remote sensing in plant breeding

8/10/2019 Remote Sensing in Plant Breeding

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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/


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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



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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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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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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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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


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


(1-Vj)/Vj indicate the force related to

the dark reaction



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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



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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



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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



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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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(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.aspx


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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-array


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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/


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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

remote sensing in plant breeding

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