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

Abstract

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 N 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 N 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 N concentration using both full length and sMC selected NIR spectra. The generic model preformatted a reliable accuracy with R2C and R2CV 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 N content in plant tissues. This study evaluates a rapid and efficient method for the estimation of N content in different tissues that can help to serve as a tool for tree N storage and recompilation study.

Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis

“…Further, leaves are complex assemblies of organic compounds and may be expected to exhibit different spectral responses. NIRS can be successfully used for the characterization of chemical components, like nitrogen, in different plant tissues ( Li et al., 2022 ). In addition to leaf tissue, starch samples have been used to identify the waxy genotype based on NIR spectra in species such as wheat ( Lavine et al., 2014 ; Delwiche and Graybosch, 2016 ; Delwiche et al., 2018 ).…”

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IPPS 2022 - Plant Phenotyping for a Sustainable Future

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“…The prediction of GK usually performs better than GB in a single environmental condition ( Bandeira e Sousa et al., 2017 ). All these kernel functions use a large number of molecular markers to predict the target traits, which is similar to predictive models built using machine or deep learning methods based on near-infrared spectroscopy (NIRS) or hyperspectral data ( Yoosefzadeh-Najafabadi et al., 2021 ; Li et al., 2022 ). Therefore, it is plausible to use spectral data to estimate the kinship matrix, similar to the use of markers ( Van Tassel et al., 2022 ).…”