New Technologies New Technologies forfor

GerminationGermination TestingTesting

SomeSome factsfacts, , ideasideas and and

examplesexamples forfor inspirationinspiration

Bert van Duijn

bertvanduijn@fytagoras.nl

New tools in germination New tools in germination

evaluationevaluation

�� Evaluation of nonEvaluation of non--germinated (germinated (dry)seedsdry)seeds�� Prediction of germinationPrediction of germination

�� Prediction of plantlet qualityPrediction of plantlet quality

�� Evaluation of germinated seedsEvaluation of germinated seeds�� Automation of standard germination test or field Automation of standard germination test or field

testtest

�� CountingCounting

�� Plantlet evaluationPlantlet evaluation

Evaluation of nonEvaluation of non--germinated germinated

((dry)seedsdry)seeds

� The germination quality of seeds is determined by:

�Physiological factors

�Genetic factors

�Structural factors

�Biological factors (e.g. microbiology)

� Hence, it is very unlikely that germination quality can be determined with one method/measurement/parameter

MeasurementMeasurement of of materialmaterial

((chemicalchemical) and ) and structuralstructural

propertiesproperties onon the the surfacesurface

((oror justjust belowbelow))

�� SpectroscopySpectroscopy

�� Image Image analysisanalysis

�� HyperspectralHyperspectral analysisanalysis and and imagingimaging

SpectralSpectral (image) (image) analysisanalysis

�� VisibleVisible lightlight

�� XX--rayray

�� UVUV

�� ((NearNear) ) infrainfra redred

�� SpecificSpecific wavelengthswavelengths

�� NMRNMR

�� AcousticsAcoustics

�� ReflectionReflection imageimage

�� FluoresenceFluoresence imageimage

�� AbsorbanceAbsorbance imageimage

�� OtherOther1616--66--20092009 55ATC, ISTAATC, ISTA

SURFACE PROPERTIES &SURFACE PROPERTIES &

LEAKING PROPERTIESLEAKING PROPERTIES

Classifying deteriorated seeds (Brassicaceae) by

sinapine leakage (T.G. Min, Daegu Univeristy, South-Korea)

Fluorescence from sinapine leakage

under UV light in the dark room

C C NF F F NF

C: control seeds, NF: non-fluorescent seeds, F: fluorescent seeds

(T.G. Min, Daegu Univeristy, South-Korea)

Schematic diagram for measuring NIR spectra of the intact single seed.

Identifying deteriorated seeds by NIR

(T.G. Min, Daegu Univeristy, South-Korea)

NIR spectra of radish seeds.(a: raw spectra, b: mean spectra of raw,c: first derivative of the mean spectra)

Principle component score plots for radish seeds.

(+: viable seed, □: nonviable seed)

(T.G. Min, Daegu Univeristy, South-Korea)

Prediction accuracy of viable and nonviable radish seeds classified by

PLS 2 models from raw, 1st, and 2nd derivative of NIR spectra data

sets.

(T.G. Min, Daegu Univeristy, South-Korea)

Chemical elements on the surfaces of seed coat and cotyledon

compared to viable and nonviable seeds.

* Significant at P=0.05, **Significant at P=0.01 , NS=non significance

NIR spectra of gourd seeds. (A: original, B:

mean spectra of original, C: first derivative of the

mean spectra)

Principle component score plot for gourd seeds

(+: viable seed, □: nonviable seed.)

Nondestructive Separation of Viable and

Non-viable

Gourd (Lagenaria siceraria Standl) Seeds

(T.G. Min, Daegu Univeristy, South-Korea)

Micrographs of viable seed coat (A) and nonviable seed coat (B) in

the B spot (x 500). Bar = 100 µm.

(T.G. Min, Daegu Univeristy, South-Korea)

Micrograph of viable cotyledon (A) and nonviable cotyledon (B)

in the B spot (x 2,000). Numerous bacteria were contaminated

in a nonviable cotyledon (B) surface while not in a viable one (A).

Bar = 20 µm.

(T.G. Min, Daegu Univeristy, South-Korea)

Sample No.

Viable seed Non-viable seed

Ay B C A B C

1 0x 0 1 0 497 1191

2 0 0 0 231 223 219

3 0 0 0 2267 932 542

4 0 0 0 896 807 517

5 0 0 10 252 0 0

6 4 45 5 176 456 172

7 0 0 0 401 538 65

8 0 0 1 534 523 303

9 14 2 0 356 108 588

10 0 12 2 615 308 244

Aveage 1.8 5.9 1.9 572.8 439.2 384.1

Bacteria number at three spots on the surfaces of

gourd cotyledon under the Field Emission Scanning

Electron Microscope.

(T.G. Min, Daegu Univeristy, South-Korea)

chlorophyll fluorescence of germinating seeds(Data from H. Jalink, WUR, Netherlands)

Hyperspectral cameras and images

Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. Much as the human eye sees visible light in three bands(red, green, and blue), spectral imagingdivides the spectrum into many more bands. This technique of dividing images into bands can be extended beyond the visible.

Measurement of internal structures

�X-ray based imaging�3-D (x-ray) imaging

Relationship Relationship RRööntgenntgen--seedseed--images images

and plantand plant--quality of cucumberquality of cucumber

Abnormalseeds

Goodseed

Goodplant

Abnormalplants

2323

High Resolution XHigh Resolution X--ray image ray image

of Watermelon Seeds (3n)of Watermelon Seeds (3n)

Raw Enhanced 1 Enhanced 2

GerminationGermination measurementmeasurement

plant plant countingcounting

in the labin the lab

in the fieldin the field

�� Plantlet Plantlet imagingimaging

�� Plantlet markersPlantlet markers

Chlorophyll fluorescenceChlorophyll fluorescence

2 3 4 7

Labeling of plantlets via Labeling of plantlets via

uptake of marker via seeduptake of marker via seed

ConclusionsConclusions

�� No single No single techniquetechnique/parameter /parameter ableable to to

predictpredict germinationgermination qualityquality

�� Multi (hyper) Multi (hyper) spectralspectral imagingimaging is is

promisingpromising in in combinationcombination withwith otherother

technologiestechnologies))

�� New New imagingimaging technologytechnology maymay help to help to

automate automate germinationgermination test test analysisanalysis (in (in

the lab and in the field)the lab and in the field)