Near-infrared spectroscopy (NIRS) can be used to non-destructively assess seed quality by classifying seeds and detecting damage. A study classified healthy and damaged soybean seeds using NIRS with over 97% accuracy. The study collected NIR spectra of soybean seeds in different categories and developed partial least squares (PLS) models to classify seeds and discriminate damage types based on their spectra. The models accurately classified healthy versus damaged seeds and identified specific damage types such as weathering, frost, sprouting, and mold. NIRS is shown to be an effective technique for non-destructive seed quality assessment and classification.

1. .......First the seeds
~American seeds
trade association

2. Saumya Shruti
Ph.D. 3rd Sem
SST 692
Anand Agricultural University
Near – Infra Red Spectroscopy for Predicting
Seed Germination and Vigour in Pulses

3. Contents of Seminar
Introduction
Use of Spectroscopy in Agriculture
Near-infrared Spectroscopy use in Seed Science
Case Studies
Conclusion
Future Aspects
3

4. Outline
Seed
• Seed is one of the basic inputs for enhancing production.
• High-quality seed is the cornerstone of any successful agriculture program.
• It is a good marketing tool for increasing the potential sale of crops,
especially in today’s competitive market.
Focus
area
• Crucial element for providing enough food security for the rising population,
which is expected to exceed nine billion by year 2050.
• Adopting an efficient method to evaluate the seed quality non-destructively is the
need of hour.
Technique
• One such technique is the use of NIRS which helps to assess seed quality non-
destructively and sort out seeds based on seed health, seed deterioration,
viability, vigour including protein, starch and fatty acid composition as well as
abiotic and biotic seed damage.
• It is a non-destructive analytical technique requires little sample preparation
time and high-throughput, which makes it reasonable technique.
4

5. Pulses Overview
5

6. Pulses Overview
• Production of Tur, Urd and other lentils in FY 2022 is
about 26.9 mt
• Area vailable : 28.8 mha
• Average growth of pulse production during last 5 years
:10.89%
• Area under cultivation(2020-21) – 0.9
mha
• Production(2020-21) – 1.06 mt
(Source :www.statistica.com, www.pib.gov.in) 6

7. When you want to know the viability of seed lots which method
you will choose?
1. Conventional or Destructive
Techniques
2. Non-Destructive Techniques
7

8. Quality Assessment of Seeds
Destructive methods Non-Destructive Methods
• Traditional or conventional seed quality
testing test:
• Physical test
Biochemical test
Molecular test
• Most reliable method
• Manual interpretation
• Modern methods based on High through
put techniques
• Evaluates properties of material or
component
• Rapid and valuable technique
• Machine interpretation
Seed quality is of great importance in optimizing cost of crop establishment
8

9. Machine
vision
Electronic
-nose
SoftX-Ray
imaging
Techniques
Spectro
-scopy
Hyperspec
-tral
imaging
Thermal
imaging
Non – Destructive techniques • Spectroscopy measures the absorption,
transmission, and emission of
electromagnetic radiation by light and
other materials based on the
wavelength of the radiation.
• Large scale phenotyping of seed
morphometric,quality and agronomic
crop product.
Different techniques -
• UV -VIS
• Fluorescence Spectroscopy
• Infra red Spectroscopy
• Mid infra red Spectroscopy
• Near infra red Spectroscopy
• Nuclear Magnetic Resonance
• Atomic Emission 9

10. Near –Infrared Spectroscopy in Agriculture
1
Most promising and non-destructive
methods
2 Robust analytical methodology
3
Preferred method for routine analysis
4 Accuracy and efficiency of process
5
Used in broad array of Agricultural
fields
Fig 1 : Why infrared detected from
plants
10

11. Seed Science contributes
significantly to the Sustainable
Development Goals (SDG)
Introduces the fundamentals of Infrared
reflectance spectroscopy
Selection and classification
of seeds
11

