Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2014 Dec 30;9(12):e116205.
doi: 10.1371/journal.pone.0116205. eCollection 2014.

Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant

Ke-Qiang Yu  1 , Yan-Ru Zhao  1 , Xiao-Li Li  2 , Yong-Ni Shao  2 , Fei Liu  2 , Yong He  2
Affiliations

Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant

Ke-Qiang Yu et al. PLoS One. .

Erratum in

Abstract

Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (RP = 0.876) and root mean square error (RMSE) for prediction (RMSEP = 0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Positions of sampled leaf, stem, and root samples in one exemplary pepper plant.
Figure 2
Figure 2. The flowchart of the Random frog algorithm from Li et al. .
Figure 3
Figure 3. Spectral curves of all pepper plant samples covering the range of 420–1,000 nm.
(a) mean spectral reflectance curves of all leaves and stems in upper, middle, and lower positions; (b) mean spectral reflectance and standard deviation (SD) of leaves, stems, and roots across all samples.
Figure 4
Figure 4. Selection probability (SP) of each wavelength averaged over 50 runs of Random frog for TNCs-HSI prediction of (a) leaves, (b) stems, (c) roots, and (d) whole-plant samples (leaf-stem-root).
Figure 5
Figure 5. Spatial distribution maps of TNCs-HSI in samples of an exemplary tested pepper plant included six leaves, three stems, and one root, respectively.
TNCs-HSI of samples in hyperspectral images were computed based on linear function (2) and TNCs-HSI distribution was achieved in MATLAB software. The numbers accompanying each sample map denote the respective TNC-DC value.
See this image and copyright information in PMC

References

    1. Johnson CD, Decoteau DR (1996) Nitrogen and potassium fertility affects jalapeño pepper plant growth, pod yield, and pungency. HortScience 31:1119–1123.
    1. Bosland PW (1992) Chiles: a diverse crop. HortTechnology 2:6–10.
    1. FAOSTAT (2001) Crop water information: pepper. FAO Water Development and Management Unit. Available: http://www.fao.org/nr/water/cropinfo_pepper. html.
    1. Feng L, Fang H, Zhou WJ, He Y (2006) Nitrogen stress measurement of canola based on multi-spectral charged coupled device imaging sensor. Spectroscopy and Spectral Analysis 26:1749–1752. - PubMed
    1. Van Hezewijk BH, De Clerck-Floate RA, Moyer JR (2008) Effect of nitrogen on the preference and performance of a biological control agent for an invasive plant. Biological Control 46:332–340.

Publication types