Published September 24, 2014 Soil Fertility & Crop Nutrition
Determination of Nitrogen Concentration in Fresh Pear Leaves by Visible/Near-Infrared Refl ectance Spectroscopy Wang Jie, Zhao Hua-bing, Shen Chang-wei, Chen qia o-wei, Dong Cai-xia,* and Xu Yang-chun ABSTRACT
A rapid and reliable method is required to determine the N status of pear (Pyrus communis L.) leaves during the growing season for timely fertilization to improve the yields and fruit quality. In the present study, we evaluated visible and near-infrared reflectance (Vis/NIR) spectra of fresh pear leaves using partial least squares (PLS) regression to determine the N concentration of fresh pear leaves. In addition, we studied the performance of modified spectra generated using different preprocessing techniques. A total of 450 leaf samples were collected from 6-yr-old pear trees of two cultivars, and randomly separated into two subsets (calibration subset [294 samples] and validation subset [180 samples]) after excluding outliers by using principle component analysis. Results showed that the model built using full spectra performed better than that developed using characteristic wavelength segments. In addition, we found that original spectral proved to provide better accuracy than derivative spectra. Among the studied preprocessing techniques, moving average smoothing (MAS) technique improved accuracy the most. Overall results suggested that PLS regression with preprocessing of full spectra using MAS is optimal method for modeling N concentration of fresh pear leaves which yielded 0.961 and 0.953 coefficient of determination (R2) for calibration and cross-validation, respectively. The validation of this method resulted high R2 value (0.847) and low mean relative error (4.48%). In conclusion, this model could provide a rapid and more reliable method to determine the total N concentration in fresh pear leaves and could be useful for fertilization management in pear orchards.
Nitrogen is a crucial factor for determining pear tree
growth conditions, significantly influencing the vegetative growth, yield, and fruit quality. In addition to contributing directly to the composition of various important compounds, N influences the metabolic functions indirectly by affecting reactions such as photosynthesis (Curran, 1989; Shi et al., 2012). To maintain regular growth and development, N status of pear trees should be monitored real-time. Once N becomes deficient, a timely diagnosis and supplementation with N fertilizers would not only limit a decrease in fruit yield but also improve fruit quality (Klein and Weinbaum, 1984; Fernandez-Escobar et al., 2011). Traditional laboratory-based methods of N status evaluation are destructive, labor intensive, time-consuming (Wang et al., 2012), and cause environmental contamination, as they require highly corrosive acids, such as concentrated sulfuric acid, perchloric acid, and hydrofluoric acid. If the product of strong acid digestion is determined using a continuous flow analyzer, organic solvents will be needed (Wang et al., 2012; Cecilia and Luis, 2009). In many cases, standard chemical samples are required to calibrate the results.
College of Resources and Environmental Sciences, Nanjing Agricultural Univ., Nanjing 210095, China. Received 21 June 2013. *Corresponding author (
[email protected]). Published in Agron. J. 106:1867–1872 (2014) doi:10.2134/agronj13.0303 Copyright © 2014 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.
Recently, with the rapid development and improvement of spectroscopy techniques, it has become feasible to use Vis/ near-infrared reflectance spectroscopy (NIRS) to determine a variety of plant biochemical components in a rapid manner (Stenberg et al., 2005; Luo et al., 2011), which was as accurate as conventional laboratory analyses in measuring botanical composition (Moore et al., 1990; Shepherd et al., 2003). In research conducted on oilseed rape (Brassica napus L.) and tomato (Lycopersicon esculentum Mill.), it was found that Vis/NIR could be used to estimate N concentration of plants (Jin et al., 2009; Ulissi et al., 2011), and the 400 to 1000 nm waveband was used to establish an N prediction model for oilseed rape leaves (Liu et al., 2011). Ecarnot et al. (2013) also developed an accurate prediction model based on NIRS that was independent of the phenological stage for both fresh and dry leaves. Chen et al. (2002) conducted a study using NIRS to provide rapid and accurate measurements of sugarcane (Saccharum officinarum L.) leaf P content, to characterize the P content of commercial sugarcane and to screen for high-P cultivars in breeding programs. They achieved a satisfactory prediction accuracy with correlation coefficient of measured values and predicted values was 0.974, and the RMSEP was 0.155. In addition to the Vis/NIR spectral measurement at the individual leaf level described above, the prediction of N concentration at the canopy level is also well-established and widely used in Abbreviations: MAS, moving average smoothing; MSC, multiplicative scatter correction; NDSI, normalized difference spectral indices; NIR, near-infrared reflectance; NIRS, near-infrared reflectance spectroscopy; PLS, partial least squares; SGS, Savitzky–Golay smoothing; SNV, standard normal variable; Vis/NIR, visible near infrared.
