Rapid detection of contents of different types of nitrogen in substrates using near-infrared spectrum and machine learning

doi:10.3969/j.issn.1004-1524.20240379

Acta Agriculturae Zhejiangensis ›› 2025, Vol. 37 ›› Issue (5): 1159-1171.DOI: 10.3969/j.issn.1004-1524.20240379

• Biosystems Engineering • Previous Articles Next Articles

Rapid detection of contents of different types of nitrogen in substrates using near-infrared spectrum and machine learning

WU Haolin¹, WANG Shuzhen¹, ZHU Zhujun¹^,², HE Yong¹^,²^,^*()

1. College of Horticultural Sciences, Zhejiang A&F University, Hangzhou 311300, China
2. Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, Hangzhou, 311300, China

Received:2024-04-26 Online:2025-05-25 Published:2025-06-11

Abstract

Abstract:

To establish near-infrared(NIR) models for determining ammonium nitrogen and nitrate nitrogen contents in common substrates(peat, vermiculite, and perlite), exogenous nitrate and ammonium nitrogen treatments were applied to the three substrates, and their NIR spectra were collected. Chemical methods were employed to measure ammonium nitrogen and nitrate nitrogen contents. Mathematical models for predicting ammonium nitrogen and nitrate nitrogen contents in the three substrates were constructed using partial least squares regression(PLSR) and support vector machine(SVM) model. The results demonstrated that for ammonium nitrogen content, the optimal spectral preprocessing method was first derivative+smoothing. For nitrate nitrogen content, the best preprocessing methods for peat, vermiculite, and perlite were multiplicative scatter correction(MSC)+smoothing, first derivative, and MSC+first derivative+smoothing, respectively. Both PLSR and SVM methods effectively established prediction models for substrate ammonium nitrogen and nitrate nitrogen contents. The SVM models exhibited higher coefficient of determination in prediction set( $R p 2$ ) and relative prediction deviation(RPD) values in the prediction set, along with lower root mean square error in prediction set(RMSEP) compared to PLSR models. For ammonium nitrogen content, the SVM models achieved $R p 2$ values of 0.983, 0.936, and 0.925 for peat, vermiculite, and perlite, respectively, with RMSEP values of 0.073, 0.528, and 0.540, and RPD values of 7.74, 4.50, and 4.80. For nitrate nitrogen content, the SVM models showed $R p 2$ values of 0.912, 0.956, and 0.921 for the respective substrates, with RMSEP values of 0.716, 0.933, and 0.976, and RPD values of 3.23, 3.75, and 3.30. The SVM models developed for peat, vermiculite, and perlite demonstrate reliability and can be effectively applied for analyzing ammonium nitrogen and nitrate nitrogen contents in these substrates.

Key words: substrate, nitrate nitrogen, ammonium nitrogen, near-infrared spectrum, machine learning

CLC Number:

S129

WU Haolin, WANG Shuzhen, ZHU Zhujun, HE Yong. Rapid detection of contents of different types of nitrogen in substrates using near-infrared spectrum and machine learning[J]. Acta Agriculturae Zhejiangensis, 2025, 37(5): 1159-1171.

Figures/Tables 8

Table 1 Physical and chemical properties of different substrates

基质类型 Substrate type	容重 Bulk density/ (g·cm^-3)	pH值 pH value	电导率 Electric conductivity/ (ms·cm^-1)	总孔隙度 Total porosity/%	持水孔隙度 Water-holding porosity/%
泥炭Peat	0.22	6.09	1.92	78.5	73.5
蛭石Vermiculite	0.25	5.05	0.13	67.5	63.4
珍珠岩Perlite	0.07	5.16	0.12	63.1	34.1

Fig.1 The frequency distribution of nitrogen content in substrate samples A, Ammonium nitrogen in peat; B, Ammonium nitrogen in vermiculite; C, Ammonium nitrogen in perlite; D, Nitrate nitrogen in peat; E, Nitrate nitrogen in vermiculite; F, Nitrate nitrogen in perlite.

