Nitrogen content diagnosis of apple trees canopies using hyperspectral reflectance combined with PLS variable extraction and extreme learning machine

Shaomin Chen, Lihui Ma, Tiantian Hu, Lihua Luo, Qiong He, Shaowu Zhang

Abstract


Nitrogen (N) is an important mineral element in apple production. Rapid estimation of apple tree N status is helpful for achieving precise N management. The objective of this work was to explore partial least squares (PLS) regression in dimensional reduction of spectral data and build the diagnostic model. The spectral reflectance data were collected from Fuji apple trees with 4 levels of N fertilizer treatment in the Loess Plateau in 2018 and 2019 using an ASD portable spectroradiometer, and leaf total N content was obtained at the same time. The raw spectra were pretreated using Savitzky-Golay (SG) smoothing and a combination of SG and first-order derivative (SG_FD) or second-order derivative (SG_SD). The samples were divided into a calibration dataset and a prediction dataset using SPXY. Based on 4 factors of PLS regression, including latent variables (LVs), X-loading, variable importance in projection (VIP) and regression coefficients (RC), the 6 methods (LVs, X-loading, VIP_01, VIP_02, RC_01 and RC_02) were derived and used for variable extraction, based on which PLS model and ELM model were established. The results indicated that the spectral data processed by SG_FD had the highest signal-to-noise ratio and was selected for subsequent analysis. The amounts of variables extracted by LVs, X-loading, VIP_01, VIP_02, RC_01 and RC_02 were 6, 11, 18, 305, 26 and 88, respectively. The method of extracting variables with an RC threshold based on the minimum RMSEP (RC_02) could effectively avoid the omission of effective information. The RC_02 method was recommended for related research which required accurate wavelength information as a variable. The variable extraction method based on LVs generated an ELM model with a simple structure. The prediction results showed that the ELM model outperformed the PLS model. The PLS(LVs)_ELM model was the best; R2P, RMSEP and RPD were 0.837, 2.393 and 2.220, respectively.
Keywords: partial least square, variable extraction method, extreme learning machine, hyperspectral reflectance, apple tree, canopy nitrogen content
DOI: 10.25165/j.ijabe.20211403.6157

Citation: Chen S M, Ma L H, Hu T T, Luo L H, He Q, Zhang S W. Nitrogen content diagnosis of apple trees canopies using hyperspectral reflectance combined with PLS variable extraction and extreme learning machine. Int J Agric & Biol Eng, 2021; 14(3): 181–188.

Keywords


partial least square, variable extraction method, extreme learning machine, hyperspectral reflectance, apple tree, canopy nitrogen content

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References


Statistics Bureau of China. China statistical yearbook 2018. Beijing, China Statistics Press, 2018. (in Chinese)

Wang G Y, Zhang X Z, Wang Y, Xu X F, Han Z H. Key minerals influencing apple quality in Chinese orchard identified by nutritional diagnosis of leaf and soil analysis. Journal of Integrative Agriculture, 2015; 14(5): 864–874.

Zhu X C, Gao L L, Fang X Y, Zhao G X, Wang L. Estimating canopy nitrogen contents of an apple tree using hyperspectral remote sensing. Remote Sensing Science, 2016; 4(2): 42–50.

Gao L L, Zhu X C, Li C, Cheng L Z. Evaluation of the nitrogen content during the new-shoot-growing stage in apple leaves using two-dimensional correlation spectroscopy. PLoS ONE, 2017; 12(10): e0186751. doi: 10.1371/journal.pone.0186751.

Fernandez S, Vidal D, Simon E, Sugranes L S. Radiometric characteristics of Triticum aestivum cv, Astral under water and nitrogen stress. International Journal of Remote Sensing, 1994, 15(9): 1867–1884.

Zhu Y, Li Y X, Zhou D Q, Tian Y C, Yao X, Cao W X. Quantitative relationship between leaf nitrogen concentration and canopy reflectance spectra in rice and wheat. Acta ecologica sinica, 2006; 26(10): 3463–3469.

Xue L H, Cao W X, Luo W H, Zhang X. Correlation between leaf nitrogen status and canopy spectral characteristics in wheat. Chinese Journal of Plant Ecology, 2004; 28(2): 172–177. (in Chinese)

Yu L, Hong Y S, Zhou Y, Zhu Q, Xu L, Li J Y, et al. Wavelength variable selection methods for estimation of soil organic matter content using hyperspectral technique. Transactions of the CSAE, 2016; 32(13): 95–102. (in Chinese)

Zou X H, Hao Z Q, Yi R X, Guo L B, Shen M, Li X Y, et al. Quantitative analysis of soil by laser-induced breakdown spectroscopy using genetic algorithm-partial least squares. Chinese Journal of Analytical Chemistry, 2015; 43(2): 181–186. (in Chinese)

Chen L D, Zhao Y R. Measurement of water content in biodiesel using visible and near infrared spectroscopy combined with Random-Frog algorithm. Transactions of the CSAE, 2014; 30(8): 168–173. (in Chinese)

Zou X B, Zhao J W, Povey M J W, Holmes M, Mao H P. Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta, 2010; 667(1-2): 14–32.