12. Seed Science contributes
significantly to the Sustainable
Development Goals (SDG)
Introduces the fundamentals of Infrared
reflectance spectroscopy
Selection and classification
of seeds
12

13. Seed Science contributes
significantly to the Sustainable
Development Goals (SDG)
Introduces the fundamentals of Infrared
reflectance spectroscopy
Selection and classification
of seeds
13

14. 1992
NIR Spectroscopy used for
quality traits of intact
seeds of cereals and oilseeds
1962
Karl Norris developed
first application of
NIRS for grain and seed
analysis
18th century
William Herschel
discovered Infra red from
fire radiation
1835
Ampere named ‘Infrared radiation’ and
demonstrated wavelength difference
between radiant heat and light
Karl Norris is regarded as the “Father” of Modern
Near-infrared Spectroscopic Analysis. He invented
the technique while working at the USDA
Instrumentation Research Laboratory, Beltsville,
USA.
(USDA-United States Department of Agriculture) 14
Chronological Events

15. Why we need NIRS
NIRS
NIR spectroscopy works
on the principle of
interaction of
electromagnetic radiation
with matter.
Drawbacks of
Destructive methods
• Damage to material
• More time-consuming
and expensive
• Less efficient
• Sample requirement is
high.
Reasons
• Increasing demand for
product quality
improvement and
product rationalization
• Environmentally
compatible analytical
tools.
15

16. WORKING AND PRINCIPLE OF NIRS
NIRS is an absorption spectroscopy method that helps determine the
chemical composition of a compound or solution by measuring how
much near-infrared radiation the compound or solution absorbs.
Spectral signature
16

17. Working of NIRS
Different functional groups bends,
stretch and wags at different
frequencies
A functional group will absorbs light on the
same frequency of bending, stretching or
wagging
IR spectra show absorption bands
that enable to determine certain
functional groups presence
IR Spectrophotometer
Detector
Light
source
Functional group Wavenumber
C-H 2850-3300
C=O 1680-1750
C-O* 1000-1300
O-H(Alcohols) 3230-3550
O-H(acids) 2500-3300(very broad)
Infrared Spectroscopy Correlation table
(*Remember functional groups gives different peaks in different graph)
Absorption
17

18. Working of NIRS
Different functional groups bends,
stretch and wags at different
frequencies
A functional group will absorbs light on the
same frequency of bending, stretching or
wagging
IR spectra show absorption bands
that enable to determine certain
functional groups presence
IR Spectrophotometer
Detector
Light
source
Functional group Wavenumber
C-H 2850-3300
C=O 1680-1750
C-O* 1000-1300
O-H(Alcohols) 3230-3550
O-H(acids) 2500-3300(very broad)
Infrared Spectroscopy Correlation table
(*Remember functional groups gives different peaks in different graph)
Transmittance 18

19. IR Spectra
1500
percent
Percent
Transmittance
Absorbance bands
IR Spectra Interpretation
19

20. NIR data interpretation steps
Sample selection and scanning
NIR Reflectance Spectra
Multivariate Projection
methods
Spectral data
Processing
Division of Spectral Range
Classification of seeds
20

21. INSTRUMENTS USED IN NIRS
Infra tech Grain Analyzer
Hand Held Sensors
Fiber Optics probes
NIR spectroscopy in wavelength (a) 900-1700nm
(b) 913-2519 nm
21

22. INSTRUMENTS USED IN NIRS
Hyperspectral system set-up using long-wave near-infrared to detect infestation in mung bean seeds. The
components of the system are: 1. Mung bean sample, 2. Liquid crystal tunable filter (LCTF), 3. Lens, 4.
NIR camera, 5. Copy stand, 6. Illumination (Halogen-tungsten lamp), 7. Data processing system
22

23. Seed
quality
evaluation
Detect
biological
contaminants
Industrial
application
Control
crop
parameters
Plant health detection Yara grain analyzer by AVANTES Food quality analysis Seed quality evaluation
Application areas of NIRS in Agriculture
23

