14th March 2017

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SANTOSH SHRESTHAAARHUS UNIVERSITYAU

Potentials of non-destructive technologies

for

seed quality assessment in Nepal

Santosh Shrestha

Department of Agroecology

Aarhus University

14th March 2017

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Outline

1) Seed system in Nepal

2) Seed quality in Nepal

3) New methods for seed testing

4) Confirmation of hybridization - multispectral imaging

5) Varietal classification - multispectral imaging

6) Viability testing - near infrared spectroscopy

7) Conclusions and perspectives

2

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Seed system in Nepal

Two types of seed system:

1) Informal seed system (Local seed networks)

• Farmers producing and preserving their own seeds for subsequent planting

• Seed exchange- monetary and non-monetary

• Genetic resources- local land races

2) Formal seed system

• Organized seed production and distribution of tested and released/registered

varieties using agreed quality control mechanism

• Seed producers- NARC and NSCL (public); Comunity based seed production

(CBSP) groups, SEAN, NGOs, Seed imports

• <less than 10% of the total seed transaction in Nepal

3

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Seed quality of the marketed seeds – 2008 to 2010

4

Thapa, 2011

Majority of samples tested were not marketable

Seed quality in Nepal

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5

17 different rice cultivar mixtures in a

farmer’s plot! @Kantipur daily, 2016

@Nepal samacharpatra, 2016

Repackaging and false labelling

Unregulated seed import

@ekantipur, 2013

Seed quality in Nepal

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Seed quality in Nepal

Seed Vision 2025- launched in 2013

Plant variety protection and farmer’s rights protection bill

Seed Testing- 13* laboratory - 13000 samples 3 x increase

17,525 T

232

2010Estimated Increase

51,254 T

431

2025

Seed Req.

New variety

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7

New methods for seed testing

Imaging

X-ray imaging

Chlorophyll fluorescence

Infrared spectroscopy

Near infrared spectroscopy (NIRS)

Hyperspectral imaging (HSI)

Multispectral imaging (MSI)

Nuclear magnetic resonance spectroscopy

Conventional imaging

Near infrared spectroscopy (NIRS)

Hyperspectral imaging (HSI)

Multispectral imaging (MSI)

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New methods for seed testing

Each pixel in the image is a

reflectance spectrum of

constituent monochromatic

images

1000 1200 1400 1600 1800 2000 2200 2400 2600

0.2

0.4

0.6

Wavelength (nm)

Reflecta

nce

Hyperspectral Imaging

405 450 505 570 630 660 780 870 940

20

40

60

Wavelength (nm)

Ref

lect

ance

(%

)

Multispectral Imaging

λ= nm

x

y

Hyper-cube

λ= 288 λ = 19

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New methods for seed testing

1000 1200 1400 1600 1800 2000 2200 2400 2600

0.2

0.4

0.6

Wavelength (nm)

Reflecta

nce

Hyperspectral Imaging

405 450 505 570 630 660 780 870 940

20

40

60

Wavelength (nm)

Ref

lect

ance

(%

)

Multispectral Imaging

λ= nm

x

y

Hyper-cube

NIR Spectrocopy

1000 1200 1400 1600 1800 2000 220

1

1.5

2

2.5

Wavelength (nm)

Absorb

ance

Mean

Each pixel in the image is a

reflectance spectrum of

constituent monochromatic

images

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1000 1200 1400 1600 1800 2000 220

1

1.5

2

2.5

Wavelength (nm)

Absorb

ance

Mean

10

New methods for seed testing

Asymmetrical

stretching

H-O-H

Symmetrical

stretching

H-O-H

Symmetrical

vibration

H-O-H

1

7

2

6

1

8

6

2

1

9

4

0

2

1

4

6

1

4

5

0

1

1

9

6

1

6

5

5

LipidsProteins

Water

Carbohydrates

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11

Image analysis-background segmentation

VideometerLab system

Region

labelling

Segmentation

ROIs

nCDA- high scores to ROIs

and low scores to background

Foreground vs

background

transform

MS image

375 – 970 nm- 19 bands

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Confirmation of hybridization

12

Parent 1 Parent 2Hybrid 1 Hybrid 2

- multispectral imaging

(Shrestha, Deleuran et al. 2015)

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Varietal classification

13

7%

1%

5%

3% 3%

0%

1%

2%

3%

4%

5%

6%

7%

8%

Classification Error Rate

PLS-DA

PCA Score plot

(Shrestha, Deleuran et al. 2016)

- multispectral imaging

5 tomato cultivar seeds – separation by multispectral imaging

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Tomato seed viability

Viability testing

Tomato- Heinz and Chiuri (500 seeds) + single seed NIR measurement

Germination test- @200C 12 hr light/dark

- near infrared spectroscopy

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Tomato seed viability

Biplot

Viability testing - near infrared spectroscopy

Biplot – Combined information on samples and variables

Water

Carbohydrates

Proteins

Proteins/water

Lipids

Lipids

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16

82%93%

12%

81%94%

13%

Non-viable Viable CER

Training set Test set

50 100 150 200 250 300-0.4

-0.2

0

0.2

0.4

0.6

0.8

Samples

Pre

dic

ted

: N

on

-Via

ble

se

ed

s

Non-viable

Viable

Discrimination threshold

Pre

dic

ted

: N

on

-via

ble

se

ed

s

Classification of viable and non-viable tomato seeds

Viability testing - near infrared spectroscopy

14th March 2017

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Viability testing- more evidences

17

(Rahman and Cho 2016)

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Near infrared and spectrosocpy has a potential for seed viability

testing

18

Conclusions and perspectives

Multispectral/hyperspectral imaging - potential tool for varietal

separation/adulteration studies

Validation of these methods – accross laboratories and species.

Research and development of cheap instrument

Biochemical studies to link the spectral fingerprints with seed

biology

AARHUS

UNIVERSITETAU