A new optical sensor based on laser speckle and chemometrics for precision agriculture: application to sunflower plant-breeding - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Journal Articles Sensors Year : 2020

A new optical sensor based on laser speckle and chemometrics for precision agriculture: application to sunflower plant-breeding

Abstract

New instruments to characterize vegetation must meet cost constraints while providing accurate information. In this paper, we study the potential of a laser speckle system as a low-cost solution for non-destructive phenotyping. The objective is to assess an original approach combining laser speckle with chemometrics to describe scattering and absorption properties of sunflower leaves, related to their chemical composition or internal structure. A laser diode system at two wavelengths 660 nm and 785 nm combined with polarization has been set up to differentiate four sunflower genotypes. REP-ASCA was used as a method to analyze parameters extracted from speckle patterns by reducing sources of measurement error. First findings have shown that measurement errors are mostly due to unwilling residual specular reflections. Moreover, results outlined that the genotype significantly impacts measurements. The variables involved in genotype dissociation are mainly related to scattering properties within the leaf. Moreover, an example of genotype classification using REP-ASCA outcomes is given and classify genotypes with an average error of about 20%. These encouraging results indicate that a laser speckle system is a promising tool to compare sunflower genotypes. Furthermore, an autonomous low-cost sensor based on this approach could be used directly in the field.
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hal-02951895 , version 1 (27-05-2021)

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Maxime Ryckewaert, Daphné Heran, Emma Faur, Pierre George, Bruno Grezes-Besset, et al.. A new optical sensor based on laser speckle and chemometrics for precision agriculture: application to sunflower plant-breeding. Sensors, 2020, 20 (16), pp.4652. ⟨10.3390/s20164652⟩. ⟨hal-02951895⟩
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