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many field crops, especially in rice (Oryza sativa L.) and wheat (Triticum aestivum L.). Research performed by Yao et al. (2010) found that the normalized difference spectral indices (NDSI) of the characteristic bands (860 and 720 nm) were good indicators of wheat leaf N accumulation. However, in orchards, it is also difficult to collect the canopy spectra of tall fruit trees, since some special use of airborne hyperspectral imagery is required and the canopy spectra are more extensively used to estimate fruit yield than are the concentrations of nutrients in leaves (Ye et al., 2008; Sepulcre-Canto et al., 2007). Therefore, Vis/NIR spectral measurement at the individual leaf level is more commonly used in orchards. Partial least squares is a widely used modeling method for multivariate calibration and has been reported as one of the most effective predictive models with NIR spectra (Serbin et al., 2012). Compared with stepwise linear regression or principal component regression, PLS avoids the high colinearity of close wavelengths and exhibits improved predictive performance (Ye et al.,2008). Partial least squares was regarded as being superior to other univariate methods, such as vegetation indices (Atzberger et al., 2010; Darvishzadeh et al., 2008). In research conducted on rice, the PLS method showed that the precision of N prediction modeling was affected by the rice growth period. Among these models established at different growth periods, the model at the tillering stage was the best, with a coefficient of determination (R2) and root mean square error of prediction (RMSEP) of 0.909 and 0.450, respectively (Shao et al., 2012). Recently, Menesatti et al. (2010) have developed a prediction model for citrus using PLS, which was highly efficient for determining N and K content. Even though numerous studies have evaluated pear quality using Vis/NIRS (Sirisomboon et al., 2007; Xu et al., 2012), few have used this technique to predict total N concentration in pear leaves. A rapid method of predicting N concentration in pear leaves during the growing period would be highly beneficial, as it would enable farmers to adjust the timing and amount of N fertilization, either by foliar N or by soil application. Therefore, in the present study, we used PLS regression to create a model to determine the total N concentration of fresh pear leaves based on the Vis/NIR spectra, using a portable field spectrometer. We aimed to develop a method for rapidly determining N concentration of pear leaves and thereby provide a tool for pear farmers to guide for N fertilization. MATERIALS AND METHODS Experimental Materials Four-hundred-and-fifty fresh pear leaves, of the Cuiguan (Pyrus pyrifolia Nakai cv. Cuiguan) and Huangguan (Pyrus pyrifolia Nakai cv. Huangguan) varieties were collected from the Ziyunshan pear orchard in Yixing of Jiangsu Province, in May, July, and August 2011. Random multipoint sampling was used to gather the middle leaves of the year’s spring flush from the external side (east, south, west, and north) of the canopy. Because leaves from this part of the tree reflect the N concentration of the whole tree, only these young leaves were considered in this study. All leaf samples were collected from multiple plants, and were free of insect or fungal infestation. The samples were rapidly sealed in preservative bags, placed in an ice box, and taken to the laboratory for analysis. 1868
Spectra Collection Before taking the spectral measurements, dust was removed from the leaves using gauze that was wet with deionized water and then the leaves were dried with blotting paper. To acquire spectra, all of the reflectance measurements of pear leaves were made using a portable field spectrometer FieldSpec 3 (ASD Co., Ltd., Boulder, CO). The instrument covered wavelengths of 350 to 1000 nm (with a sampling interval of 1.4 nm and a spectral resolution of 3 nm) and 1000 to 2500 nm (with a sampling interval of 2 nm and a spectral resolution of 10 nm). The output spectral band number was 2151, and the interval of re-sampling was 1 nm. The spectra were collected using both a vegetation probe and a foliage holder. A built-in light source was included to provide a stable working environment during leaf spectral collection. To achieve the correct relative reflectance spectra, the spectra of the samples were collected under the conditions of a reference standard: a Teflon white reference panel purchased with the spectrometer was used to set up the maximum reflectance conditions. The spectra were taken in a laboratory setting, and the two central symmetrical points on the ventral side of leaves were designated as the