24. Saves time and
Labour
Advantages of NIRS

25. NIRS DETECTION METHODS IN SEEDS
NIRS based
seed testing
Single Seed
level
Bulk seed lot
Fig 2: The possible interaction of incident light(Io) with seed
and subsequent reflected, refracted, transmitted, scattered or
absorbed light(I)
Mainly used for analysis of Wheat, Soy
bean and Corn and gives Spectral
signature
Each spectrum represents variations
within seed lots .
Finger Printing of
Individual Seeds.
NIRS allows for the selection
and classification of seeds
according to specific traits and
attributes without alteration of
their properties.
25

26. 26

27. Application of NIRS in Seed Quality Assessment
Varietal Identification
Chemical Composition
Seed Viability
Seed Vigour and Germination
Insect Damage and Diseases
27

28. Quality assessment of seeds: Variety Identification and Classification
Variety
classification/
identification
Seed Feature(s)/spectr
a region (nm)
Result References
Grading Bean Size, colour 69.1–99.3% Kulik et al., 2007
Variety
Identification
Bean Morphology 82.4–100% Venora et al., 2009
GM, non-GM Soybean 400–2500 97% Lee and Choung,
2011
Hardness Maize 960–2498 RMSEP = 0.18,
0.29
Williams et al., 2009
Variety
Identification
Rice 1039–1612 80–100% Kong et al., 2013
• The microstructure and chemical composition of specific seed coat cell layers give rise to species and
varieties differences. Most morphological features of the seed coat are relatively insensitive to
environmental conditions and therefore very useful for taxonomic identification.
Dwivedi et al. (2020)
(RMSEP- Root-Mean-Squared-Error)
28
Table 1: List of studies conducted in the areas of variety identification

29. Quality assessment of Seed : Germination and Seed Vigour
• Standard germination percentages provide an estimation of a seed lot’s potential for
germination and seedling establishment under favorable conditions.
• Seed vigor may be defined as the potential for rapid uniform emergence and
development of normal seedling under a wide range of field condition.
• Hard seeds (physical dormancy) which are impermeable or semi-permeable and hence
do not absorb water.
29

30. Quality assessment of seeds: Seed Viability
• A good-quality seed is one that is capable of germination under various conditions.
• A non-viable seed is one that fails to germinate even under optimal conditions. In
recent years, non-destructive techniques, mainly spectroscopy and hyperspectral
imaging, have been widely used to predict seed viability.
Fig 3: Viable and Non-
Viable seed detection
30
Application Seed Feature(s)/spect-
ra region (nm)
Result References
Germination
ability
Muskmelon 948–2494 948–2494 Kandpal et al.,
2016
Viable and non-
viable seeds
Gourd 1100–2500 96%, 95 Min and Kang,
2003
Viable and
empty seeds
Patula pine 400–2498 96%, 88% Tigabu and
Odén, 2003
Seed
germinability
Soybean, snap
bean
60 Hz–8 MHz R2 = 0.27–0.49,
0.44–0.50
Vozary et al.,
2007
Table 2: List of chemical composition studies in the NIR in different spectra region

31. • Presence of different functional group
detection by NIR spectra used to
determine the internal composition of
seeds.
• Tannins, phenols, waxes, pigments,
germination inhibitors and other
substances are found in the seed
covering structures of different species,
and these may influence the function of
the seed coat and subsequently the
physiological development of the seed.
Quality assessment of seeds: Chemical Composition
Fig 4 : Spectra for approximate absorption for
different chemical composition of seed sample
31

