The internal cycling of nitrogen (N) storage and consumption in trees is an important physiological mechanism associated with tree growth. Here, we examined the capability of near-infrared spectroscopy (NIR) to quantify the <svg xmlns="http://www.w3.org/2000/svg" id="M1" style="vertical-align:-0.1876707pt" height="8.50298pt" version="1.1" viewBox="-0.0498162 -8.31531 10.1887 8.50298" width="10.1887pt"><g transform="matrix(.0125,0,0,-0.0125,0,0)"><path d="M766 662H679L558 178L360 662H239L74 0H161L301 564L532 0L597 -14L766 662Z"/></g></svg> concentration across tissue types (needle, trunk, branch, and root) without time and cost-consuming. The NIR spectral data of different tissues from slash pine trees were collected, and the <svg xmlns="http://www.w3.org/2000/svg" id="M2" style="vertical-align:-0.1876707pt" height="8.50298pt" version="1.1" viewBox="-0.0498162 -8.31531 10.1887 8.50298" width="10.1887pt"><g transform="matrix(.0125,0,0,-0.0125,0,0)"><path d="M766 662H679L558 178L360 662H239L74 0H161L301 564L532 0L597 -14L766 662Z"/></g></svg> concentration in each tissue was determined using standard analytical method in laboratory. Partial least squares regression (PLSR) models were performed on a set of training data randomly selected. The full-length spectra and the significant multivariate correlation (sMC) variable selected spectra were used for model calibration. Branch, needle, and trunk PLSR models performed well for the <svg xmlns="http://www.w3.org/2000/svg" id="M3" style="vertical-align:-0.1876707pt" height="8.50298pt" version="1.1" viewBox="-0.0498162 -8.31531 10.1887 8.50298" width="10.1887pt"><g transform="matrix(.0125,0,0,-0.0125,0,0)"><path d="M766 662H679L558 178L360 662H239L74 0H161L301 564L532 0L597 -14L766 662Z"/></g></svg> concentration using both full length and sMC selected NIR spectra. The generic model preformatted a reliable accuracy with R<sup>2</sup><sub>C</sub> and R<sup>2</sup><sub>CV</sub> of 0.62 and 0.66 using the full-length spectra, and 0.61 and 0.65 using sMC-selected spectra, respectively. Individual tissue models did not perform well when being used in other tissues. Five significantly important regions, i.e., 1480, 1650, 1744, 2170, and 2390 nm, were found highly related to the <svg xmlns="http://www.w3.org/2000/svg" id="M4" style="vertical-align:-0.1876707pt" height="8.50298pt" version="1.1" viewBox="-0.0498162 -8.31531 10.1887 8.50298" width="10.1887pt"><g transform="matrix(.0125,0,0,-0.0125,0,0)"><path d="M766 662H679L558 178L360 662H239L74 0H161L301 564L532 0L597 -14L766 662Z"/></g></svg> content in plant tissues. This study evaluates a rapid and efficient method for the estimation of <svg xmlns="http://www.w3.org/2000/svg" id="M5" style="vertical-align:-0.1876707pt" height="8.50298pt" version="1.1" viewBox="-0.0498162 -8.31531 10.1887 8.50298" width="10.1887pt"><g transform="matrix(.0125,0,0,-0.0125,0,0)"><path d="M766 662H679L558 178L360 662H239L74 0H161L301 564L532 0L597 -14L766 662Z"/></g></svg> content in different tissues that can help to serve as a tool for tree <svg xmlns="http://www.w3.org/2000/svg" id="M6" style="vertical-align:-0.1876707pt" height="8.50298pt" version="1.1" viewBox="-0.0498162 -8.31531 10.1887 8.50298" width="10.1887pt"><g transform="matrix(.0125,0,0,-0.0125,0,0)"><path d="M766 662H679L558 178L360 662H239L74 0H161L301 564L532 0L597 -14L766 662Z"/></g></svg> storage and recompilation study.

Yanjie Li

Honggang Sun

Federico Tomasetto

Jingmin Jiang

Qifu Luan

Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees

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<p>The open access journal <em>Plant Phenomics</em>, published in association with NAU, publishes novel research that advances plant phenotyping and connects phenomics with other research domains.</p>


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<p><em>Plant Phenomics'</em> editorial board is led by Seishi Ninomiya (University of Tokyo),&nbsp;Frédéric Baret (French National Institute of Agricultural Research and Environment), and&nbsp;Zong-Ming Cheng (Nanjing Agricultural University/University of Tennessee) and is comprised of leading experts in the field.</p>


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<p>Open for submission:</p>

<p><strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/time-series-phenotyping/">Advances in Proximal Sensing and Image Analysis for Time-Series Phenotyping</a></strong></p>

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<p>Published Special Issues:</p>

<p><strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/image-analysis">Image Analysis and Machine Learning for Cyber-Agricultural Systems</a></strong></p>

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<p><strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/statistics-bioinformatics/">Statistics, Bioinformatics, and Machine Learning Methods in Phenomics</a></strong></p>


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Plant Phenomics | Science Partner Journal

title

The open access journal Plant Phenomics, published in association with NAU, publishes novel research that advances plant phenotyping and connects phenomics with other research domains.

Table of Contents

Table of Contents: 2022

Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees