jpg" /> 
Figure 1: Relevance of root studies in plant sciences. 
Number of root related studies as a percentage of all published plant studies in SCI journals over the last decades. Search result from Scopus using keywords "plant" and "plant AND root". <a href="//cloudflare.jove.com/files/ftp_upload/56251/56251fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Two main reasons can be hypothesized to underlie the recent advances in root research. First, terrestrial vegetation is exposed to more frequent environmental stresses as a result of global change3. In the context of agricultural crop production it is estimated that globally around 30% of the agricultural area are limited by water and phosphorus4,5. Stress reduction of crop yields are a main reason for significant yield gaps that are globally estimated at lower 50% of potential productivity for rainfed agro-ecosystems6. Besides low resource availability, this is also related to poor resource use efficiency, i.e. insufficient capacity of a plant to exploit available resources7. This results in losses of mobile resources such as nitrate which can negatively affect other ecosystems. The current global nitrogen use efficiency for example is estimated at 47%8. Better resource use efficiency via improved management methods and cultivars is therefore of high importance for both sustained growth of agricultural outputs as well as for environmental sustainability. In this context plant roots are considered to be a key target for improved crops and cropping systems9,10.
A second important background for the recent interest in plant roots is technological advance in measurement methods. Root methods have long been restricted by two key challenges: for measurement of roots from plants growing in soil they had to be isolated for quantification, mostly by washing11, thereby disturbing the architectural arrangement of root axes. In-situ root observation using excavation methods, thereby conserving the natural location of roots in soil, have been used for botanical description12. Still they are very time-consuming and thus do not meet the throughput requirements of comparative structural-functional root system analysis. On the other hand high-throughput methods for root architecture measurement were mostly done on artificial media and for seedling plants13 where the extrapolation to the natural growth environment of plants is questionable14.
The recent boom of root research is tightly linked to the advance in imaging methods15. Imaging approaches in root studies can be roughly grouped into three types. First there are high resolution 3D methods such as CT and MRI16. These methods are most suitable to study interaction processes of plant roots with soil, such as drought induced xylem embolism17. Typically they are applied to comparatively small samples where they allow detailed observations. A comparison of CT and MRI for differently sized pots and fine root imaging is provided in18. Second, there are high-throughput imaging methods19,20. These methods are mostly based on common 2D RGB imaging of roots growing on artificial media (gel, germination paper) where high contrast allows comparatively simple dissection between roots and background. They are appropriate for high throughput comparison among seedling root traits of different crop genotypes under standardized artificial growing conditions13. In between these two approaches are rhizobox methods: they use 2D imaging of roots growing in soil over longer time period and have medium throughput21,22. A recent challenge to (2D) root imaging is to capture also indicators of root functionality in addition to description of structure23.
In the present paper we present the experimental protocols for imaging rhizobox grown root systems using (i) a cheap and simple custom-made RGB imaging setup and (ii) a more complex NIR imaging setup. Example results obtained from these two setups are shown and discussed in the context of plant phenotyping and plant physiological research.

