Reference : Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Multidisciplinary, general & others
Life sciences : Agriculture & agronomy
Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging
Leemans, Vincent mailto [Université de Liège - ULiège > TERRA Teaching and Research Centre > > >]
Marlier, Guillaume [Université de Liège - ULiège > TERRA Teaching and Research Centre > > >]
Destain, Marie-France mailto [Université de Liège - ULiège > Ingénierie des biosystèmes (Biose) > Biosystems Dynamics and Exchanges >]
Dumont, Benjamin mailto [Université de Liège - ULiège > Agronomie, Bio-ingénierie et Chimie (AgroBioChem) > Ingénierie des productions végétales et valorisation >]
Mercatoris, Benoît mailto [Université de Liège - ULiège > TERRA Teaching and Research Centre > > >]
Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017
SPIE Commercial + Scientific Sensing and ImagingSPIE D
9 - 13 April 2017
[en] leaves nitrogen concentration ; multispectral imaging ; reflectance and textural attributes ; wavelength selection
[en] Precision agriculture can be considered as one of the solutions to optimize agricultural practice such as nitrogen fertilization. Nitrogen deficiency is a major limitation to crop production worldwide whereas excess leads to environmental pollution. In this context, some devices were developed as reflectance spot sensors for on-the-go applications to detect leaves nitrogen concentration deduced from chlorophyll concentration. However, such measurements suffer from interferences with the crop growth stage and the water content of plants. The aim of this contribution is to evaluate the nitrogen status in winter wheat by using multispectral imaging. The proposed system is composed of a CMOS camera and a set of filters ranged from 450 nm to 950 nm and mounted on a wheel which moves due to a stepper motor. To avoid the natural irradiance variability, a white reference is used to adjust the integration time. The segmentation of Photosynthetically Active Leaves is performed by using Bayes theorem to extract their mean reflectance. In order to introduce information related to the canopy architecture, i.e. the crop growth stage, textural attributes are also extracted from raw images at different wavelength ranges. Nc was estimated by partial least squares regression (R² = 0.94). The best attribute was homogeneity extracted from the gray level co-occurrence matrix (R² = 0.91). In order to select in limited number of filters, best subset selection was performed. Nc could be estimated by four filters (450 ± 40 nm, 500 ± 20 nm, 650 ± 40 nm, 800 ± 50 nm) (R² = 0.91).
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