measurement points of all the leaves. The scan number for each spectrum was set to 10 at the same position and the average value of 20 spectra was used as the final reflectance of the sample. Spectral Data Processing To decrease spectral noise and enhance the spectral information related to N concentration, original spectra were modified by (i) filtering out the wavelength segments that were highly correlated with N concentration; (ii) using ViewSpec to derive and transform the original data; (iii) using Unscrambler 9.7 (Camo Process AS, Oslo, Norway) to preprocess the data by MAS, Savitzky–Golay smoothing (SGS), multiplicative scatter correction (MSC), normalization, baseline, noise, and standard normal variable (SNV). Smoothing is an averaging algorithm to remove the random noise, with MAS and SGS being the most widely used forms of smoothing. The MAS operates by averaging a number of points in a recursive fashion. The Savitzky–Golay fits a polynomial to the data points and the value to be averaged is then predicted from this polynomial equation. Standard Normal Variate, MSC, and normalization are both row-oriented transformation techniques that remove scatter effects from spectra by centering and scaling each individual spectrum. The SNV approach effectively removes the multiplicative interferences of scatter and particle size. Multiplicative scatter correction is used to compensate for multiplicative and additive scatter effects in the data. Normalization is used to get all data in approximately the same scaling, or to get a more even distribution of the variances and the average values. Baseline includes two methods, named Baseline offset and Linear baseline correction, which are used to correct the baseline of samples, and are set in the dialog Transform Baseline. The two transformations can be executed separately or together, and in the combined case, the Linear baseline correction will be run first, followed by the Baseline offset. Noise is commonly used to see how sensitive the model is to noise in the data. This transformation has no specific row or column orientation. Agronomy Journal • Volume 106, Issue 5 • 2014
Nitrogen Concentration Determination Total N concentration of the leaf samples was measured analytically as a reference for the spectral prediction models. After spectral measurements were taken, the leaves were placed in a dry oven at 105°C for 30 min, and then dried to a constant weight at a temperature of 70°C for 48 h. The samples were ground to a fine powder in a mill. The total N concentration of leaves was analyzed according to the Kjeldahl method. Each sample was analyzed separately and a standard citrus leaf sample (GBW10020) was added in the process, to ensure that the N concentrations were correct. Modeling Method The 450 samples were split randomly at a ratio of 3:2 according to the Kennard–Stone algorithm into two groups: calibration and validation. With the application of processing outlined above, a model based on PLS was estabished. The model was assessed by means of statistical parameters such as the calibration coefficient of determination (R2c), cross validation (a leave-one-out CV) coefficient of determination (R2cv), root mean square error in calibration (RMSEC), and root mean square error in cross validation (RMSECV). Generally, the higher the coefficient of determination (R2) and the lower and closer the RMSEC and RMSECV, the better was the predictive ability of this model. The software Unscrambler 9.7 was used to build the model and identify the optimal number of factors to be used in the PLS model. Furthermore, validation (based on 180 samples) was conducted to examine and evaluate the model’s performance. The closer the N concentration between the measured and predicted values, the better the practical ability of the model. The PLS model was completed using Unscrambler 9.7, and was selected with a default maximum factor of 20 for model fitting. This software automatically calculates the optimal factor number of the PLS model, thereby reducing over fitting or fitting shortage errors. RESULTS Visible/Near Infrared Spectral Features of Pear Leaves The characteristics of reflectance spectra of fresh pear leaves are shown in Fig. 1. The distribution of spectra obtained from samples with different N concentrations followed roughly the same trend. However, at certain wavelengths, the spectral reflectance of samples differed; additionally, the reflectance and absorption maxima of each sample were not all at the same wavelength, which shows that there are indeed differences in the internal composition of pear leaves. In the blue (480 nm) and red (680 nm) regions of the visible spectrum, low reflectance (