<table><tbody><tr><td>Rhizobox</td><td>Technisches B&amp;uuml;ro f&amp;uuml;r Bodenkultur</td><td>Experimental builder</td><td></td></tr><tr><td>LED Lamps ATUM HORTI 600</td><td>Klutronic</td><td>AHI10600F</td><td></td></tr><tr><td>Fluorescent light tube HiLite T5 Day</td><td>Juwel Aquarium</td><td>86324</td><td></td></tr><tr><td>UV light tube Eurolite 45cm slim 15 W</td><td>Conrad</td><td>593384 - 62</td><td></td></tr><tr><td>Canon EOS 6D</td><td>Canon Austria GmbH</td><td>8035B024</td><td></td></tr><tr><td>Adobe Photoshop CS5 Extended Version 12.0 x 32</td><td>Adobe Systems Software Ireland Ltd.</td><td></td><td></td></tr><tr><td>WinRhizo Pro v. 2013</td><td>Regent Instruments Inc.</td><td></td><td></td></tr><tr><td>Xeva-1.7-320 SWIR camera</td><td>Xenics</td><td>XEN-000105</td><td></td></tr><tr><td>Spectrograph Imspector N25E</td><td>Specim</td><td></td><td></td></tr><tr><td>Hyperspectral imaging scanner</td><td>Carinthian Tech Research AG</td><td>Experimental builder</td><td>Design and assemblage of Hyperspectral Imaging Scanner and software</td></tr><tr><td>Matlab R2106a</td><td>Mathworks</td><td></td><td>Including Toolboxes for Image Processing, Signal Processing and Statistics and Machine Learning</td></tr><tr><td>AP4 Poromoeter</td><td>Delta-T-Devices</td><td></td><td></td></tr><tr><td>LI-3100C Area Meter</td><td>LI-COR</td><td></td><td></td></tr><tr><td>BASF Styradur polystyrene sheets</td><td>Obi Baumarkt</td><td>9706318</td><td>Different types of polystyrene sheets or other material separating differently moistured soil can be used</td></tr></tbody></table>

rgb and spectral root imaging for plant phenotyping and physiological research: experimental setup and imaging protocols

