Potential of hyperspectral imagery for nitrogen content retrieval in sugar beet leaves - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Conference Papers Year : 2014

Potential of hyperspectral imagery for nitrogen content retrieval in sugar beet leaves

Potentiel de l'imagerie hyperspectrale pour l'estimation de la teneur en azote dans les feuilles de betteraves sucrières

Abstract

Leaf nitrogen content (LNC) is one of the most important limiting key nutrients in sugar beet crops, so plant nitrogen status has to be carefully monitored throughout the plant life. In this study, close-range hyperspectral imaging was used to infer LNC from reflectance spectra in a non-destructive way and under in-field conditions. First, after acquisition, images were preprocessed in order to remove some sources of variability that were not correlated to LNC, such as specular reflection and spectral noise. For every hyperspectral image, the mean leaf spectrum was then evaluated and associated to the actual average LNC value measured on the same plants. Partial Least Square regression was used to calibrate a regression model. With six latent variables, LNC was accurately predicted with a low error and a high coefficient of determination (RMSECV = 1.72 g/kg; R² = 0.86). When applied to individual spectra of hyperspectral images, this model led to a consistent LNC map of sugar beet leaves, i.e., LNC was low in old nitrogen-deficient leaves and it was high in young wide leaves. Such a mapping is therefore a valuable non-destructive evaluation tool to better understand how LNC is distributed within plants and to identify LNC-deficient zones.
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Dates and versions

hal-01122086 , version 1 (03-03-2015)

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S. Jay, X. Hadoux, N. Gorretta, Gilles Rabatel. Potential of hyperspectral imagery for nitrogen content retrieval in sugar beet leaves. AgEng 2014, Jul 2014, Zurich, Switzerland. pp.8. ⟨hal-01122086⟩
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