32. Quality assessment of seeds: Insect Damage and Diseases
Insect
damage/diseas
es
Seed Spectra
region (nm)
Result References
Insect-damaged Soybean 900-1700 40-94% Chelladurai et
al., 2014
Defect
detection
Soybean 600-1100 84–100% Wang et al.,
2004
Fungal-
damaged
Soybean 400-700 91.7%, 90.5% Lee et al.,
2016
• Seed damage by insects, fungi or natural causes, are an important factor in seed
quality during storage and processing, causes significant loss in seed quality.
• Seed damage is therefore taken seriously by consumers and the food industry.
• This technique is mainly used to identify the seeds infestation.
Huwang et al. (2015) 32
Table 3 : List of studies conducted in the areas of insect damage and pest

33. Case
Studies

34. Objective
To classify sound and damaged seeds and discriminate among various types of damage using NIRS.
Kansas (USA)
Wang et al.(2002)
Materials and Methods:
• Six different categories of soybean seeds
• Kernel colour measurement
• Model – PLS for 2 class and 6 class model
• Seeds classified using grain inspector
34

35. Table 4 : Characteristics of sound and damaged soybean samples and the number of
soybean seeds used for classification of sound and damaged soybeans
35

36. Callibration Results[a] Validation Results[b]
Spectral
Region
F[c] Sound Damaged Average Sound Damaged Average
490-750nm 6 98.8 98.4 98.6 98.2 97.8 98.0
750-
1690nm
10 100 100 100 99.7 99.7 99.7
490-
1690nm
10 99.7 99.3 99.5 99.7 99.6 99.6
Table 5. Classification accuracy (%) of sound and damaged soybean seeds using two–class partial
least squares (PLS) models
[a] Total number of soybean seeds in the calibration sample set = 800.
[b] Total number of soybean seeds in the validation sample set = 800.
[c] F = number of PLS regression factors.
36

37. Table 6. Classification accuracy of sound and damaged soybean seeds using six–class partial least squares
(PLS) models
Classification Accuracy (%)
Sample Sets *Sound Weather
damaged
Frost
damaged
Sprout
damaged
*Heat
damaged
Mold
damaged
Average
Calibration set[b]
490-750nm 85.0 64 53 80.0 88.0 68.0 70
750-1690nm 88.0 62 72 56.0 86.0 87.0 75.2
490-1690nm 70.5 57 60 50.0 77.0 80 65.8
Validation set[c]
490-750nm 86.5 67 45 76.0 84.0 77.0 72.5
750-1690nm 90.2 61 72 54.0 84.0 76 74.5
490-1690nm 70.2 79 71 40.0 55.0 81 66.0
[a] Number of PLS regression factors = 10.
[b] Total number of soybean seeds in the calibration sample set = 650.
[c] Total number of soybean seeds in the validation sample set = 800
37

38. Figure 5. NIR absorption curves for sound
and damaged soybean seeds.
Figure 6. Beta coefficients curve of PLS
model for classification of sound and
damaged soybean seeds.
38

39. Kansas (USA) Wang et al.(2010)
Objective:
To classify healthy and fungal-damaged soybean seeds and discriminate among various
types of fungal damage using near-infrared (NIR) spectroscopy.
Materials and methods:
• Soybean seeds of 5 categories
• Spectra collected with a diode-array NIR spectrometer
• Data analysis model: PLS
• Grams/32 software-for seed colour detection
39

40. Calibration Results[a] Validation Results[b]
Spectral
Region
F[c] Sound Damaged Average Sound Damaged Average
490-750nm 6 99.2 97.0 97.8 99.6 97.8 98.4
750-
1690nm
10 99.6 97.3 98.3 99.6 98.0 98.6
490-
1690nm
10 100 99.8 99.8 100 98.5 99.1
Table 7. Classification accuracy (%) of healthy and fungal-damaged soybean seeds using two-class
PLS models.
[a] Total number of soybean seeds in the calibration sample set = 650.
[b] Total number of soybean seeds in the validation sample set = 650.
[c] F = number of PLS regression factors.
40