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

Example results are presented for root segmentation based on RGB and HS imaging. For spectral imaging an example of high resolution water mapping is provided. Finally results are shown that demonstrate the scientific context where image based root data are applied.
RGB based root measurement
Figure 8 shows an RGB root image time series of sugar beet cultivar Ferrara. The images reveal some artefacts from inhomogeneous illumination of the rhizoboxes, with brighter areas along the left side and different brightness at the overlapping area between top and bottom images.
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/56251/56251fig8.jpg" /> 
Figure 8: Root growth time series from the RGB imaging. 
Pictures show the sugar beet cultivar Ferrara at different days after sowing (DAS). The images show some artefacts due to non-homogeneous illumination at the left side of the image and between top and bottom images. Scale bars, 2 cm. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure 9 provides details on root segmentation based on color thresholding for cultivar Ferrara at day after sowing (DAS) 35. As a reference (Figure 9A), a binary image is used where all roots were manually tracked with a Graphic Tablet. The time required for manual tracking of the entire, fully developed, dense sugar beet root system was around four hours. Figure 9B gives a detailed view on a selected area at the top of the image where old lateral roots are present. Here several root axes are not classified by the color threshold. At the bottom (Figure 9C) on the contrary, where white young roots are predominant, the color based segmentation properly classifies all root axes. The binarized root system (Figure 9D) shows a black area at the left side from the illumination artefact which was defined as exclusion region before running quantitative analysis. Figure 9E shows the corresponding pixel histograms of selected features (roots vs. soil) for the red channel of the RGB image from Ferrara at DAS 35. The root pixels (blue color) clearly show three peaks corresponding to bright young laterals, dark old laterals and tap root. The overlap between the old laterals and the soil background is very strong, leading to unclassified root axes (cf. Figure 9B).
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/56251/56251fig9.jpg" /> 
Figure 9: Root segmentation using a color threshold. 
(A) Manually segmented root system using a Graphic Tablet, (B) area with poorly segmented old root axes in the top and (C) properly segmented young axes in the bottom of the image, and (D) binary image obtained from color based thresholding. (E) Pixel histograms for selected features of the RGB image. Roots are represented by the blue bars with different root types indicated; soil is represented by the red bars. Scale bars in A and D, 2 cm; scale bars in B and C, 1 cm. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The resulting total visible root length quantified for the manually segmented reference image is 1534.1 cm, while the automatized, color based segmentation gives a total root length of 1427.6 cm.
Greyscale images from UV-illumination do not provide an advantage in the case shown here and performed worse compared to color thresholding (root length: 1679.7 cm). Old roots could not be segmented, and there was more noise in the image, probably due to lower light intensity of the UV lamps. However, in case of young roots with high auto-fluorescence and a bright background substrate, UV-illumination can still be an option as shown by an image obtained from another experiment where sand was used as background substrate (Figure 10).
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/56251/56251fig10.jpg" /> 
Figure 10: UV illumination to visualize roots on bright background. 
Example from a durum wheat root system growing in a rhizobox filled with quartz sand. The rhizobox is imaged with illumination for (A) UV light and (B) fluorescent (day) light. Scale bars, 2 cm. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
HSI based root measurements
Figure 11 provides the mean spectra for three root ROIs (old and young lateral, tap root) and two soil ROIs (top and bottom of rhizobox).
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/56251/56251fig11.jpg" /> 
Figure 11: Mean spectra of root and soil. 
Spectra from regions of interest (ROIs) on the root (three root types) and in the soil (top and bottom of the rhizobox). The ROIs are selected to determine an optimum segmentation criterion between root and soil. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
It is evident that the tap and young lateral roots differ substantially from the background in intensity of most spectral bands. For the old laterals the intensity differences are much lower. A feature that can be inferred visually is the different slope of the spectrum around water absorption region (1450 nm). Here the slope of root spectra is higher compared to soil spectra. Furthermore a change of tap and young lateral spectra in the region around 1100 nm can be identified that does not occur in the old laterals.
Figure 12A shows the result from the search algorithm identifying a spectral ratio with strongest foreground-background contrast. The ratio of spectra at 1476 nm to 1076 nm provides the best separation between roots and soil. The resulting histogram of root foreground and soil background pixels is shown in Figure 12B. Although there is some overlap, most pixels are clearly separated from the soil background. Fitting a bimodal Gaussian curve through the histogram and using Bhattacharyya distance for quantification, a value of 7.80 is obtained. A value higher 3.0 indicates strong image contrast allowing reliable separation28.
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/56251/56251fig12.jpg" /> 
Figure 12: Difference in reflectance between root foreground and soil background for different spectral band ratios and pixel histogram at spectral ratio used for segmentation. 
(A) Bright colors (yellow) show high contrast between foreground and background, dark colors (blue) show low contrast. The first 15 bands have been removed because of noise. The red lines indicate the band ratio with highest contrast. (B) Pixel histogram of roots (blue) and soil (red) at segmentation spectral ratio. Blue bars represent the root and red bars the soil. The intensity value corresponds to the ratio of spectral band 160 to spectral band 49. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The binary image (Figure 13) is created by applying a global intensity threshold of the identified spectral ratio at a value of 1.008 calculated from the histogram distance27. Analysis of root length of this image gives a total length of 1557.3 cm which represents an error of only 1.5% compared to the manually tracked reference image.
