应用近红外光谱估测小麦叶片氮含量

doi:10.3724/SP.J.1258.2011.00844

摘要/Abstract

摘要：

研究利用近红外光谱(near-infrared, NIR)和化学计量学方法估测小麦(Triticum aestivum)新鲜叶片和粉末状干叶中全氮含量的可行性, 并建立小麦叶片氮含量估测模型, 以期为小麦氮素营养的精确管理提供理论依据。以3个小麦田间试验观测资料为基础, 分别运用偏最小二乘法(partial least squares, PLS)、反向传播神经网络(back-propagation neural network, BPNN)和小波神经网络(wavelet neural network, WNN), 建立小麦叶片氮含量的鲜叶和粉末状干叶近红外光谱估测模型, 用随机选择的样品集对所建模型进行测试和检验。结果显示, 利用PLS、BPNN和WNN 3种方法构建的近红外光谱模型均能准确地估测小麦叶片氮含量, 其中基于BPNN和WNN的模型优于基于PLS的模型, 且以基于WNN的模型表现最好。对模型进行检验的结果显示, 粉末状干叶模型的预测均方根误差(RMSEP)分别为0.147、0.101和0.094, 鲜叶模型的RMSEP分别为0.216、0.175和0.169, 模型的相关系数均在0.84以上。因此, 利用近红外光谱估算小麦叶片氮素营养精确可行, 对其他作物的氮素营养估测提供了借鉴和参考。

关键词: 叶片, 近红外光谱, 神经网络, 偏最小二乘法, 氮含量, 小麦

Abstract:

Aims Our objectives were to determine the feasibility of estimating nitrogen content in fresh and dry wheat leaves using near-infrared (NIR) spectroscopy and chemometrics and to establish the near-infrared model for estimating nitrogen content in wheat leaves in order to lay a foundation for wheat nitrogen management.

Methods We conducted three field experiments with different years, wheat varieties and nitrogen rates and determined time-course near-infrared absorbance spectroscopy and total nitrogen content from fresh and dry wheat leaves. The methods of partial least squares (PLS), back-propagation neural network (BPNN) and wavelet neural network (WNN) were used to establish the calibration models, and a dataset selected at random was used to evaluate the established models.

Important findings Near infrared calibration models based on PLS, BPNN and WNN could be used to estimate nitrogen content in wheat leaves with high precision and stable performance, especially WNN. The validation results showed that the root mean square errors of prediction (RMSEP) for the power model are 0.147, 0.101 and 0.094, respectively, while those for the fresh leaves model are 0.216, 0.175 and 0.169, respectively. The correlation coefficients (R²) for all models are >0.84. Therefore, near-infrared spectrometry can be an efficient method to estimate the nitrogen nutrition of crops.

Key words: leaf, near-infrared spectroscopy, neural network, partial least squares, total nitrogen content, wheat

姚霞, 汤守鹏, 曹卫星, 田永超, 朱艳. 应用近红外光谱估测小麦叶片氮含量. 植物生态学报, 2011, 35(8): 844-852. DOI: 10.3724/SP.J.1258.2011.00844

YAO Xia, TANG Shou-Peng, CAO Wei-Xing, TIAN Yong-Chao, ZHU Yan. Estimating the nitrogen content in wheat leaves by near-infrared reflectance spectroscopy. Chinese Journal of Plant Ecology, 2011, 35(8): 844-852. DOI: 10.3724/SP.J.1258.2011.00844

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链接本文: https://www.plant-ecology.com/CN/10.3724/SP.J.1258.2011.00844

https://www.plant-ecology.com/CN/Y2011/V35/I8/844

图/表 6

图1 小麦鲜叶和粉末状干叶近红外光谱原始光谱。

Fig. 1 Original near infrared spectra of fresh and dry wheat leaf.

表1 校正集和检验集小麦叶片全氮含量统计参数

Table 1 The statistical parameters of calibration set and testing set for total nitrogen content in leaf of wheat

统计参数 Statistical parameter	样品数 Number of samples	最小值 Minimum value (%)	最大值 Maximum value (%)	平均值 Mean value (%)	标准偏差 Standard deviation (%)	变异系数 Coefficient of variation (%)
校正集 Calibration set	136	0.57	4.42	2.50	1.01	40.40
检验集 Testing set	39	0.60	4.26	2.42	0.95	39.26

表2 不同光谱预处理方法下定标方程的表现

Table 2 Performance of the calibration equations under different methods of spectra preprocessing

回归类型 Regression type	样品 Sample	预处理方式 Preprocessing method	校正均方根误差 RMSEC(%)	R²
偏最小二乘法 PLS	鲜叶 Fresh leaf	MSC+SG	0.252	0.805
		MSC+SG+1D	0.243	0.824
		MSC+SG+2D	0.231	0.832
		MSC+N+1D	0.276	0.786
		MSC+N+2D	0.269	0.791
	粉末状干叶 Dry leaf powder	MSC+SG	0.247	0.829
		MSC+SG+1D	0.275	0.811
		MSC+SG+2D	0.264	0.821
		MSC+N+1D	0.153	0.904
		MSC+N+2D	0.161	0.900
反向传播神经网络 BPNN	鲜叶 Fresh leaf	MSC+SG	0.195	0.852
		MSC+SG+1D	0.186	0.873
		MSC+SG+2D	0.174	0.881
		MSC+N+1D	0.206	0.844
		MSC+N+2D	0.198	0.856
	粉末状干叶 Dry leaf powder	MSC+SG	0.111	0.956
		MSC+SG+1D	0.121	0.950
		MSC+SG+2D	0.118	0.951
		MSC+N+1D	0.108	0.962
		MSC+N+2D	0.129	0.946
小波神经网络 WNN	鲜叶 Fresh leaf	MSC+SG	0.199	0.872
		MSC+SG+1D	0.185	0.884
		MSC+SG+2D	0.173	0.899
		MSC+N+1D	0.201	0.847
		MSC+N+2D	0.196	0.875
	粉末状干叶 Dry leaf powder	MSC+SG	0.105	0.958
		MSC+SG+1D	0.113	0.954
		MSC+SG+2D	0.102	0.967
		MSC+N+1D	0.091	0.983
		MSC+N+2D	0.118	0.961

图2 内部交叉验证均方差(RMSECV)随偏最小二乘法主成分数(PC)的变化。

Fig. 2 Changes of root mean square error of cross validation (RMSECV) with the number of partial least squares principal components (PC). Dry, dry leaf powder; Fresh, fresh leaf.

图3 内部交叉验证均方差(RMSECV)随隐层节点数的变化。BPNN, 反向传播神经网络; Dry, 粉未状干叶; Fresh, 鲜叶; WNN, 小波神经网络。

Fig. 3 Changes of root mean square error of cross validation (RMSECV) with the number of neurons in hidden layer. BPNN, back propagation neural network; Dry, dry leaf powder; Fresh, fresh leaf; WNN, wavelet neural network.

图4 小麦叶片全氮含量预测值与观测值的相关性。BPNN, 反向传播神经网络; Dry, 粉末干叶状; Fresh, 鲜叶; PLS, 偏最小二乘法; RMSEP, 预测均方根误差; WNN, 小波神经网络。

Fig. 4 Correlation between predicted and observed values of total nitrogen content in wheat leaf. BPNN, back-propagation neural network; Dry, dry leaf powder; Fresh, fresh leaf; PLS, partial least squares; RMSEP, root mean square errors of prediction; WNN, wavelet neural network.

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