Prediction model of moisture content of masson pine seedling roots based on near infrared spectroscopy

doi:10.3969/j.issn.1000-2006.201902028

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2019, Vol. 43 ›› Issue (6): 91-96.doi: 10.3969/j.issn.1000-2006.201902028

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Prediction model of moisture content of masson pine seedling roots based on near infrared spectroscopy

NI Chao¹(), ZHANG Yun¹, GAO Handong²

1. College of Mechanical and Electronic Engineering,Nanjing Forestry University, Nanjing 210037, China
2. Southern Tree Seed Inspection Center, State Forestry and Grassland Administration,College of Forestry, Nanjing Forestry University, Nanjing 210037, China

Received:2019-02-26 Revised:2019-09-08 Online:2019-11-30 Published:2019-11-30

Abstract

Abstract:

【Objective】 Masson pine is the main afforestation species in southern China. The moisture content of roots is an important indicator for evaluating the vigour of seedlings. In this study, the method for estimating moisture content of masson pine seedling roots based on near infrared spectroscopy is advanced. 【Method】 The spectrum of masson pine seedling root is obtained. The prediction model which combines variable-wise weighted stacked autoencoder with support vector regression, was proposed to estimate the moisture content in the root of masson pine seedlings. The variable-wise weighted stacked autoencoder is used to extract the features which relating to the output and the support vector regression further accurately predict the moisture contents by using the identified features. 【Result】 Compared with the other commonly used models, our model had the best performance in the root moisture prediction, withR² value of 0.970 8 and RMSE of 0.635 8 in the training dataset and R² value of 0.941 3 and RMSE of 1.027 0 in the validation dataset. 【Conclusion】 The combination of the variable-wise weighted stacked autoencoder and support vector regression is feasible to realize the accurate prediction of moisture content in masson pine seedlings based on near infrared spectroscopy.

Key words: masson pine seedling, moisture content, near infrared spectroscopy, autoencoder, variable-wise weighted, support vector regression

CLC Number:

S725

NI Chao, ZHANG Yun, GAO Handong. Prediction model of moisture content of masson pine seedling roots based on near infrared spectroscopy[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2019, 43(6): 91-96.