<img alt="Figure 13" class="xfigimg" src="/files/ftp_upload/56251/56251fig13.jpg" /> 
Figure 13: Binary image of the root system of sugar beet cultivar Ferrara. 
The image is obtained by applying a global spectral threshold. Scale bar (bottom left corner), 2 cm. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig13large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Although root segmentation has improved using spectral information compared to color based information, the main intention of HS imaging is analysis of chemometric image properties. This is exemplified via mapping the water content of a rhizobox image.
Figure 14A shows the mean spectra of the compartments in the calibration rhizobox (cf. Figure 7) filled with soil of different water content. The shape of spectra is similar between the compartments, i.e. here a spectral ratio does not necessarily provide a more stable classification criterion. Thus intensity at a single spectral band (1680 nm), where the average difference between adjacent water contents is maximized, is identified as best separating criterion. The resulting pixel histograms for this spectral wavelength are shown in Figure 14B.
<img alt="Figure 14" class="xfigimg" src="/files/ftp_upload/56251/56251fig14.jpg" /> 
Figure 14: Spectral features for water content calibration. 
(A) Mean spectra of nine water compartments from the calibration rhizobox with different water contents; (B) Pixel histograms for the water compartments at band 216 where average distance between neighboring compartment is maximum. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig14large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The relation of the average pixel intensity at 1680 nm and the measured water content is shown in Figure 15.
<img alt="Figure 15" class="xfigimg" src="/files/ftp_upload/56251/56251fig15.jpg" /> 
Figure 15: Relation of spectral reflectance and volumetric water content. 
The figure shows data pairs of measured water content and spectral reflectance with empirical curves (linear and exponential) fit to the data excluding the highest water contents (red triangles). <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig15large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Differentiation of higher water contents from spectral intensity becomes difficult. A significant regression (either linear or exponential) with high R2 can be fit to water contents up to around 0.30 cm3 cm-3. Wetter soil conditions cannot be reliably predicted by the intensity value. Similar behavior of an exponential relation between reflectivity and water content with a decreasing response to water contents higher 0.30 cm3 cm-3 was also found in other studies30.
A rhizobox image with fine mapping of water content is shown in Figure 16. Four aspects have to be remarked. First, a region of lower water content can be seen in the rooted parts of the rhizobox. Second, strongest depletion is concentrated in the vicinity to single root axes. Third, depletion zones also occur where no root axes are visible on the surface, indicating regions where roots are hidden in soil. Fourth, water mapping without further image-processing results in a patchy appearance due to the aggregated soil. This can indicate inhomogeneous water content distribution at the aggregate scale, but also surface morphology effect on image quality. Chemometric image-processing techniques are an option to overcome such morphological effects in spectral images31, but are not implemented so far in the Matlab scripts used here.
<img alt="Figure 16" class="xfigimg" src="/files/ftp_upload/56251/56251fig16.jpg" /> 
Figure 16: Water content mapping on a rhizobox. 
The dark blue colours represent regions of high water content, green to red areas show regions with low water content. The plant root is overlaid on the image in black. Scale bar (bottom left corner), 2 cm. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig16large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Application examples
Figure 17 relates quantitative root traits from image analysis with aboveground measurements.
<img alt="Figure 17" class="xfigimg" src="/files/ftp_upload/56251/56251fig17.jpg" /> 
Figure 17: Typical application examples for root data. 
(A) and (B) show root information used for aboveground-belowground plant characterization in a phenotyping context. (A) represents root growth from the sugar beet cultivar Ferrara, (B) compares six rhizobox grown sugar beet cultivars using leaf-to-root area ratio (data from one replicate). (C) and (D) are functional relations between traits as found in plant physiological research. (C) shows the influence of leaf-to-root area ratio on dry matter production and (D) the relation of root surface area to stomata conductance. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig17large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure 17A and 17B are relevant for phenotyping focusing on comprehensive aboveground and belowground plant characterization. Figure 17A shows root growth of sugar beet cultivar Ferrara (cf. Figure 8 for images). Expansion of the root system indicates the capacity of a cultivar to explore the soil volume in a given time span of the vegetation period. Figure 17B shows leaf-to-root surface area ratio of six sugar beet cultivars, providing a descriptor for the balance between plant supply (root) and demand (leaf).
Figures 17C and 17D give examples for functional relations of interest in physiological research. In Figure 17C leaf-to-root surface area ratio is related to dry matter formed during the experiment, indicating the predominant role of the assimilating surface as a limiting factor for dry matter accumulation. The lack of significance in spite of a comparatively high R2 is related to the low number of paired data (n=6) used here. Figure 17D reveals that cultivars with higher root surface area (improved uptake) have an average higher stomata conductance over the course of the experiment. The higher root area apparently sustains water extraction, thereby prolonging stomata opening.

