应用近红外光谱预测水稻叶片氮含量

doi:10.3773/j.issn.1005-264x.2010.06.010

摘要/Abstract

摘要：

以水稻(Oryza sativa)新鲜叶片和干叶粉末两种状态的样品为研究对象, 基于近红外光谱(NIRS)技术, 应用偏最小二乘法(PLS)、主成分回归(PCR)和逐步多元回归(SMLR), 建立并评价了水稻叶片氮含量(NC)近红外光谱模型。结果表明, 基于PLS建立的模型表现最好, 鲜叶氮含量近红外光谱校正模型校正决定系数R_C²为0.940, 校正标准误差RMSEC为0.226; 干叶粉末氮含量的近红外光谱校正模型R_C²为0.977, RMSEC为0.136。模型的内部交叉验证分析表明, 预测鲜叶氮含量内部验证决定系数R_CV²为0.866, 内部验证标准误差RMSECV为0.243; 预测干叶粉末氮含量R_CV²为0.900, RMSECV为0.202。模型的外部验证分析表明, 预测水稻鲜叶氮含量的外部验证决定系数R_V²大于0.800, 外部验证标准误差RMSEP小于0.500, 预测干叶粉末氮含量的R_V²为0.944, RMSEP为0.142。说明, 近红外光谱分析技术与化学分析方法一致性较好, 且基于干叶粉末建立的近红外光谱预测模型的准确性和精确度较新鲜叶片高。

关键词: 新鲜叶片, 干叶粉末, 近红外光谱, 氮含量, 水稻

Abstract:

Aim Our primary objective was to establish an effective method of near infrared reflectance spectroscopy (NIRS) for estimating leaf nitrogen content in rice, which would help with nitrogen diagnosis and dressing fertilization in rice production.

Methods Using the techniques of partial least square (PLS), principal component regression (PCR) and stepwise multiple linear regression (SMLR), we established four NIRS-based models for estimating nitrogen content (NC) in fresh leaf and leaf powder of rice cultivars under varied nitrogen application rates.

Important findings The coefficient of determination (R_C²) and root mean square error for calibration (RMSEC) of NC models with fresh leaf were 0.940 and 0.226, respectively, whereas the R_C² and RMSEC of NC models with leaf powder were 0.977 and 0.136, respectively. We tested the accuracy of models with independent experiment datasets by the determination coefficient (R_CV²) and root mean square error of cross-validation (RMSECV), and the determination coefficient (R_V²) and root mean square error of external validation (RMSEP). With fresh leaf, the R_CV² and RMSECV of NC models were 0.866 and 0.243, respectively, while the R_V² was >0.800 and RMSEP was <0.500. With leaf powder, the R_CV² and RMSECV of NC models were 0.900 and 0.202, respectively, whereas the R_V² and RMSEP were 0.944 and 0.142, respectively. Overall, the performance of the models with leaf powder is better than that with fresh leaf in rice.

Key words: fresh leaf, leaf powder, near infrared reflectance spectroscopy, nitrogen content, rice

张玉森, 姚霞, 田永超, 曹卫星, 朱艳. 应用近红外光谱预测水稻叶片氮含量. 植物生态学报, 2010, 34(6): 704-712. DOI: 10.3773/j.issn.1005-264x.2010.06.010

ZHANG Yu-Sen, YAO Xia, TIAN Yong-Chao, CAO Wei-Xing, ZHU Yan. Estimating leaf nitrogen content with near infrared reflectance spectroscopy in rice. Chinese Journal of Plant Ecology, 2010, 34(6): 704-712. DOI: 10.3773/j.issn.1005-264x.2010.06.010

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链接本文: https://www.plant-ecology.com/CN/10.3773/j.issn.1005-264x.2010.06.010

https://www.plant-ecology.com/CN/Y2010/V34/I6/704

图/表 9

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叶片状态 Form of leaf	校正集 Calibration set				检验集 Validation set
叶片状态 Form of leaf	样品数 No. of sample	变幅 Range (%)	平均值 Mean (%)	标准差 SD	样品数 No. of sample	变幅 Range (%)	平均值 Mean (%)	标准差SD
鲜叶 Fresh leaf	234	0.94-3.93	2.55	0.67	44	1.07-3.54	2.24	0.71
干叶粉末 Leaf powder	230	1.01-4.06	2.67	0.64	62	1.27-3.63	2.53	0.57

叶片状态 Form of leaf	校正集 Calibration set				检验集 Validation set
叶片状态 Form of leaf	样品数 No. of sample	变幅 Range (%)	平均值 Mean (%)	标准差 SD	样品数 No. of sample	变幅 Range (%)	平均值 Mean (%)	标准差SD
鲜叶 Fresh leaf	234	0.94-3.93	2.55	0.67	44	1.07-3.54	2.24	0.71
干叶粉末 Leaf powder	230	1.01-4.06	2.67	0.64	62	1.27-3.63	2.53	0.57

叶片状态 Form of leaf	化学计量学方法 Chemical metrology method	氮含量 NC
叶片状态 Form of leaf	化学计量学方法 Chemical metrology method	R_C²	RMSEC	RMSEP
鲜叶 Fresh leaf	偏最小二乘法 PLS	0.945	0.228	0.390
	主成分回归 PCR	0.472	0.568	0.979
	逐步多元回归 SMLR	0.426	0.602	0.907
叶片粉末 Leaf powder	偏最小二乘法 PLS	0.962	0.176	0.133
	主成分回归 PCR	0.821	0.365	0.415
	逐步多元回归 SMLR	0.741	0.429	0.410

叶片状态 Form of leaf	化学计量学方法 Chemical metrology method	氮含量 NC
叶片状态 Form of leaf	化学计量学方法 Chemical metrology method	R_C²	RMSEC	RMSEP
鲜叶 Fresh leaf	偏最小二乘法 PLS	0.945	0.228	0.390
	主成分回归 PCR	0.472	0.568	0.979
	逐步多元回归 SMLR	0.426	0.602	0.907
叶片粉末 Leaf powder	偏最小二乘法 PLS	0.962	0.176	0.133
	主成分回归 PCR	0.821	0.365	0.415
	逐步多元回归 SMLR	0.741	0.429	0.410

叶片状态 Form of leaf	光谱预处理方法 Spectral-pretreatment method	氮含量NC
叶片状态 Form of leaf	光谱预处理方法 Spectral-pretreatment method	R_C²	RMSEC	RMSEP
鲜叶 Fresh leaf	原始光谱 Original spectrum	0.945	0.228	0.390
	基线校正 Baseline correction	0.945	0.218	0.290
	一阶导数 First derivative	0.976	0.144	0.553
	二阶导数 Second derivative	0.922	0.257	0.840
	多元散射校正 (multiplicative signal correction, MSC)	0.927	0.248	0.297
	Norris平滑 (norris derivative filter, NDF)	0.944	0.219	0.421
	First derivative + MSC	0.972	0.155	0.471
	First derivative + MSC + NDF	0.931	0.284	0.411
叶片粉末 Leaf powder	原始光谱 Original spectrum	0.962	0.176	0.133
	基线校正 Baseline correction	0.961	0.176	0.133
	一阶导数 First derivative	0.975	0.143	0.208
	二阶导数 Second derivative	0.954	0.151	0.565
	多元散射校正 MSC	0.951	0.197	0.120
	Norris平滑 NDF	0.961	0.177	0.144
	First derivative + MSC	0.969	0.159	0.210
	First derivative + MSC + NDF	0.967	0.166	0.209