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Remote Sensing in Plant Breeding
(Field-based Phenomics)
Novi Tri Astutiningsih
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Introduction
Current plant breeding purpose mainly
focused on the development of high yielding
and stress resisting cultivars or lines
Reduces in cost and time for genomics
processes
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http://www.plant-phenotyping-network.eu/
How to predict crop performance as a
function of genetic architecture?
http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/8/10/2019 Remote Sensing in Plant Breeding
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Current phenotyping limitation:
mainly performed in a controlled-environment system (e.g.greenhouse or plant growth chamber)
limited space and soil volume, and atmospheric differences
(von Mogel, 2012)
Phytomorph project at the University of WisconsinMadison:robotic camera that photographs growing seedlings and roots at
regular intervals, with micron-level precision
LemnaTec (Germany):phenotyping individual plants in large, robotic greenhouses
using photography, fluorescence imaging, 3D image analysis
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High-throughput field-based
phenotyping (FBP)
Simultaneous proximal sensing for spectralreflectance, canopy temperature, and plant
architecture Larger samples/scales
Multiple environments
Throughout crop life cycle Characterize multiple traits in a single pass
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Remote sensing platform
*Using sensing platform commonly applied for remote sensing of vegetation
1. Static and within-fieldplatforms
2. Aircraft
3. Satellites
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Static and within-field platforms
Monitoring plant status for either a single leaf (or plant) or theentire field plots
Proximal (close-range) sensing Is often the only approach that can provide adequate resolution for
phenotyping studies Higher resolution sensingpixel-to-pixel analyses
Provide multiple view-angles, control illumination and regulate the distancefrom the target to the sensors
Reduce background signal and atmospheric correction
Permit positioning of sensors or sources of illumination at the base or side of
the canopy, allowing measurement of transmittance rather than reflectance
Using hand-held instrument or vehicle-mounted instruments(e.g. high-clearance tractors, crane-like vehicles, cable robots)
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Measuring:
- Canopy height
- Canopy temperature
- Spectral reflectance (three bandwidths)
(White et al., 2012)
At the USDA Arid-Land Agricultural Research Center in Maricopa, AZ
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Ag Eyes (AgIIS, Agricultural Irrigation Imaging System) at the Maricopa Agricultural Center
(Haberland et al., 2010)
AgIIS cart, arm, and sensor
AgIIS rail
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(White and Bostelman, 2011)
Prototype of RoboCrane (Large-area Overhead Manipulator for Access of Fields, LOMAF)at The National Institute of Standards and Technology (NIST)
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Aircraft
Versatile
Adjusted height
Specific area, particular angle
May be not reproducible
Not universally available and
certain permissions have to be
acquired
Not a very stable platform Operational skills
E.g. UAVs (unmanned aerial vehicles), balloons, light planes,
helicopters, aerostats, model aircrafts, phenocopters
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(von Mogel, 2013)Measure:
- Plant height
- Canopy cover
- Lodging
- temperature throughout a day
Phenocopter (a remote controlled gas-powered model helicopter) at CSIRO
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(Garcia-Ruiz et al., 2013)
Being developed at the Department of Biological Systems
Engineering (WSU) for plant phenotyping purpose
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Comparison of five vehicle options for field-based phenomics
(White et al., 2012)
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Satellites
Current satellite platforms provide various sensor instruments
for vegetation monitoring. Several satellite platforms that are
commonly used in remote sensing of vegetation
Terra (using MODIS)
Landsat 7 (using EMT+)
NOAA (using AVHRR).
To maintain its performance, each satellite is supported with
ground validation to constantly monitoring the operation of
each instrument.
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(Xie et al., 2008)
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(Jones and Vaughan, 2010)
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(Jones and Vaughan, 2010)
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(Jones and Vaughan, 2010)
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Parameters measured
1. Thermography (digital, infrared, NIR)
2. Chlorophyll fluorescence analysis
3. Reflectance Spectroscopy
4. Digital growth analysis
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Thermography
Non-destructive measurement of
plant performance using its canopy
temperature
Hand-held thermometers or infrared
camera are time consuming
Infrared sensors mounted on
vehicles on or above the
experimental plots can be used to
remotely sense canopy
temperatures. (Berger et al., 2010;
Furbank and Tester, 2011)
(Furbank and Tester, 2011)
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Jones, H. G., Serraj, R., Loveys, B. R., Xiong, L., Wheaton, A., and Price, A.H. (2009). Thermal infrared imaging of crop canopies for the remote
diagnosis and quantification of plant responses to water stress in the
field. Functional Plant Biology36, 978-989.
