Optical Sensing of Soil Macronutrient

Presented by:

Jyoti Singh

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Introduction

• Great Demand for Soil Property Data.

• Methods for soil attributes measurement relies mainly uponthe use of laboratory methods (More samples andmeasurements, time consuming).

• Problem with field measurement is the variation in soilmoisture content and surface roughness.

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Soil NPK Sensing Techniques

• Standard Soil Testing Laboratory– time consuming, Laborious, use of chemical and

reagents which effect human health andenvironment, costly, do not consider spatialvariation in the field.

• Electrochemical Sensing– Ion Selective Electrodes

– Ion Sensitive Field Effect Transistor

• Optical Spectroscopy– NIR Spectroscopy

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Optical Spectroscopy

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The Electromagnetic Spectrum

Electron Electron Molecular MolecularExcitation Transition Vibration Rotation

Wavelength λ µm 0.0001 0.01 0.1 1 10 100 1000Wavenumber cm-1 106 105 10000 1000 100 10 1

Spectral region X-ray UV Vis Infrared Microwave

NIR MIR FIR

Wavelength (nm) 0.7 2.5 25 100

III II I

Overtones Overtones Combination2nd N-H 1st C-H, N-H C-H, N-H, O-H3rd C-H 2nd C-H C=O

Weak Strong

Absorbance

)(10)( 41 mcm

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NIR and Light Interaction with matter

If the frequency of radiation matches with vibrational frequency of molecule, then radiation will be absorbed causing change in amplitude of molecule vibration.

SymmetricalStretching

AntisymmetricalStretching

Bending

• Utilizes the absorbance of NIR light (700 - 2500 nm) byvibrating bonds between atoms in molecules.

• O-H, C-H, C-N, C-O, P-O, S-O.

• Compositional information on samples (n~>100) iscorrelated with the spectral information to developstatistical calibration models.

• The calibrations “train” the instrument to analyze futureunknown samples.

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Extracting information from

spectral data

• Signal processing is used to transform spectral data prior to analysis.

• Data pretreatment

-Local filters

-Smoothing

-Derivatives

-Baseline correction

-Multiplicative Scatter Correction (MSC)

-Orthogonal Scatter Correction (OSC)

1. Qualitative information grouping and classification of spectral objects from samples into supervised and non-supervised learning methods.

2. Quantitative information relationships between spectral data and parameter(s) of interest.

How to extract the information?

1. Multivariate analysis (MVA)

Principal Component Analysis (PCA), Projection to Latent Structures (PLS), PLS-Discriminant Analysis (PLS-DA), …

2. Two dimensional correlation spectroscopy

Homo-correlation, Hetero-correlation

Regression by data compression

Regression on scores

PC1

t-score

y

q

ti

PCAto compress data

x1

x2

x3

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*Prediction of soil content using near-infrared spectroscopyYong He and Haiyan Song2006 SPIE—The International Society for Optical Engineering

N, PCA P, PCA K, PCA

Correlation between measured and predicted values of N, P and K

NIR Spectroscopy

Advantages• Minimal to no sample preparation.• Able to measure many constituents

simultaneously with high scanSpeed ( < 1sec).

• Quantitative and Qualitativeresults.

• No phase constraints – gas, liquidor solid.

• Non Destructive, non contact.• Faster, safer working environment

that does not require chemicals.• The availability of efficient

chemometric evaluation tools andsoftware as well as light-fiber opticshas made NIRS to an invaluable toolfor academic research andindustrial quality control.

Disadvantages

• Less information contained inspectra and Spectra is affected byparticle size, moisture etc.

• Combination and overtone bandsmake association with individualchemical groups more difficult.

• Generally can’t indentifycomponents of less than 1% inproduct hence need more robustcalibration techniques.

• Chemometrics – PCA, PLS.

• Robustness of calibrations needs tobe monitored.

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Soil Properties Predicted with NIR

• Sand, silt, clay• Organic C, organic matter, total C• C:N ratio• Biomass• Exchangeable Ca, Mg• Fe• N,P,K• pH• Water content• Electrical conductivity

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UV-Vis & UV-Vis-NIR Systems Cary 5000 UV-Vis-NIR

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Spectrometers

RED-Wave Micro MEMS NIR spectrometer from Stellar Net

Micro NIR Spectrometer

High resolution spectral data from a ruggedized field-portable spectroradiometer4/24/2017 17

Block Diagram

r

Light Source (Tungsten Halogen lamp)

Monochromator

Soil Sample

Photodetector

Processor

Display

Spectrometer

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References [1] R. A. Viscarra Rossel, D. J. J. Walvoort, A. B. McBratney, L. J. Janik, and J. O. Skjemstad, “Visible,

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[2] Y. Qiao and S. Zhang, “Near-infrared spectroscopy technology for soil nutrients detection basedon LS-SVM”, IFIP Advances in Information and Communication Technology, Vol. 368, pp. 325–335,2012.

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[7] D. Bertrand and C. N. G. Scotter, “Application of Multivariate Analyses to NIR Spectra ofGelatinized Starch”, Applied Spectroscopy, Vol. 46, No. 9, pp. 1420–1425, 1992.

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References [8] M. Urbano-Cuadrado, M. D. Luque De Castro, P. M. Pérez-Juan, J. García-Olmo, and M. A.

Gómez-Nieto, “Near infrared reflectance spectroscopy and multivariate analysis in enology:Determination or screening of fifteen parameters in different types of wines”, AnalyticaChimica Acta, Vol. 527, No. 1, pp. 81–88, 2004.

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