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PRESENTED BY MANJUSHREE MASHAL 2GI14LDN07

PRESENTATION ON:“NEAR INFRARED SPECTROSCOPY IN IMAGE PROCESSING”

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CONTENTS

INTRODUCTION HISTORY MATERIALS AND METHODS

FIELD IMAGE SPECTROSCOPY RESULTS AND DISCUSSION IMAGING SPECTROSCOPY APPLICATIONS CONCLUSION AND FUTURE WORK REFERENCE

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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).

INTRODUCTION

Figure 1.1 Range of Electromagnetic Radiation

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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

Intro Conti…

Basic Principle Of NIR

Figure 1.2: Vibrational transition

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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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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).

HISTORY

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MATERIALS AND METHODS

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A. Study siteField 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.

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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.

D. RANDOM FORESTS

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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.

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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.

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Linear regression (LR)

Stepwise multiple linear regression (SMLR)

Partial least squares regression (PLSR)

Support-vector machine (SVM) regression

Spectral Analysis Models for Chlorophyll Content Estimation

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RESULTS AND DISCUSSION 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.

NEAR INFRARED SPECTROSCOPY ADVANTAGE• Fast• Simple sample preparation• Non destructive• Online for process applications• Need for calibration• Opportunity for data analysis

Disadvantages of NIR

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.

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Other Application are Imaging spectroscopy for scene analysis.

Mineral mapping and Application.

Medical field

Industrial uses

Astronomical spectroscopy

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CONCLUSION

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.

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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.

REFERENCE1. 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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ANY QUESTIONS

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THANK YOU