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(Jones et al., 2009)
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(Jones et al., 2009)
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(Jones et al., 2009)
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Prashar, A., Yildiz, J., McNicol, J. W., Bryan, G. J., and Jones, H. G. (2013).
Infra-red Thermography for High Throughput Field Phenotyping in
Solanum tuberosum. PLoS ONE8, e65816.
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(Prashar et al., 2013)
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(Prashar et al., 2013)
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Chlorophyll fluorescence analysis
Measuring plant photosynthesis performance by identifying the
photochemical efficiency
Using a fluorimeter
dark-adapted Fv/Fm
electron transport rate (ETR)
non-photochemical quenching (NPQ) higher sensitivity but challenging
As a complimentary (Berger et al.,
2010)
Fluorescence imaging to determine
plant growth using projected leaf area
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Protocol for chlorophyll fluorescence analysis using pulse
modulated technique (Baker and Rosenqvist, 2004)
(Campbell et al. 3003)
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Parameter Definition Description Reference
Fo Initial fluorescence initial emission of the
oxidized electron acceptors of
PSII after illumination
(Baker and
Rosenqvist, 2004)
Fm Maximum
fluorescence
maximum chlorophyll
fluorescence value obtained
after electron acceptors inPSII is fully reduced by
photochemistry
(Baker and
Rosenqvist, 2004)
Fv Variable
fluorescence (=Fm-
Fo)
indicates fluorescence
emission during the excitation
of chlorophyll molecules
(Baker and
Rosenqvist, 2004)
Fv/Fm Maximum quantum
yield of PSII
indicates efficiency of PSII to
do photochemistry
(Maxwell and
Johnson, 2000; Baker
and Rosenqvist,
2004)Fv/Fo indicates the efficiency of
oxygen evolving complex in
PSII
(Skrska and Szwarc,
2007)
Tfm indicate the time at which Fm
was reached
(Strasser et al., 2004)
Area proportional to the pool size
of the electron acceptors on
PS II
(Strasser et al., 2004)
PI Performance Index indicate sample vitality (Strasser et al., 2004)
RC/ABS Concentration of
active PSII reaction
centers per photon
flux absorbed by the
antenna pigments
quantify the energy for
absorption by PSII
(Strasser et al., 2004)
(1-Vj)/Vj indicate the force related to
the dark reaction
(Strasser et al., 2004)
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Reflectance spectroscopy
Measurement of leaf spectroscopy using radiometric or imaging sensors
Leaf absorption and reflectance features of different solar radiation
wavelength allows the development of several indices to measure leaf or
tissue component and their correlation with plant photosynthesis activity
or plant biomass; such as
NDVI (normalized difference vegetation index)
PRI (photosynthetic reflective index)
Environmental variability (e.g. solar angle and cloud cover) created
difficulties to interpreting and qualifying hyperspectral reflectance
spectroscopy data and has not commonly been used in plant phenotyping(Furbank, 2009; Peuelas and Filella, 1998)
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Multi-spectral imagery Hyper-spectral imagery
Thermal (IR) imagery
(von Mogel, 2013)
NIR imagery
(Prashar et al., 2013)
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(White et al., 2012)
Examples of possible locations of sensors or cameras (S) and high-intensity illumination
(HIL) suspended above or below the crop canopy to measure transmittance and thus infer
light interception or canopy architecture at specific wavelengths
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(Peuelas and Filella, 1998)
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Examples of proximal sensing methods that show promise for field-based phenomics
(White et al., 2012)
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Digital growth analysis
Using multiple viewing angle analysis of projected leaf area or biomass
Currently used in in situphenotyping under controlled environments
Examining digital plant growth in a period of plant development that
allows accurate assessment of plant stress response mechanisms.