Watch this Scientific Journal Video about RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols at JoVE.com

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop ...

rgb-spectral-root-imaging-for-plant-phenotyping-physiological

Research

JoVE Journal

Environment

16.1K Views.  University of Natural Resources and Life Sciences. Een experimenteel protocol wordt gepresenteerd voor de beoordeling van de grond gekweekte plant wortelstelsel met RGB- en Hyperspectrale beeldvorming. Combinatie van RGB-afbeelding tijd serie met chemometric informatie van hyperspectrale scant optimaliseert inzichten in plant wortel dynamiek.

Video: RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to climate change and crop management. Quantitative size information on roots in their native environment is invaluable for studying root growth and environmental processes involving plants. X-ray computed tomography (XCT) has been demonstrated to be an effective tool for in situ root scanning and analysis. We aimed to develop a costless and efficient tool that approximates the surface and volume of the root regardless of its shape from three-dimensional (3D) tomography data. The root structure of a Prairie dropseed (Sporobolus heterolepis) specimen was imaged using XCT. The root was reconstructed, and the primary root structure was extracted from the data using a combination of licensed and open-source software. An isosurface polygonal mesh was then created for ease of analysis. We have developed the standalone application imeshJ, generated in MATLAB1, to calculate root volume and surface area from the mesh. The outputs of imeshJ are surface area (in mm2) and the volume (in mm3). The process, utilizing a unique combination of tools from imaging to quantitative root analysis, is described. A combination of XCT and open-source software proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information from the 3D data. This methodology of processing 3D data should be applicable to other material/sample systems where there is connectivity between components of similar X-ray attenuation and difficulties arise with segmentation.

A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to ...

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive harvest or expensive equipment. We present a customizable and affordable rhizobox method that allows the non-destructive visualization of root growth over time and is particularly well-suited to studying root plasticity in response to localized resource patches. The method was validated by assessing maize genotypic variation in plasticity responses to patches containing 15N-labeled legume residue. Methods are described to obtain representative developmental measurements over time, measure root length density in resource-containing and control patches, calculate root growth rates, and determine 15N recovery by plant roots and shoots. Advantages, caveats, and potential future applications of the method are also discussed. Although care must be taken to ensure that experimental conditions do not bias root growth data, the rhizobox protocol presented here yields reliable results if carried out with sufficient attention to detail.

Visualizing and measuring root growth in situ is extremely challenging. We present a customizable rhizobox method to track root development and proliferation over time in response to nutrient enrichment. This method is used to analyze maize genotypic differences in root plasticity in response to an organic nitrogen source.

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive ...

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized technique for assessing ear density would aid in improving agricultural management, providing improvements in preharvest yield predictions, or could even be used as a tool for crop breeding when it has been defined as a trait of importance. Not only are the current techniques for manual ear density assessments laborious and time-consuming, but they are also without any official standardized protocol, whether by linear meter, area quadrant, or an extrapolation based on plant ear density and plant counts postharvest. An automatic ear counting algorithm is presented in detail for estimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digital images, allowing for high-throughput standardized measurements. Different field trials of durum wheat and barley distributed geographically across Spain during the 2014/2015 and 2015/2016 crop seasons in irrigated and rainfed trials were used to provide representative results. The three-phase protocol includes crop growth stage and field condition planning, image capture guidelines, and a computer algorithm of three steps: (i) a Laplacian frequency filter to remove low- and high-frequency artifacts, (ii) a median filter to reduce high noise, and (iii) segmentation and counting using local maxima peaks for the final count. Minor adjustments to the algorithm code must be made corresponding to the camera resolution, focal length, and distance between the camera and the crop canopy. The results demonstrate a high success rate (higher than 90%) and R2 values (of 0.62-0.75) between the algorithm counts and the manual image-based ear counts for both durum wheat and barley.

We present a protocol for counting durum wheat and barley ears, using natural color (RGB) digital photographs taken in natural sunlight under field conditions. With minimal adjustments for camera parameters and some environmental condition limitations, the technique provides precise and consistent results across a range of growth stages.

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, ...