Figures/Tables 5

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References 29

[1]	沈海龙, 丁贵杰, 高捍东, 等. 苗木培育学[M]. 北京: 中国林业出版社, 2009.
	SHEN H L, DING G J, GAO H D, et al. Seedling cultivation [M]. Beijing: China Forestry Publishing House, 2009.
[2]	喻方圆, 徐锡增. 苗木生理与质量研究进展[J]. 世界林业研究, 2000(4):17-24. DOI: 10.13348/j.cnki.sjlyyj.2000.04.006.
	YU F Y, XU X Z. Advances in research on seedling physiology and quality[J]. World Forestry Research, 2000(4):17-24.
[3]	张亚伟, 王书茂, 陈度, 等. 基于近红外的小麦植株含水率检测方法[J]. 农业机械学报, 2017, 48(S1):118-122,261.
	ZHANG Y W, WANG S M, CHEN D, et al. Method for detecting moisture content of wheat plants based on near infrared[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(S1):118-122,261.
[4]	吴伟斌, 刘文超, 李泽艺, 等. 基于高光谱的茶叶含水量检测模型建立与试验研究[J]. 河南农业大学学报, 2018, 52(5):818-824. DOI: 10.16445/j.cnki.1000-2340.2018.05.024.
	WU W B, LIU W C, LI Z Y, et al. Establishment and experimental study of tea water content detection model based on hyperspectral[J]. Journal of Henan Agricultural University, 2018, 52(5):818-824.
[5]	刘燕德, 邓清, 张光伟. 高光谱技术的赣南脐橙叶片含氮量分析[J]. 中国农机化学报, 2016, 37(9):99-103. DOI: 10.13733/j.jcam.issn.2095-5553.2016.09.022.
	LIU Y D, DENG Q, ZHANG G W. Analysis of nitrogen content in leaves of minnan navel orange by hyperspectral technique[J]. Journal of Chinese Agricultural Mechanization, 2016, 37(9):99-103.
[6]	孔军龙, 赵京音, 杨娟. 基于可见/近红外光谱技术定量分析青菜叶片含氮量[J]. 上海农业学报, 2013, 29(3):36-39. DOI: 10.3969/j.issn.1000-3924.2013.03.009.
	KONG J L, ZHAO J Y, YANG J. Quantitative analysis of nitrogen content in leaves of green cabbage based on visible/near infrared spectroscopy[J]. Acta Agriculturae Shanghai, 2013, 29(3):36-39.
[7]	王志超, 王建军, 戴晓宇, 等. 基于低成本RGB相机和近红外相机的作物叶片叶绿素含量估测方法比较研究[J]. 吉林农业, 2018(24):53. DOI: 10.14025/j.cnki.jlny.2018.24.031.
	WANG Z C, WANG J J, DAI X Y, et al. Comparative study on estimation methods of chlorophyll content in crop leaves based on low cost RGB camera and near infrared camera[J]. Agriculture of Jilin, 2018(24):53.
[8]	裴浩杰, 冯海宽, 李长春, 等. 基于多元线性回归和随机森林的苹果叶绿素含量高光谱估测方法比较[J]. 江苏农业科学, 2018, 46(17):224-230. DOI: 10.15889/j.issn.1002-1302.2018.17.060.
	PEI H J, FENG H K, LI C C, et al. Comparison of hyperspectral estimation methods of apple chlorophyll content based on multiple linear regression and random forest[J]. Jiangsu Agricultural Sciences, 2018, 46(17):224-230.
[9]	AIKEN L S, WEST S G, PITTS S C. Multiple linear regression[J]. Handbook of Psychology, 2003, 4(19):481-507.
[10]	李颖, 李耀翔, 徐浩凯, 等. 基于降噪处理的蒙古栎木材气干密度NIRS定标模型[J]. 南京林业大学学报(自然科学版), 2016, 40(6):148-156. DOI: 10.3969/j.issn.1000-2006.2016.06.023.
	LI Y, LI Y X, XU H K, et al. NIRS calibration model for air-dry density of Mongolian oak wood based on noise reduction [J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2016, 40(6):148-156.
[11]	刘镇波, 孙凤亮, WANG X M, 等. 基于近红外光谱法的人工林杨木木质素含量预测[J]. 南京林业大学学报(自然科学版), 2013, 37(6):121-126. DOI: 10.3969/j.issn.1000-2006.2013.06.024
	LIU Z B, SUN F L, WANG X M, et al. Prediction of lignin content in poplar plantation based on near infrared spectroscopy[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2013, 37(6):121-126.
[12]	DEVOS O, RUCKEBUSCH C, DURAND A, et al. Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation[J]. Chemometrics and Intelligent Laboratory Systems 2009, 96:27-33. doi: 10.1016/j.chemolab.2008.11.005
[13]	Üstün B, MELSSEN W, BUYDENS L. Visualisation and interpretation of support vector regression models[J]. Analytica Chimica Acta, 2007, 595:299-309. doi: 10.1016/j.aca.2007.03.023
[14]	BALABIN R M, LOMAKINA E I, SAFIEVA R Z. Neural network (ANN) approach to biodiesel analysis: analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy[J]. Fuel, 2011, 90:2007-2015. doi: 10.1016/j.fuel.2010.11.038
[15]	沈剑波, 雷相东, 李玉堂, 等. 基于BP神经网络的长白落叶松人工林林分平均高预测[J]. 南京林业大学学报(自然科学版), 2018, 42(2):147-154.
	SHEN J B, LEI X D, LI Y T, et al. Prediction of average height of stands ofLarix olgensis plantation based on BP neural network [J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2018, 42(2):147-154.
[16]	HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18:1527-1554. doi: 10.1162/neco.2006.18.7.1527
[17]	LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86:2278-2324. doi: 10.1109/5.726791
[18]	VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11:3371-3408.
[19]	GEHRING J, MIAO Y, METZE F, et al. Extracting deep bottleneck features using stacked auto-encoders[C]// Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference: 2013: 3377-3381.
[20]	TAO C, PAN H, LI Y, et al. Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12:2438-2442. doi: 10.1109/LGRS.2015.2482520
[21]	WANG X, LIU H. Soft sensor based on stacked auto-encoder deep neural network for air preheater rotor deformation prediction[J]. Advanced Engineering Informatics, 2018, 36:112-119. doi: 10.1016/j.aei.2018.03.003
[22]	YU X, TANG L, WU X, et al. Nondestructive freshness discriminating of shrimp using visible/near-infrared hyperspectral imaging technique and deep learning algorithm[J]. Food Analytical Methods, 2018, 11:768-780. doi: 10.1007/s12161-017-1050-8
[23]	ZHANG W, LIU Z, HU J, et al. Near infrared spectroscopy drug discrimination method based on stacked sparse auto-encoders extreme learning machine[J]. Artificial Intelligence and Robotics, 2018, 752:203-211.
[24]	LIU T, LI Z, YU C, et al. NIRS feature extraction based on deep auto-encoder neural network[J]. Infrared Physics & Technology, 2017, 87:124-128.
[25]	XU L, JIANG J H, WU H L, et al. Variable-weighted PLS[J]. Chemometrics and Intelligent Laboratory Systems, 2007, 85:140-143. doi: 10.1016/j.chemolab.2006.06.001
[26]	YUAN X, HUANG B, WANG Y, et al. Deep learning based feature representation and its application for soft sensor modeling with variable-wise weighted SAE[J]. IEEE Transactions on Industrial Informatics, 2018, 14(7):3235-3243. doi: 10.1109/TII.9424
[27]	BALABIN R M, LOMAKINA E I. Support vector machine regression (SVR/LS-SVM)—an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data[J]. Analyst, 2011, 136:1703-1712. doi: 10.1039/c0an00387e
[28]	AVCI E. Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm-support vector machines: HGASVM[J]. Expert Systems with Applications, 2009, 36:1391-1402. doi: 10.1016/j.eswa.2007.11.014
[29]	PAL M, MATHER P. Support vector machines for classification in remote sensing[J]. International Journal of Remote Sensing, 2005, 26:1007-1011. doi: 10.1080/01431160512331314083

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模型 model	校正集 calibration set		预测集 prediction set
模型 model	R²	均方根误差 RMSE	R²	均方根误差 RMSE
PLSR	0.838 5	1.436 0	0.839 9	1.461 0
SVR	0.926 7	0.962 9	0.867 2	1.399 0
SAE-ANN	0.950 8	0.844 4	0.883 8	1.398 0
SAE-SVR	0.971 5	0.656 4	0.894 3	1.376 0
VW-SAE-ANN	0.960 5	0.760 5	0.913 1	1.197 0
VW-SAE-SVR	0.970 8	0.635 8	0.941 3	1.027 0

Prediction model of moisture content of masson pine seedling roots based on near infrared spectroscopy

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