Using visual score from visible digital imaging, it is also possible to obtaininformation related to plant size and color for plant senescence or toxicity
quantification (Furbank and Tester, 2011)
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(Furbank and Tester, 2011)
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An integrated FBP platform
1. Instruments for acquiring raw data
2. Systems for integrating instruments
3. Vehicles for positioning instruments
4. High-throughput analysis of plant samples
5. Management of data flow and analysis
6. Integrated management of FBP
The system needs to be rapid, flexible and reliable
(White et al., 2012)
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Integrated management of FBP
Standard operating procedures are needed to ensure
reliable performance throughout an experiment,
including crop management, instrument calibration,
data transfer and initial analysis, and vehiclemaintenance
Field management to minimize or control within-field
sources of variation (e.g. soil characterization, soil
nutrient content, weather station, irrigation)
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(White et al., 2012)
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(Montes et al., 2007)
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Examples of possible paths of data analysis
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Challenges
Highly integrative approachescannot rely on
individual/small group researchers
Recently established national and international collaborations
Desired phenotype are combination of multiple traits
Large volumes of data
Develop protocols for testing instruments
Better algorithms for analyzing proximal sensing data
Patents?? Future sophisticated instruments (e.g. Kinect technology)
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Conclusion
FBP appears capable of attaining the requisitehigh levels of throughput needed asphenotyping tools.
FBP requires integrative, interdisciplinaryteamwork and thorough attention at all stages
field preparation and experimental design
processing and analysis of data
direct application toward finding solutions tomajor problems currently limiting crop production
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Future possibilities
The platform can also be used for site-specific
crop management
However, it would be hard to implement in
the developing countries (smallholder type
farming system)easier approaches are
preferable (e.g Leaf Color Chart for rice)
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Current phenomics centers
High Resolution Plant Phenomics Centre(CSIRO, Australia)
(http://www.csiro.au/Outcomes/Food-and-Agriculture/HRPPC.aspx)
Australian Plant Phenomics Facility(APPF) (www.plantphenomics.org.au)
National Plant Phenomics Centre (Aberystwyth University, UK)
(www.phenomics.org.uk)
PhenoFab the Plant Phenomics Service Center (LemnaTec, Germany)(http://www.lemnatec.com/news/phenofab-plant-phenomics-service-center )
International Plant Phenomics Network (IPPN) (http://www.plant-
phenotyping.org/)
Jlich Plant Phenotyping Centre (Germany) (http://www.fz-juelich.de/ibg/ibg-
2/EN/organisation/JPPC/JPPC_node.html) European Plant Phenotyping Network(EPPN) (http://www.plant-phenotyping-
network.eu/)
http://www.csiro.au/Outcomes/Food-and-Agriculture/HRPPC.aspxhttp://www.plantphenomics.org.au/http://www.phenomics.org.uk/http://www.lemnatec.com/news/phenofab-plant-phenomics-service-centerhttp://www.plant-phenotyping.org/http://www.plant-phenotyping.org/http://www.fz-juelich.de/ibg/ibg-2/EN/organisation/JPPC/JPPC_node.htmlhttp://www.fz-juelich.de/ibg/ibg-2/EN/organisation/JPPC/JPPC_node.htmlhttp://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.fz-juelich.de/ibg/ibg-2/EN/organisation/JPPC/JPPC_node.htmlhttp://www.fz-juelich.de/ibg/ibg-2/EN/organisation/JPPC/JPPC_node.htmlhttp://www.fz-juelich.de/ibg/ibg-2/EN/organisation/JPPC/JPPC_node.htmlhttp://www.fz-juelich.de/ibg/ibg-2/EN/organisation/JPPC/JPPC_node.htmlhttp://www.fz-juelich.de/ibg/ibg-2/EN/organisation/JPPC/JPPC_node.htmlhttp://www.fz-juelich.de/ibg/ibg-2/EN/organisation/JPPC/JPPC_node.htmlhttp://www.plant-phenotyping.org/http://www.plant-phenotyping.org/http://www.plant-phenotyping.org/http://www.plant-phenotyping.org/http://www.lemnatec.com/news/phenofab-plant-phenomics-service-centerhttp://www.lemnatec.com/news/phenofab-plant-phenomics-service-centerhttp://www.lemnatec.com/news/phenofab-plant-phenomics-service-centerhttp://www.lemnatec.com/news/phenofab-plant-phenomics-service-centerhttp://www.lemnatec.com/news/phenofab-plant-phenomics-service-centerhttp://www.lemnatec.com/news/phenofab-plant-phenomics-service-centerhttp://www.lemnatec.com/news/phenofab-plant-phenomics-service-centerhttp://www.lemnatec.com/news/phenofab-plant-phenomics-service-centerhttp://www.lemnatec.com/news/phenofab-plant-phenomics-service-centerhttp://www.lemnatec.com/news/phenofab-plant-phenomics-service-centerhttp://www.phenomics.org.uk/http://www.plantphenomics.org.au/http://www.csiro.au/Outcomes/Food-and-Agriculture/HRPPC.aspxhttp://www.csiro.au/Outcomes/Food-and-Agriculture/HRPPC.aspxhttp://www.csiro.au/Outcomes/Food-and-Agriculture/HRPPC.aspxhttp://www.csiro.au/Outcomes/Food-and-Agriculture/HRPPC.aspxhttp://www.csiro.au/Outcomes/Food-and-Agriculture/HRPPC.aspxhttp://www.csiro.au/Outcomes/Food-and-Agriculture/HRPPC.aspx8/10/2019 Remote Sensing in Plant Breeding
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Further Readings
Part One of Field Phenomics: Developing and Using a Sensor Array
http://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-
sensor-array#.Uo73Z8TB3X7
Part Two of Field Phenomics: Data Analysis
http://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-
analysis#.Uo73acTB3X7
http://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68270/part-two-of-field-phenomics:-data-analysishttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-arrayhttp://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-array8/10/2019 Remote Sensing in Plant Breeding
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References
Berger, B., Parent, B., and Tester, M. (2010). High-throughput shoot imaging to study drought responses.Journal ofExperimental Botany.
Furbank, R. T. (2009). Plant phenomics: from gene to form and function. Functional Plant Biology36, vvi.
Furbank, R. T., and Tester, M. (2011). Phenomicstechnologies to relieve the phenotyping bottleneck. Trends in PlantScience16, 635-644.
Garcia-Ruiz, F., Sankaran, S., Maja, J. M., Lee, W. S., Rasmussen, J., and Ehsani, R. (2013). Comparison of two aerialimaging platforms for identification of Huanglongbing-infected citrus trees. Computers and Electronics inAgriculture91, 106-115.
Jones, H. G., Serraj, R., Loveys, B. R., Xiong, L., Wheaton, A., and Price, A. H. (2009). Thermal infrared imaging of crop
canopies for the remote diagnosis and quantification of plant responses to water stress in the field. FunctionalPlant Biology36, 978-989.
Jones, H. G., and Vaughan, R. A. (2010). "Remote sensing of vegetation: principles, techniques, and applications,"Oxford University Press.
Peuelas, J., and Filella, I. (1998). Visible and near-infrared reflectance techniques for diagnosing plant physiologicalstatus. Trends in Plant Science3, 151-156.
Prashar, A., Yildiz, J., McNicol, J. W., Bryan, G. J., and Jones, H. G. (2013). Infra-red Thermography for High ThroughputField Phenotyping in Solanum tuberosum. PLoS ONE8, e65816.
von Mogel, K. H. (2013). Taking the Phenomics Revolution into the Field. CSA News58, 4-10.
White, J. W., Andrade-Sanchez, P., Gore, M. A., Bronson, K. F., Coffelt, T. A., Conley, M. M., Feldmann, K. A., French, A.N., Heun, J. T., Hunsaker, D. J., Jenks, M. A., Kimball, B. A., Roth, R. L., Strand, R. J., Thorp, K. R., Wall, G. W., andWang, G. (2012). Field-based phenomics for plant genetics research. Field Crops Research133, 101-112.
Xie, Y., Sha, Z., and Yu, M. (2008). Remote sensing imagery in vegetation mapping: a review.Journal of Plant Ecology1,9-23.
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http://www.plant-phenotyping-network.eu/
Questions?
http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/http://www.plant-phenotyping-network.eu/8/10/2019 Remote Sensing in Plant Breeding
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Fv/Fo
oxygen evolvingefficiency
Fv/Fm
efficiency of PSII to dophotochemistry or to convert
absorbed light into chemical
energy
proportional to the
pool size of theelectron acceptors
(Qa) on PS II
Performance Index (PI)indicator of sample vitality(samples internal force to resist
constraints from outside
electron acceptors
are fully oxidized (or
in an open state)
and ready to receiveelectrons
electron acceptors in the
PSII saturated (fully
reduced / closed) due
to the continuously
electron transport
= Fm-Fo
fluorescence
emission during theexcitation of
chlorophyll molecules