Zhu Y X, Yu L, Hong Y S, Zhang T, Zhu Q, Li S D, et al. Hyperspectral features and wavelength variables selection methods of soil organic matter. Scientia Agricultura Sinica, 2017; 50(22): 4325–4337. (in Chinese)

Liu F, He Y, Wang L. Comparison of calibrations for the determination of soluble solids content and pH of rice vinegars using visible and short-wave near infrared spectroscopy. Analytica Chimica Acta, 2008; 610(2): 196–204.

Zhang R R, Wen Y, Li L L, Chen L P, Xu G, Huang Y B, et al. Method for UAV spraying pattern measurement with PLS model based spectrum analysis. Int J Agric & Biol Eng, 2020; 13(3): 22–28.

Gao J F, Zhang H L, Kong W W, He Y. Nondestructive discrimination of waxed apples based on hyperspectral imaging technology. Spectroscopy and spectral analysis, 2013; 33(7): 1922–1926. (in Chinese)

Zhang H, Wang S, Li D X, Zhang Y Y, Hu J D, Wang L. Edible gelatin diagnosis using laser-induced breakdown spectroscopy and partial least square assisted support vector machine. Sensors, 2019; 19: 4225. doi: 10.3390/s19194225.

Ye X J, Abe S, Zhang S H. Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging. Precision Agric, 2020; 21: 198–225.

Cheng P Y, Fan W L, Xu Y. Quality grade discrimination of Chinese

strong aroma type liquors using mass spectrometry and multivariate analysis. Food Research International, 2013; 54(2): 1753–1760.

Zhang H L. Soil nutrition content and type measurement based on NIR spectrum and hyper spectra image technology and design portable instrument. Doctoral dissertation. Hangzhou: Zhejiang University, 2015; 145p. (in Chinese)

Wang Y, Gao Y, Yu X, Wang Y Y, Deng S, Gao J M. Rapid determination of Lycium barbarum polysaccharide with effective wavelength selection using near-infrared diffuse reflectance spectroscopy. Food Analytical Methods, 2016; 9: 131–138.

Zhang C, Liu J G, Shang J L, Cai H J. Capability of crop water content for revealing variability of winter wheat grain yield and soil moisture under limited irrigation. Science of the Total Environment, 2018; 631–632: 677–687.

Zhu W J, Li J Y, Li L, Wang A C, Wei X H, Mao H P. Nondestructive diagnostics of soluble sugar, total nitrogen and their ratio of tomato leaves in greenhouse by polarized spectra–hyperspectral data fusion. Int J Agric & Biol Eng, 2020; 13(2): 189–197.

Li X X, Zhou J, Tang H, Sun L Q, Cao X M, Zhang X S. Rapid determination of total nitrogen in aquaculture water based on ultraviolet spectroscopy. Spectroscopy and spectral analysis, 2020; 40(1): 195–201. (in Chinese)

Ollinger S V. Sources of variability in canopy reflectance and the convergent properties of plants. New Phytologist, 2011; 189: 375–394.

Shi Z, Liang Z Z, Yang Y Y, Guo Y. Status and prospect of agricultural remote sensing. Transactions of CSAM, 2015; 46(2): 247–260. (in Chinese)

Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006; 70: 489–501.

Ouyang Q, Chen Q S, Zhao J W, Lin H. Determination of amino acid nitrogen in soy sauce using near infrared spectroscopy combined with characteristic variables selection and extreme learning machine. Food Bioprocess Technol, 2013; 6: 2486–2493.

Huang G B, Zhou H M, Ding X J, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems Man & Cybernetics Part B, 2012; 42(2): 513–529.

Czarnecki W M. Weighted tanimoto extreme learning machine with case study in drug discovery. IEEE Computational Intelligence Magazine, 2015; 10(3): 19–29.

Kamruzzaman M, Elmasry G, Sun D W, Allen P. Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Analytica Chimica Acta, 2012; 714: 57–67.

Cao D S, Liang Y Z, Xu Q S, Li H D, Chen X. A new strategy of outlier detection for QSAR/QSPR. Journal of Computational Chemistry, 2010; 31(3): 592–602.

Galvão R K H, Araujo M C U, José G E, Pontes M J C, Silva E C, Saldanha T C B. A method for calibration and validation subset partitioning. Talanta, 2005; 67(4): 736–740.

Shan P, Zhao Y H, Wang Q Y, Sha X P, Lyu X Y, Peng S L, et al. Stacked ensemble extreme learning machine coupled with Partial Least Squares-based weighting strategy for nonlinear multivariate calibration. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019; 215: 97–111.

Ramadan Z, Hopke P K, Johnson M J, Scow K M. Application of PLS and back-propagation neural networks for the estimation of soil properties. Chemometrics & Intelligent Laboratory Systems, 2005; 75(1): 23–30.

Chong I G, Jun C H. Performance of some variable selection methods when multicollinearity is present. Chemometrics & Intelligent Laboratory Systems, 2005; 78(1-2): 103–112.

Wen P F. Monitoring the vertical distribution of nitrogen status at leaf and canopy scales with remote sensing data in maize. Doctoral dissertation. Yangling: Northwest A& F University, 2019; 124p. (in Chinese)

Guo P T, Su Y, Cha Z Z, Lin Q H, Luo W, Lin Z M. Prediction of leaf phosphorus contents for rubber seedlings based on hyperspectral sensitive bands and back propagation artificial neural network. Transactions of the CSAE, 2016; 32(Supp. 1): 177–183. (in Chinese)




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