Yu Ke-Qiang, Zhao Yan-Ru, Li Xiao-Li, Shao Yong-Ni, Liu Fei, He Yong
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China; Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture, Beijing, China.
PLoS One. 2014 Dec 30;9(12):e116205. doi: 10.1371/journal.pone.0116205. eCollection 2014.
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.
采用可见/近红外（Vis/NIR）高光谱成像技术来测定辣椒植株中总氮的空间分布。采集了样本（辣椒植株的叶片、茎和根）的高光谱图像，并使用杜马斯燃烧法测量了它们的总氮含量（TNC）。从高光谱图像中的感兴趣区域（ROI）提取所有样本的平均光谱。实施随机蛙（RF）算法来选择分别携带用于预测叶片、茎、根和全株（叶 - 茎 - 根）中总氮含量有效信息的重要波长。基于全光谱和所选的重要波长，分别使用偏最小二乘回归（PLSR）建立了器官（叶、茎和根）以及全株（叶 - 茎 - 根）中光谱数据与相应总氮含量之间的定量关系。结果，由用于预测全株（叶 - 茎 - 根）中总氮含量的重要波长构建的PLSR模型在预测相关系数（R）（RP = 0.876）和预测均方根误差（RMSE）（RMSEP = 0.426%）方面给出了有前景的结果。最后，估计样本ROI内每个像素的总氮含量，以生成辣椒植株中总氮含量的空间分布图。该研究成果表明，高光谱成像技术在测定辣椒植株氮含量空间分布方面具有前景且展现出强大潜力。