41. Table 8: Effect of wavelength region on classification accuracies (%) of healthy and fungal damaged
soybean seeds using five-class models
Classification Accuracy (%)
Sample Sets Healthy Phomopsis C.kikuchii SMV Downy mildew Average %
Calibration set[b]
490-750nm 85.0 64 53 80.0 88.0 68.0
750-1690nm 88.0 62 72 56.0 86.0 87.0
490-1690nm 70.5 57 60 50.0 77.0 80
Validation set[c]
490-750nm 86.5 67 45 76.0 84.0 77.0
750-1690nm 90.2 61 72 54.0 84.0 76
490-1690nm 70.2 79 71 40.0 55.0 81
[a] Number of PLS regression factors = 10.
[b] Total number of soybean seeds in the calibration sample set = 650.
[c] Total number of soybean seeds in the validation sample set = 800 41
(SMV-Soybean Mosaic Virus)

42. Figure 5:Average visible and near-infrared
absorption curves for healthy soybean
seeds and fungal damaged seeds.
Figure 6: Beta coefficients curve of PLS
model for classification of healthy and fungal
damaged soybean seeds.
42

43. Deajeon (Korea) Kandpal et al.(2013)
Objective:
To discriminate various types of damaged soybean seeds from healthy seeds using HSI system in the range of 700-
1000 nm.
Materials and methods:
• Different varieties of seeds were taken
• 160 seeds prepared for investigation
• Defected seeds includes-fungal damage, growth mask and seed coat damage
• Germination test was conducted after spectra collection
• Hyperspectral Visible/Near Infrared (VIS/NIR) Imaging Technique

44. Fig 7 : VIS/NIR hyperspectral system
components
Fig 8: Mean Spectra of sound and damage
soybean samples
Intensity
Wavelength
44

45. Table 9:Classification results of PLS-DA sound and damaged soybean seeds : (a) calibration set (b) validation
set
45

46. Beta coefficient curve of PLS-DA
Wavelength
Intensity
Fig 9 : Beta coefficient of PLS-DA for
soybean seeds
Fig 10:PLS-DA images of sound and
defected seeds
46

47. Nakhon Pathom, Thailand
Objective
To investigate the possibility of applying the NIRS technique to separate hard seeds from normal seeds using a
classifying model could compensate for the effect of bean orientation.
Materials and Methods:
• Sample preparation-200 seeds of 2 different varieties
• Germination test after 3 days of spectra acquisition
• Classification Model-PLS-DA (Partial Least Square –Discriminant Analysis)
Phuangsombut et al. (2017) 47

48. Fig 11: Three orientations of mung bean
seed for measurement: (a) hilum face-up,
(b) hilum face-down and (c) hilum-parallel-
to the-ground
Fig. 12: Average absorbance of mung beans at different hilum
orientations (HD: hilum face-down, HU: hilum face-up, and HP: hilum-
parallel-to-the-ground)
48

49. Table 10 : Results of classification performance of calibration models based on absorbance of single
kernel of mung bean
**r- Correlation Coefficient, RMSECV – Root Mean Square Error of Cross Validation ,RMSEP-Root Mean Square Error of Prediction
***Number in parenthesis indicates the total number of seeds for classification
****T=Transmittance, R= Reflectance
49

50. Fig. 14. Second derivative of absorbance
for sound and dead mung bean seeds.
Fig 15 :Regression coefficient plot with
respect to wavelength of model predicting
mung bean seed orientation.
50

51. Fig. 13: PLS score plot of score 1 versus score 2 showing two mostly separated
clusters of sound mung bean seeds and dead mung bean seeds.
51

52. Baghdad (Iraq) Al-Amery et al.(2018)
Objective:
To develop NIRS predictive models for seed germination and vigour using a large data set from 81 soybean
seed lots that naturally varied in their seed quality
Materials and methods:
• Eighty-one soybean (Glycine max, cv. ‘Essex’) seed samples were obtained from different lots produced
at the University of Kentucky research farm over 8 years (2007 to 2014).
• The samples were stored in open plastic bags in a 10°C and 50% relative humidity room.
• Soyabean (Glycine max)
• Characterization –Near Infra Red Spectroscopy
52