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&ndash;Environment Interactions

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating a knowledge gap. To meet the challenge of improving crops to feed the growing global population, this gap must be bridged.
Physiological traits are considered key functional traits in the context of responsiveness or sensitivity to environmental conditions. Many recently introduced high-throughput (HTP) phenotyping techniques are based on remote sensing or imaging and are capable of directly measuring morphological traits, but measure physiological parameters mainly indirectly.
This paper describes a method for direct physiological phenotyping that has several advantages for the functional phenotyping of plant&#8211;environment interactions. It helps users overcome the many challenges encountered in the use of load-cell gravimetric systems and pot experiments. The suggested techniques will enable users to distinguish between soil weight, plant weight and soil water content, providing a method for the continuous and simultaneous measurement of dynamic soil, plant and atmosphere conditions, alongside the measurement of key physiological traits. This method allows researchers to closely mimic field stress scenarios while taking into consideration the environment&#8217;s effects on the plants&#8217; physiology. This method also minimizes pot effects, which are one of the major problems in pre-field phenotyping. It includes a feed-back fertigation system that enables a truly randomized experimental design at a field-like plant density. This system detects the soil-water-content limiting threshold (&#952;) and allows for the translation of data into knowledge through the use of a real-time analytic tool and an online statistical resource. This method for the rapid and direct measurement of the physiological responses of multiple plants to a dynamic environment has great potential for use in screening for beneficial traits associated with responses to abiotic stress, in the context of pre-field breeding and crop improvement.

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&#8211;Environment Interactions

This high-throughput, telemetric, whole-plant water relations gravimetric phenotyping method enables direct and simultaneous real-time measurements, as well as the analysis of multiple yield-related physiological traits involved in dynamic plant&#8211;environment interactions.

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating ...

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Reflectance Spectroscopy (RS) and Fiber Optics Reflectance Spectroscopy (FORS) are well-established techniques for the investigation of works of art with particular attention to paintings. Most modern museums put at the disposal of their research groups portable equipment that, together with the intrinsic non-invasiveness of RS and FORS, makes possible the in situ collection of reflectance spectra from the surface of artefacts. The comparison, performed by experts in pigments and painting materials, of the experimental data with databases of reference spectra drives the characterization of the palettes and of the techniques used by the artists. However, this approach requires specific skills and it is time consuming especially if the number of the spectra to be investigated becomes large as is the case of Hyperspectral Reflectance Imaging (HRI) datasets. The HRI experimental setups are multi-dimensional cameras that associate the spectral information, given by the reflectance spectra, with the spatial localization of the spectra over the painted surface. The resulting datasets are 3D-cubes (called hypercubes or data-cubes) where the first two dimensions locate the spectrum over the painting and the third is the spectrum itself (i.e., the reflectance of that point of the painted surface versus the wavelength in the operative range of the detector). The capability of the detector to simultaneously collect a great number of spectra (typically much more than 10,000 for each hypercube) makes the HRI datasets large reservoirs of information and justifies the need for the development of robust and, possibly, automated protocols to analyze the data. After the description of the procedure designed for the data acquisition, we present an analysis method that systematically exploits the potential of the hypercubes. Based on Spectral Angle Mapper (SAM) and on the manipulation of the collected spectra, the algorithm handles and analyzes thousands of spectra while at the same time it supports&#160;the user to unveil the features of the samples under investigation. The power of the approach is illustrated by applying it to Quarto Stato, the iconic masterpiece by Giuseppe Pellizza da Volpedo, held in the Museo del Novecento in Milan (Italy).

Hyperspectral Reflectance Imaging hypercubes include remarkable information into a large amount of data. Therefore, the request for automated protocols to manage and study the datasets is widely justified. The combination of Spectral Angle Mapper, data manipulation, and a user-adjustable analysis method constitutes a key-turn for exploring the experimental results.

Reflectance Spectroscopy (RS) and Fiber Optics Reflectance Spectroscopy (FORS) are well-established techniques for the investigation of works of art with ...

