Image processing Application.

1. PRESENTED BY
MANJUSHREE MASHAL
2GI14LDN07
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 INTRODUCTION
 HISTORY
 MATERIALS AND METHODS
 FIELD IMAGE SPECTROSCOPY
 RESULTS AND DISCUSSION
 IMAGING SPECTROSCOPY APPLICATIONS
 CONCLUSION AND FUTURE WORK
 REFERENCE

3. NIR spectroscopy is a vibrational spectroscopic method belongs to
the infrared light spectrum which is very close to the visible region
(from about 750 to 2500 nm).
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Figure 1.1 Range of Electromagnetic Radiation

4. NIR is a spectroscopic method based on molecular overtones and combination
of C-H ,O-H,N-H ions.
There are two laws which govern the basics of vibrational spectroscopy
 Hooke’s law and
 Frank principle
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Figure 1.2: Vibrational transition

5. Chlorophyll Estimation using NIR
 Chlorophyll is an extremely important biomolecule, critical in
photosynthesis, which allows plants to absorb energy from light.
The chlorophyll content in leaf is an indicator of grow health
condition and the soybean yield.
 Imaging spectroscopy has great potential for estimating chlorophyll
content of soybean dynamic, rapidly.
 The objective of this presentation is to combine random forests and
field imaging spectroscopy for estimating chlorophyll content in
soybean at leaf scale.
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6. Three centuries ago Sir Isaac Newton published in his ‘Treatise of Light’
the concept of dispersion of light.
 The history of NIR is begins with William Herschel in 18th century.
 The term spectroscopy was first used in the late 19th century and provided the
empirical foundations for atomic and molecular physics.
 A large number of spectral indices have been developed for the study of
chlorophyll content based on leaf reflectance. Reviews of different spectral
indices developed for estimating chlorophyll content are offered by Bannari et
al. (2007), He et al. (2006) and Haboudane et al. (2008).
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A. Study site
Field spectroscopy was carried out with an ASD FieldSpec3 in summer 2009,
at the farmlands of city of Chang’chun, Jinlin province. Chlorophyll content
of soybean was taken as researching target.
The spectral range between 350-1250 nm was used for the retrieval of leaf
chlorophyll concentration. Leaf chlorophyll concentration in soybean was
measured by SPAD502.
SPAD 502 Plus Chlorophyll Meter

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B. Field campaign data
Field campaigns in the soybean field were carried out under clear sky
conditions. Reflectance of soybean was collected by ASD3 spectrometer,
with a 350-2500nm spectral range.
C. PROSPECT model
PROSEPCT is a radiative transfer model to simulate the leaf reflectance
spectra with the range of 400 to 2500 nm. And it only needs a small amount
of input parameters like chlorophyll content, moisture, dry matter content
and structure parameter.

10.  Random forests is a classification and regression
algorithm originally designed for statistical machine
learning method, which is created by Breiman.
 Random forests were adopted to train the training
data set, in order to establish leaf chlorophyll content
estimation model.
 Advantages: Non parametric,
Non sensitive to overfitting,
Calculate classification error.
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FIELD IMAGE SPECTROSCOPY
A field imaging spectrometer system was designed for agriculture applications.
FISS was used to gather spectral information from soybean leaves. Leaf
estimation model of chlorophyll concentration was applied to the validation data
set to estimate leaf chlorophyll content of soybean in the research area and
validation data set was established based on proximal hyperspectral data.

12. FISS CONSISTS OF
 Opto-mechanical system: Scanning mirror, Optical lenses,
Spectroscopic devices and a Charge-coupled device (CCD)
camera.
 Electronic system: Power and Motor control circuits
 computer system: hardware and software
Hardware is a portable laptop computer, and the software
includes the FISS operating software, data acquisition
software and data processing software.
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Actual photograph of FISS field measurements.

14.  Linear regression (LR)
 Stepwise multiple linear regression (SMLR)
 Partial least squares regression (PLSR)
 Support-vector machine (SVM) regression
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STATISTICAL DESCRIPTION OF SOYBEAN AT
SAMPLING SITES

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Effect of number of trees in RF-PROSPECT model
for estimating chlorophyll content.

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Validation of the soybean chlorophyll content
estimation model.

18. • Fast
• Simple sample preparation
• Non destructive
• Online for process applications
• Need for calibration
• Opportunity for data analysis

19. Overlapping bands (combination), not easy to interpret.
Usually not for trace level analysis.

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APPLICATIONS
Forecasting corn yield with imaging spectroscopy.
Image spectroscopy to mapping canopy concentration in the
nitrogen.
Near Infrared Spectroscopic Analysis in the Food Industry and
Research.
Image spectroscopy in Forest application.

21. Other Application are
 Imaging spectroscopy for scene analysis.
 Mineral mapping and Application.
 Medical field
 Industrial uses
 Astronomical spectroscopy
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22. CONCLUSION
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This work established the soybean chlorophyll based on random forests and
PROSPECT, The estimation model can be used as an effective tool for estimation
of soybean chlorophyll content, and can be adopted to precision agriculture
management.
Future study will concentrated on scaled up the field estimation model to
satellite remote sensing level, which will monitor the soybean’s health condition in
a large scale.

23. 2. D.Sathis Kumar et al“ Near Infra Red Spectroscopy- An Overview” International
Journal of ChemTech Research CODEN( USA): IJCRGG ISSN : 0974-4290 Vol. 3,
No.2, pp 825-836, April-June 2011.
3.M.E. Schaepman, S. L. Ustin, and A. J. Plaza, “Earth system science related
imaging spectroscopy—An assessment, ”. Remote Sensing of Environment, vol.
113, pp. 123-S137, 2009.
4.J. Delegido,L. Alonso, and L.G. Gonzalez, “Estimating chlorophyll content of crops
from hyperspectral data using a normalized area over reflectance curve (NAOC)”.
International Journal of Applied Earth Observation and Geoinformation, vol. 13,
pp. 165-174, 2010.
5. P.O. Gislason, J.A. Benediktsson, and J.R. Sveinsson, “Random Forests for land
cover classification”, Pattern Recognition Letters, vol. 27, pp. 294-300, 2006.
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1. Jie Lv, Zhenguo Yan “Estimating Leaf Chlorophyll Concentration in Soybean
Using Random Forests and Field Imaging Spectroscopy”IEEE Transactions On
Geoscience And Remote Sensing.

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25. THANK YOU
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