Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial least-square regression and support vector machine regression methods

Zhai, Yanfang; Cui, Lijuan; Zhou, Xin; Gao, Yun; Fei, Teng; Gao, Wenxiu

doi:10.1080/01431161.2012.746484

Cited by 148 publications

(75 citation statements)

References 47 publications

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“…The latter connection may be due to the inverse relationship between P content and cellulose or the role of P in protein synthesis (Sawan, Hafez, and Basyony 2001;Specht and Rundel 1990). Visible wavelengths, which are known to indicate K deficiency, had a significant effect on K predictions (Zhai et al 2013). Furthermore, wavelengths in the chlorophyll absorption features and red-edge are highly related to Mg, which is a chlorophyll component (Gökkaya et al 2014).…”

Section: Model Componentsmentioning

confidence: 99%

Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales

Stein

Thomas

Lorentz

et al. 2014

GIScience & Remote Sensing

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Given the economic importance of loblolly pine (Pinus taeda) in the southeastern US, there is a need to establish efficient methods of detecting potential nutrient deficiencies that may limit productivity. This study evaluated the use of remote sensing for macronutrient assessment in loblolly pine. Reflectance-based models were developed at two spatial scales: (1) a natural nutrient gradient across the species' range, and (2) localized fertilization and genotype treatments in North Carolina and Virginia. Fascicles were collected regionally from 237 samples of 3 flushes at 18 sites, and locally from 72 trees with 2 fertilization treatments and 6 genotypes. Sample spectral reflectance was calculated using a spectroradiometer, and nutrient concentrations were measured with dry combustion and wet chemical digestion. Results were analyzed statistically using nutrient correlations with reflectance and common vegetation indices, and partial least squares regression (PLSR). PLSR performed well at the regional scale, with R 2 values for nitrogen, phosphorus, potassium, calcium, and magnesium of 0. 81, 0.70, 0.68, 0.42, and 0.51, respectively. No model successfully predicted nutrients at local sites for any treatment or canopy stratum. This discrepancy implies that a large nutrient range and/or spatial scale may be necessary to model loblolly pine nutrients with spectral reflectance.

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Section: Model Componentsmentioning

confidence: 99%

Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales

Stein

Thomas

Lorentz

et al. 2014

GIScience & Remote Sensing

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“…Although some studies have made comparisons between different multivariate regression methods or spectral pre-processing methods in estimating biochemical components in plant leaves [14,27,31], this study is a further attempt to comprehensively compare the performances of 13 statistical modeling methods for grass (e.g., C. cinerascens) nutrient (N and P) estimation using canopy hyperspectral reflectance. Univariate linear regression with nine published VIs, three classical multivariate regression methods (SMLR, PLSR and SVR) and the SPA-MLR method were compared, and the results comprehensively showed that SPA-MLR was an optimal method from the perspectives of prediction accuracy, model simplicity and robustness.…”

Section: Comparison Of Model Performancementioning

confidence: 99%

“…The performance of a statistical model using hyperspectral reflectance is often evaluated by predictive accuracy, i.e., determination coefficient (R 2 ), root mean square error (RMSE) and ratio of prediction to deviation (RPD) [14,20,27,31,35,36]. However, little information of model simplicity, robustness and interpretation can be gained from the predictive accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Among these studies, univariate regression with vegetation indices (VIs) and multivariate regression methods are commonly used to extract useful information for nutrient characterization. These modeling methods provide convenient and interpretable means for researchers to understand the fundamental interaction of plant condition with radiant energy detected by multispectral or hyperspectral sensors [4][5][6][7][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Since the early attempt at estimating the N content in sweet pepper leaves from laboratory-based reflectance with univariate regression [26], many researchers have used linear or non-linear multivariate regressions for plant nutrient estimations, such as stepwise multiple linear regression (SMLR) [5,6,8,27], partial least squares regression (PLSR) [5,9,27] and support vector regression (SVR) [14,28]. SMLR is the linear combination of several important bands selected from the whole reflectance spectra; however, it has the potential problem of over-fitting [29].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Different Methods for Grass Nutrient Estimation from Canopy Hyperspectral Reflectance

Wang

Skidmore

et al. 2015

Remote Sensing

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Abstract:The characterization of plant nutrients is important to understand the process of plant growth in natural ecosystems. This study attempted to evaluate the performances of univariate linear regression with various vegetation indices (VIs) and multivariate regression methods in estimating grass nutrients (i.e., nitrogen (N) and phosphorus (P)) with canopy hyperspectral reflectance. Synthetically considering predictive accuracy, simplicity, robustness and interpretation, the successive projections algorithm coupled with multiple linear regression (SPA-MLR) method was considered optimal for grass nutrient estimation at the canopy level, when compared with the performances of 12 statistical modeling methods, i.e., univariate linear regression with nine published VIs and three classical multivariate regression methods (stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector regression (SVR)). The simple ratio index ( ⁄ , is derivative reflectance) model had comparable performance to SPA-MLR model for P estimation. SPA-MLR provided comparable prediction accuracies with only three first derivative spectral bands for N (715, 731 and 2283 nm) and P (714, 729 and OPEN ACCESSRemote Sens. 2015, 7 5902 1319 nm) estimations, compared with PLSR and SVR models, which used the full spectrum. Moreover, SPA-MLR provided robust prediction with the lowest bias values for N (−0.007%) and P (0.001%) estimations, and the fitting line between predicted and measured values was closer to the 1:1 line than the other models. Finally, most of the bands selected by SPA-MLR indirectly relate to foliar chlorophyll content, which suggests good physical interpretation.

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NanoMetrix: An app for chemometric analysis from near infrared spectra

Baumann

Librelotto

Pappis

et al. 2020

Journal of Chemometrics

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Near-infrared spectroscopy (NIR) associated with chemometric methods has been widely used in the determination of various compounds, as it is a fast, nondestructive, and analytical method with minimal sample preparation and universal application. In addition, the trend of miniaturization has allowed the development of new portable equipment, increasing the possibilities of point-of-use analysis at low cost. In this context, the objective of this study was to develop a portable analytical methodology using a low-cost DLP NIRscan Nano spectrophotometer (Texas Instruments ®) connected to a smartphone for data storage. For this, an app called NanoMetrix was developed, created natively for Android from Android Studio IDE, according to a software design standard known as Model-View-ViewModel (MVVM). To demonstrate the applicability of the method, 36 samples of plant tissue were analyzed to determine total nitrogen content. The calibration models were compared to those obtained with the SOLO+MIA software (Eigenvector Research, Inc.), 8.6.1. The results presented root mean square error of calibration (RMSEC) 1.90 and 1.82 g kg −1 , root mean square error of prediction (RMSEP) 2.00 and 1.97 g kg −1 , and R 2 pred 0.973 and 0.975, for the NanoMetrix application and the SOLO+MIA software, respectively. Therefore, the results indicate that the NanoMetrix integrated with the portable spectrophotometer, besides the acquisition of spectra, allows the processing of data in the field and reducing the time for the analysis and obtaining the results. Also, the chemometric associated with the NIR spectroscopy is a viable alternative to replace, or complement, the methods used for nitrogen determination.

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Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial least-square regression and support vector machine regression methods

Cited by 148 publications

References 47 publications

Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales

Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales

Evaluating Different Methods for Grass Nutrient Estimation from Canopy Hyperspectral Reflectance

NanoMetrix: An app for chemometric analysis from near infrared spectra

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