1. How plant structure impacts the biochemical leaf traits assessment from in-field hyperspectral images: A simulation study based on light propagation modeling in 3D virtual wheat scenes
- Author
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Martin Ecarnot, Gilles Rabatel, Nathalie Al Makdessi, Pierre Roumet, Nathalie Gorretta, Pierre-Antoine Jean, Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Agropolis Foundation : 1202-008, 'Investissements d'avenir' program (Labex Agro) : ANR-10-LABX-0001-01, France Agrimer and l'Agence Nationale de la Recherche (ANR) through the Phenoble and Phenome programmes, and Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)
- Subjects
0106 biological sciences ,010504 meteorology & atmospheric sciences ,Feature vector ,simulation models ,Soil Science ,three dimensional model ,modèle de simulation ,01 natural sciences ,3D virtual scenes ,modelling ,leaf nitrogen content ,Partial least squares regression ,biochimie végétale ,modélisation ,triticum durum ,0105 earth and related environmental sciences ,Remote sensing ,Mathematics ,caribu ,diffusion de la lumière ,Spectrometer ,Ecology ,canopy light propagation ,modèle 3d ,Simulation modeling ,Hyperspectral imaging ,Regression analysis ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,Field (geography) ,multiple scattering ,Principal component analysis ,Agronomy and Crop Science ,010606 plant biology & botany - Abstract
AGAP : équipe GE2pop (Génomique évolutive et gestion des populations); Light propagation modeling in 3-dimensional virtual scenes has been successfully applied to many fields, including plant canopies. However, its application to detailed analyses on how multiple scattering affects spectral-based biochemistry assessments has never been proposed. In this article, a wheat canopy model has been built using simulation models included in the open source software platform Open-Alea. Adel-Wheat, a 3D dynamic model of the aerial growth of winter wheat, has been associated with spectra collected on wheat leaves with an ASD spectrometer, and then used as input of the Caribu light propagation model. Caribu calculates the proportion of direct and scattered light for all polygons of the 3D scene. Principal component analysis was first applied to analyze the distribution of resulting spectra in the spectral feature space. Then the influence of canopy structure on quantitative regression models has been considered. For this purpose, a typical agronomical problem, i.e. nitrogen content retrieval, was addressed, using a Partial Least Square regression model. This study exhibits some important results concerning the distribution of collected spectra in the spectral feature space due to multiple scattering, and underlines the physical interpretation of these results. In the short term, it shows that satisfactory nitrogen content prediction (error about 0.5% of dry matter) can be obtained at the plant level, when considering only the plant top leaves. Moreover, its paves the way for future researches to develop spectral analysis tools able to overcome such multiple scattering phenomena.
- Published
- 2017
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