Chemistry

Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses

Chlorophyll a fluorescence analysis is widely used to measure photosynthetic behaviors in intact plants, and has resulted in the development of many parameters that efficiently measure photosynthesis. Leaf reflectance analysis provides several vegetation indices in ecology and agriculture, including the photochemical reflectance index (PRI), which can be used as an indicator of thermal energy dissipation during photosynthesis because it correlates with non-photochemical quenching (NPQ). However, since NPQ is a composite parameter, its validation is required to understand the nature of the PRI parameter. To obtain physiological evidence for evaluation of the PRI parameter, we simultaneously measured chlorophyll fluorescence and leaf reflectance in xanthophyll cycle defective mutant (npq1) and wild-type Arabidopsis plants. Additionally, the qZ parameter, which likely reflects the xanthophyll cycle, was extracted from the results of chlorophyll fluorescence analysis by monitoring relaxation kinetics of NPQ after switching the light off. These simultaneous measurements were carried out using a pulse-amplitude modulation (PAM) chlorophyll fluorometer and a spectral radiometer. The fiber probes from both instruments were positioned close to each other to detect signals from the same leaf position. An external light source was used to activate photosynthesis, and the measuring lights and saturated light were provided from the PAM instrument. This experimental system enabled us to monitor light-dependent PRI in the intact plant and revealed that light-dependent changes in PRI differ significantly between the wild type and npq1 mutant. Furthermore, PRI was strongly correlated with qZ, meaning that qZ reflects the xanthophyll cycle. Together, these measurements demonstrated that simultaneous measurement of leaf reflectance and chlorophyll fluorescence is a valid approach for parameter evaluation.

We describe a new technical approach to study photosynthetic responses in higher plants involving simultaneous measurements of chlorophyll a fluorescence and leaf reflectance using a PAM and a spectral radiometer for the detection of signals from the same leaf area in Arabidopsis.

Chlorophyll a fluorescence analysis is widely used to measure photosynthetic behaviors in intact plants, and has resulted in the development of many ...

Bioengineering

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

Research efforts in biology increasingly require use of methodologies that enable high-volume collection of high-resolution data. A challenge laboratories can face is the development and attainment of these methods. Observation of phenotypes in a process of interest is a typical objective of research labs studying gene function and this is often achieved through image capture. A particular process that is amenable to observation using imaging approaches is the corrective growth of a seedling root that has been displaced from alignment with the gravity vector. Imaging platforms used to measure the root gravitropic response can be expensive, relatively low in throughput, and/or labor intensive. These issues have been addressed by developing a high-throughput image capture method using inexpensive, yet high-resolution, flatbed scanners. Using this method, images can be captured every few minutes at 4,800 dpi. The current setup enables collection of 216 individual responses per day. The image data collected is of ample quality for image analysis applications.

This protocol describes a process for rapid collection of images of Arabidopsis seedlings responding to a gravity stimulus using commercially-available flatbed scanners. The method allows for inexpensive, high-volume capture of high-resolution images amenable for downstream analysis algorithms.

Research efforts in biology increasingly require use of methodologies that enable high-volume collection of high-resolution data. A challenge laboratories ...

Biology

A Simple Protocol for Mapping the Plant Root System Architecture Traits

Comprehensive knowledge of plant root system architecture (RSA) development is critical for improving nutrient use efficiency and increasing crop cultivar tolerance to environmental challenges. An experimental protocol is presented for setting up the hydroponic system, plantlet growth, RSA spreading, and imaging. The approach used a magenta box-based hydroponic system containing polypropylene mesh supported by polycarbonate wedges. Experimental settings are exemplified by assessing the RSA of the plantlets under varying nutrient (phosphate [Pi]) supply. The system was established to examine the RSA of Arabidopsis, but it is readily adaptable to study other plants like Medicago sativa (Alfalfa). Arabidopsis thaliana (Col-0) plantlets are used in this investigation as an example to understand the plant RSA. Seeds are surface sterilized by treating ethanol and diluted commercial bleach, and kept at 4 &#176;C for stratification. The seeds are germinated and grown on a liquid half-MS medium on a polypropylene mesh supported by polycarbonate wedges. The plantlets are grown under standard growth conditions for the desired number days, gently picked out from the mesh, and submersed in water-containing agar plates. Each root system of the plantlets is spread gently on the water-filled plate with the help of a round art brush. These Petri plates are photographed or scanned at high resolution to document the RSA traits. The root traits, such as primary root, lateral roots, and branching zone, are measured using the freely available ImageJ software. This study provides techniques for measuring plant root characteristics in controlled environmental settings. We discuss how to (1) grow the plantlets, and collect and spread root samples, (2) obtain pictures of spread RSA samples, (3) capture the images, and (4) use image analysis software to quantify root attributes. The advantage of the present method is the versatile, easy, and efficient measurement of the RSA traits.

