mailto:adelinengie@gmail.com

Background

Maize has been considered

globally as the most important

agricultural grain which is

staple food in many countries

and feed to livestock

Call to promote maize

production on the continent to

achieve self-sufficiency by

2015

Ngie et al., 2014

Background (cont’d)

The high genetic variation within

grown maize cultivars has

practically meant a tough

challenge to readily differentiate

them morphologically on the field

unless expensive and labour

intensive techniques are utilised.

Ngie et al., 2014

Spectral characteristics of vegetation leaves

Reflection of light at the leaf-level depends primarily on

pigments and internal structure of the leaf.

Agricultural studies use measurements in the visible

(400-700nm wavelength) and near infrared (700-1100nm

wavelength) region of the electromagnetic spectrum.

Large differences in the spectral characteristics of the

soil and crop, especially at the red edge is usually used

in vegetation studies.

The red edge point is where the electromagnetic

spectrum changes from visual to near infrared (680-

760nm)

Ngie et al., 2014

Spectral characteristics of vegetation leaves

The spectral

characteristics of

healthy vegetative

surfaces are

distinctive with low

reflectance in blue,

high in green, very

low in red and very

high in the near

infrared (NIR).

Ngie et al., 2014

Hyperspectral remote sensing and vegetation studies

Hyperspectral remote sensing technology is made up of

narrow and contiguous bands which makes it is possible

to distinguish variations of absorption features that are

not possible with multispectral sensors (Li, 2006).

The unique spectral signatures of features resulting from

the absorptive or emissive factors in hyperspectral

sensors allows much finer sampling of vegetation and

other materials

Ngie et al., 2014

Action Vegetation type Author(s)

Vegetation

identification

Teas Chen et al., 2007

Discrimination of plant

species

Pears; Wheat; Shrubs

& trees; tomatoes

Fu et al., 2007; Wang

et al., 2006; Cho et al.,

2008; Xu et al., 2009

Discrimination of

cultivars of

Sugar cane in Brazil Galvão et al., 2005

Estimate biochemical

content like nitrogen

Wheat; sugar cane Yao et al., 2010; Abdel-

Rahman et al., 2010

Ngie et al., 2014

While some studies have used simply the spectral

reflectance curve to evaluate the identification of

different vegetation types (Beck, 2003), others have

tested the use of different vegetation indices at both leaf

and canopy levels to discriminate species (Cho et al.,

2008).

Aim and Hypothesis

The purpose of this paper was

to demonstrate the potential of

hyperspectral remote sensing

technology in the differentiation

of maize cultivars at foliar level.

Hypothesis: that cultivar related

characteristic differences at

foliar level produce different

spectral responses that can be

detected with high spectral

resolution sensors.

Ngie et al., 2014

Materials

Used the hand held Portable Spectroradiometer (PSR-

3500 series) to measure the reflectance spectra from the

leaves of eight different maize cultivars planted under

field conditions.

Ngie et al., 2014

The Leaf-clip of the

handheld Portable

Spectroradiometer

(PSR-3500 series

of Spectral

Evolution Inc.,

USA)

Spectral measurements

Maize leaf spectra

measurements were

collected on February 4th,

2014 in Reitz, Free State

province of South Africa

The cultivars included

DKC 78-79 BRGEN, BT 1

YGI Mon 810, 8216 BR,

KKS 8301, PAN 3Q-222,

Imp 51-92 R, SC 602

Kuilvoer, P 31 M 05 R and

some experimental ones.

Ngie et al., 2014

Spectral measurements

Leaf samples were collected and

measurements taken in-doors

with the leaf-clip, avoiding the

mid-rib region.

About ten target measurements

were made after each

measurement on the reference

panel.

The spectra data obtained from

these measurements were then

used to create reflective curves.

Ngie et al., 2014

Spectral measurements

Through statistical

analyses relevant bands

were selected which

showed differences

between the maize

cultivars.

The Tukey-Kramer HSD

(honest significant

difference) was applied to

show how significant were

the differences at each

band in discriminating

cultivars.

Ngie et al., 2014

Reflectance curves of maize cultivars

Spectral

portions of the

890-990nm,

1355-1450nm

and 1800-

1950nm

Ngie et al., 2014

0

10

20

30

40

50

60

0 500 1000 1500 2000 2500 3000

Re

fle

cta

nce

%

Wavelength (nm)

Mean_DKC

Mean_BT

Mean_BR

Mean_KKS

Mean_PAN

Mean_IMP

Mean_SC

Mean_P

Statistical analysis

The NIR region showed

successful results in

separating some

cultivars between three

and six out of the eight.

