Data acquisition (2013 field measurement campaign) Hyperspectral imaging

o Characteristics: - Wavelength range: 400-1000 nm (160 bands), - FWHM resolution : 4.5 nm / Spatial resolution : 0.6 mm.

o Considered plants: - 2 sugar beet varieties x 4 nitrogen fertilizations x 3 rows x 2 growth stages = 48 combinations.

Reference measurements (after image acquisition) o About 5 imaged plants, o Mean LNC measured over these plants using the Kjeldahl method.

Main objective of the AKER project: improve the competitiveness of sugar beet (Beta Vulgaris L.) by 2020 by doubling the annual increase of sugar yield / hectare (from 2% to 4%).

Development of effective and non-destructive phenotyping methods, a strong need for variety selection.

Nitrogen, one of the most important limiting key nutrients. How the Leaf Nitrogen Content (LNC) is distributed within plants has to be deeply understood in order to optimize nitrogen use.

VNIR spectroscopy, a well known technique for non-destructive LNC retrieval, either by using spectral indices [1] or chemometrics tools such as PCR or PLSR [2].

Hyperspectral imaging, a useful way to combine both spectral and spatial information into one multivariate image, to describe the LNC distribution within living plants, to identify LNC-deficient zones, and thus

to give detailed information about the actual nitrogen status.

POTENTIAL OF HYPESPECTRAL IMAGERY FOR NITROGEN CONTENT RETRIEVAL IN SUGAR BEET LEAVES

Sylvain JAY, Xavier HADOUX, Nathalie GORRETTA, Gilles RABATEL

IRSTEA, UMR ITAP, 361 rue JF Breton, 34196 Montpellier, France

Context

Results

Extend the data base with the data collected in 2014 so as to increase the model robustness against varieties and soil and climate conditions. Handle other effects (still uncorrected) related to the scene geometry (e.g., sky reflections). Use the SWIR range in order to evaluate directly the impact of nitrogen absorption on reflectance spectra.

Figure: Phenotyping platform: (a) Overall setup, and (b) Hyperspectral camera mounted on the translation stage

Figure: (a) True color composite image, (b) False color composite image after mean centering, and (c) Spectra measured with (solid lines) or without specular reflection (dashed lines).

Materials and methods

Perspectives

Spectral pre-processing Objective: Remove as much spectral variability not related to LNC as possible. Biochemical considerations

o Problem: in the VNIR range, nitrogen absorption ↓. o However, chlorophyll absorption ↑, and in general, LNC and chlorophyll content positively correlated. o NIR plateau mainly related to the anatomical structure and dry matter amount. o LNC retrieved through chlorophyll content

⇒ Wavelength range restricted to 400-700 nm, i.e. 79 bands (maximum chlorophyll absorption). Geometrical considerations

o Close-range remote sensing ⇒ problems related to the complex scene geometry: specular reflections (sun and sky), shadows, leaf inclination, multiple reflections. o Model proposed in [2]: Rmeas(λ)= α.Rleaf(λ)+ β

⇒ Preprocessing steps applied to every HS image: (1) Denoising, (2) Mean centering, and (3) Mean spectrum computation.

Partial Least Squares Regression (PLSR)

Objective: Build a regression model B (Q x 1) from the calibration set made up of N spectra X (N x Q) and LNC reference measurements Y (N x 1) (N=47, Q=79):

Y = X . B Problem: Spectral data highly correlated ⇒ Partial Least Squares Regression Procedure

o Calibration using leave-one-out cross-validation, o Number of latent variables best explaining the LNC

variability obtained by minimizing the RMSECV, o Regression model applied to individual spectra of HS images, o Estimated LNC distribution evaluated by visual inspection.

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Figure: Regression model obtained with the two varieties: (a) RMSECV versus the number of latent variables, and (b) LNC predicted values versus LNC actual values (six latent variables). .

Figure: (a) True color composite image, and (b) Estimated LNC map.

References [1] Wang, W., Yao, X., Yao, X., Tian, Y., Liu, X., Ni, J., et al. (2012). Estimating leaf nitrogen concentration with three-band vegetation in rice and wheat. Field Crops Research , 129, 90-98. [2] Vigneau, N., Ecarnot, M., Rabatel, G., Roumet, P. (2011). Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in wheat. Field Crops Research , 122, 25-31.

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