We use simple laboratory tools to examine the root system architecture (RSA) of Arabidopsis and Medicago. The plantlets are grown hydroponically over mesh and spread using an art brush to reveal the RSA. Images are taken using scanning or a high-resolution camera, then analyzed with ImageJ to map traits.

Comprehensive knowledge of plant root system architecture (RSA) development is critical for improving nutrient use efficiency and increasing crop cultivar ...

Potato Resilience Analysis Under Climate-Related Stresses

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato

High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions over time. By using multiple imaging sensors, many traits of interest can be assessed, including plant biomass, photosynthetic efficiency, canopy temperature, and leaf reflectance indices. Plants are frequently exposed to multiple stresses under field conditions where severe heat waves, flooding, and drought events seriously threaten crop productivity. When stresses coincide, resulting effects on plants can be distinct due to synergistic or antagonistic interactions. To elucidate how potato plants respond to single and combined stresses that resemble naturally occurring stress scenarios, five different treatments were imposed on a selected potato cultivar (Solanum tuberosum L., cv. Lady Rosetta) at the onset of tuberization, i.e. control, drought, heat, waterlogging, and combinations of heat, drought, and waterlogging stresses. Our analysis shows that waterlogging stress had the most detrimental effect on plant performance, leading to fast and drastic physiological responses related to stomatal closure, including a reduction in the quantum yield and efficiency of photosystem II and an increase in canopy temperature and water index. Under heat and combined stress treatments, the relative growth rate was reduced in the early phase of stress. Under drought and combined stresses, plant volume and photosynthetic performance dropped with an increased temperature and stomata closure in the late phase of stress. The combination of optimized stress treatment under defined environmental conditions together with selected phenotyping protocols allowed to reveal&#160;the dynamics of morphological and physiological responses to single and combined stresses. Here, a useful tool is presented for plant researchers looking to identify plant traits indicative of resilience to several climate change-related stresses.

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

We designed an image-based phenotyping protocol to determine the morphological and physiological responses to single and combined heat, drought, and waterlogging treatments. This approach enabled the identification of early, late, and recovery responses at a whole plant level, particularly above-ground parts, and highlighted the necessity of using multiple imaging sensors.

High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions ...

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Measuring quantitative plant phenotypes, or traits, is essential for understanding plant responses to environmental factors, including biotic and abiotic stresses. However, traditional methods of measuring plants can be time-consuming, inaccurate, and destructive, particularly when assessing traits such as biomass and height on an individual basis. Image-based analysis offers an opportunity to reduce labor, improve accuracy and precision, and enable repeated measurements of the same plant over multiple time points. While high-throughput phenotyping facilities provide a solution, they are often prohibitively expensive and limited in availability worldwide. A method is described for capturing plant images using low-cost single-board computers and digital cameras within a photo studio setup, accompanied by an image analysis pipeline based on the free and open-source package PlantCV. This method offers a fast and accurate protocol for image capture, labeling, and transfer, along with best practices for ensuring high-quality image acquisition. Through image analysis, more than 10 phenotypes were quantified for all images in the experiment simultaneously, including traits such as plant size, height, and color. These methods were used to characterize the response of the Zea mays (maize) inbred variety B73 to heat, drought, and combined heat and drought abiotic stress conditions.

A method is presented for low-cost imaging of plants to measure phenotypes, along with best practices for image capture and an image analysis pipeline for quantifying plant traits. These methods were applied to measure maize (Zea mays) phenotypes under heat, drought, and combined abiotic stress conditions.

Measuring quantitative plant phenotypes, or traits, is essential for understanding plant responses to environmental factors, including biotic and abiotic ...

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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