Share Email Print

Proceedings Paper

Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging
Author(s): Vincent Leemans; Guillaume Marlier; Marie-France Destain; Benjamin Dumont; Benoit Mercatoris
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

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).

Paper Details

Date Published: 28 April 2017
PDF: 10 pages
Proc. SPIE 10213, Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017, 102130I (28 April 2017); doi: 10.1117/12.2268398
Show Author Affiliations
Vincent Leemans, Univ. de Liège (Belgium)
Guillaume Marlier, Univ. de Liège (Belgium)
Marie-France Destain, Univ. de Liège (Belgium)
Benjamin Dumont, Univ. de Liège (Belgium)
Benoit Mercatoris, Univ. de Liège (Belgium)

Published in SPIE Proceedings Vol. 10213:
Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017
David P. Bannon, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?