The intersection angles

showing no intersections

or slight intersections of

less than 90 degrees

mean significant

difference while those of

intersections greater

than 90 degrees are

insignificant

Ngie et al., 2014

37.5

40

42.5

45

47.5

50

52.5

B5

0

BR

BT

DK

C

IMP

KK

S

PA

N

PIO

NE

ER

SC

Cultivar

All Pairs

Tukey-Kramer

0.05

Statistical analysis (cont’d)

The Tukey-Kramer HSD Threshold matrix shows the actual

absolute difference in the means minus the HSD (Abs (Dif)-

HSD), which is the difference that would be significant. Pairs

with a positive value are significantly different.

Ngie et al., 2014

Abs(Dif)-HSD KKS BR PAN SC DKC IMP PIONEER BT

KKS -0.512 1.423 2.342 2.863 4.402 5.301 5.549 11.160

BR 1.423 -0.512 0.407 0.928 2.467 3.366 3.614 9.225

PAN 2.342 0.407 -0.512 0.008 1.548 2.447 2.694 8.305

SC 2.863 0.928 0.008 -0.512 1.027 1.926 2.174 7.785

DKC 4.402 2.467 1.548 1.027 -0.512 0.387 0.634 6.245

IMP 5.301 3.366 2.447 1.926 0.387 -0.512 -0.265 5.346

PIONEER 5.549 3.614 2.694 2.174 0.634 -0.265 -0.512 5.099

BT 11.160 9.225 8.305 7.785 6.245 5.346 5.099 -0.512

Statistical analysis (cont’d)

The Tukey-Kramer HSD test

resulted in 27 possible pair

combinations of discrimination

between the six out of eight

(75%) maize cultivars.

All pairs were significantly

different.

Ngie et al., 2014

Statistical analysis (cont’d)

Ngie et al., 2014

Level - Level Difference Std Err Dif Lower CL Upper CL p-Value Difference

B BT 11.67275 0.1603193 11.1603 12.18521 <.0001* BR BT 9.73775 0.1603193 9.2253 10.25021 <.0001* PAN BT 8.81783 0.1603193 8.3054 9.33030 <.0001* SC BT 8.29750 0.1603193 7.7850 8.80996 <.0001* DKC BT 6.75783 0.1603193 6.2454 7.27030 <.0001* B PIONEER 6.06125 0.1603193 5.5488 6.57371 <.0001* IMP BT 5.85883 0.1603193 5.3464 6.37130 <.0001* B IMP 5.81392 0.1603193 5.3015 6.32638 <.0001* PIONEER BT 5.61150 0.1603193 5.0990 6.12396 <.0001* B DKC 4.91492 0.1603193 4.4025 5.42738 <.0001* BR PIONEER 4.12625 0.1603193 3.6138 4.63871 <.0001* BR IMP 3.87892 0.1603193 3.3665 4.39138 <.0001* B SC 3.37525 0.1603193 2.8628 3.88771 <.0001* PAN PIONEER 3.20633 0.1603193 2.6939 3.71880 <.0001* BR DKC 2.97992 0.1603193 2.4675 3.49238 <.0001* PAN IMP 2.95900 0.1603193 2.4465 3.47146 <.0001* B PAN 2.85492 0.1603193 2.3425 3.36738 <.0001* SC PIONEER 2.68600 0.1603193 2.1735 3.19846 <.0001* SC IMP 2.43867 0.1603193 1.9262 2.95113 <.0001* PAN DKC 2.06000 0.1603193 1.5475 2.57246 <.0001* B BR 1.93500 0.1603193 1.4225 2.44746 <.0001* SC DKC 1.53967 0.1603193 1.0272 2.05213 <.0001* BR SC 1.44025 0.1603193 0.9278 1.95271 <.0001* DKC PIONEER 1.14633 0.1603193 0.6339 1.65880 <.0001* BR PAN 0.91992 0.1603193 0.4075 1.43238 <.0001* DKC IMP 0.89900 0.1603193 0.3865 1.41146 <.0001* PAN SC 0.52033 0.1603193 0.0079 1.03280 0.0443* IMP PIONEER 0.24733 0.1603193 -0.2651 0.75980 0.7799

Statistical analysis (cont’d)

The mean reflectance values for each maize cultivar in

summary indicates the six of eight cultivars separated

as levels not connected by same letter are significantly

different.

Ngie et al., 2014

Level Mean

KKS A 50.527250

BR B 48.592250

PAN C 47.672333

SC D 47.152000

DKC E 45.612333

IMP F 44.713333

PIONEER F 44.466000

BT G 38.854500

Conclusion

The results validated the

hypothesis that the genetic

constituent of different cultivars

at foliar level would influence

their reflectance levels at

different wavelengths through

high resolution sensors.

Ngie et al., 2014

Recommendations

There is the possibility of

integrating such in-situ

hyperspectral measurements

with space-borne hyperspectral

remote sensing data for

automatic identification and

discrimination of various maize

cultivars grown on the fields.

Ngie et al., 2014