53. Standard germination Accelerated ageing
Percentage No. of seed lots Percentage No. of seed lots
100 11 100 4
90-99 44 90-99 22
80-89 12 80-89 14
60-79 9 60-79 5
<59 5 <59 36
Table 11: Range of seed quality for 81 seed lots of soybean indicated by standard germination and accelerated ageing.
Figure 16: Average absorbance spectra
(log 1/reflectance) for samples
differentiated into low and high
germination.
53

54. Number of spectral data per sample (three spectra per
sample)
Quality parameter Category Training set Validation
Germination (%) Low 21 21
High 162 39
Low 12-13 5-6
Vigour (%) Medium 31-33 9-11
High 54 18
Table 12. Distribution of soybean seed samples based on number of spectral data
used as training and validation samples for prediction of germination and vigour
54

55. Training data set Validation data set
Correct classification(%)
Prediction model Sample
set
Factors R2 SECV* Predicted Low high
Qualitative
germination(%)
1 5 0.3944 0.2904 - 1.5
1.7
47.6
85.7
100.00
89.7
2 6 0.4720 0.2712 - 1.5
1.7
47.6
85.7
100.00
82.05
3 8 0.5214 0.2586 - 1.5
1.7
47.6
85.7
97.44
89.74
R2
Quantitative
Germination
(%)
1 12 0.6034 12.55 0.659 - - -
2 10 0.5748 11.42 0.590 - - -
3 10 0.6733 11.42 0.549 - - -
Table13 : Quantitative and Qualitative determination of germination using training data set and the resulting
classifications of validation samples
55
(*SECV-
Standard Error
of Cross
Validation)

56. Figure 17: Average absorbance spectra
(log 1/reflectance) of high-germination
soybean seeds differentiated into low,
medium and high vigour
56
Low vigour
Medium vigour
High vigour

57. Figure 19: Actual versus NIR-predicted quantitative accelerated
ageing values of validation samples for three sample sets: (A)
sample set 1, (B) sample set 2, and (C) sample set 3.
Figure 18: Actual versus NIR-predicted
quantitative accelerated ageing values of
validation samples for three sample sets: (A)
sample set 1, (B) sample set 2, and (C) sample set
3
57

58. LIMITATIONS
Development and validation of appropriate statistical models to classify future seeds
and a better understanding of these models i.e., specification of seeds to certain
groups.
 Advance knowledge of data analysis and machine operating.
 Initial dependence and reliability to an alternative external reference method like
HPLC and GLC etc.
 High detection only allows to quantify compounds above trace concentration.
58

59. Conclusions
59
 Useful tools for breeders interested in vigour genetics and germplasm preservation programs where
high germination/vigour of individual seeds could be identified in ageing seed lots.
 Physiological seed quality is often reflected in the chemistry of the seed and therefore information
from the NIR wavelength regions is often very informative.
 NIRS technology used to classify between healthy or sound seeds and damaged or fungal damaged
seeds using PLS models.
 NIRS combined with Hyperspectral Imaging System is a good potential tool for accurate and rapid
detection of damaged seeds.
 NIRS classification model based on a combination of both transmission-absorption spectra and
reflection-absorption spectra yielded better performance than the model based on only
transmission-absorption spectra.

60. Future Aspects
• Single seed and bulk NIRS to characterize seed covering structures is a future potential
for the development of specific applications in seed testing.
• Multi-disciplinary studies between seed research and data science may combine the
required insights in seed biology and data .
• Standardization of spectral acquisition accessories during single seed detection
technology will greatly improve its applicability in the future.
60

61. - Spectroscopy Plays a Important role
in the future of Smart Agriculture