Table 2 Statistical parameters of the sample set after division

基质类型 Substrate type	氮类型 Nitrogen type	样本集 Sample set	样本量 Sample size	氮含量最大值 Maximum nitrogen content/ (mg·kg^-1)	氮含量最小值 Minimum nitrogen content/ (mg·kg^-1)	氮含量平均值 Average nitrogen content/ (mg·kg^-1)	标准差 Standard error/ (mg·kg^-1)
泥炭Peat	铵态氮Ammonium nitrogen	建模集Modeling set	60	1 889	31	429	575
	铵态氮Ammonium nitrogen	预测集Prediction set	18	1 738	18	374	545
	硝态氮Nitrate nitrogen	建模集Modeling set	71	7 891	954	4 532	2 328
	硝态氮Nitrate nitrogen	预测集Prediction set	23	8 134	1 073	4 569	2 289
蛭石Vermiculite	铵态氮Ammonium nitrogen	建模集Modeling set	96	11 017	14	3 064	2 426
	铵态氮Ammonium nitrogen	预测集Prediction set	30	9 005	23	3 159	2 208
	硝态氮Nitrate nitrogen	建模集Modeling set	90	12 897	288	5 142	3 493
	硝态氮Nitrate nitrogen	预测集Prediction set	29	12 797	322	5 285	3 595
珍珠岩Vermiculite	铵态氮Ammonium nitrogen	建模集Modeling set	89	14 138	23	3 249	2 632
	铵态氮Ammonium nitrogen	预测集Prediction set	28	11 351	15	3 356	2 837
	硝态氮Nitrate nitrogen	建模集Modeling set	94	11 404	13	4 559	3 193
	硝态氮Nitrate nitrogen	预测集Prediction set	31	10 508	15	4 706	3 133

Fig.2 Original spectra and preprocessed spectra of peat samples treated with ammonium nitrogen

Table 3 The modeling results of different pretreatment combinations

预处理方法 Pretreatment method	泥炭Peat				蛭石Vermiculite				珍珠岩Perlite
	铵态氮 Ammonium nitrogen		硝态氮 Nitrate nitrogen		铵态氮 Ammonium nitrogen		硝态氮 Nitrate nitrogen		铵态氮 Ammonium nitrogen		硝态氮 Nitrate nitrogen
	$R P 2$	RMSEP	$R P 2$	RMSEP	$R P 2$	RMSEP	$R P 2$	RMSEP	$R P 2$	RMSEP	$R P 2$	RMSEP
原始光谱Raw spectrum	0.928	0.149	0.770	1.117	0.912	0.678	0.790	1.650	0.883	0.907	0.868	1.140
多元散射校正MSC	0.914	0.171	0.888	0.798	0.921	0.633	0.680	2.060	0.832	1.030	0.939	0.786
标准正态变量变换SNV	0.914	0.171	0.871	0.864	0.924	0.625	0.682	2.050	0.853	0.952	0.943	0.756
平滑SG	0.927	0.149	0.795	1.100	0.912	0.675	0.786	1.670	0.883	0.905	0.868	1.140
一阶导数FD	0.932	0.142	0.686	1.300	0.923	0.652	0.946	0.938	0.893	0.917	0.929	0.845
一阶导数+平滑FD+SG	0.961	0.110	0.797	1.060	0.926	0.635	0.943	0.949	0.890	0.923	0.927	0.854
多元散射校正+平滑MSC+SG	0.916	0.166	0.899	0.752	0.921	0.631	0.679	2.060	0.832	1.040	0.938	0.791
SNV+平滑SNV+SG	0.917	0.166	0.867	0.870	0.923	0.630	0.680	2.050	0.853	0.952	0.940	0.776
多元散射校正+一阶导数MSC+FD	0.934	0.142	0.737	1.200	0.922	0.646	0.919	1.120	0.706	1.670	0.957	0.666
标准正态变量变换+一阶导数SNV+FD	0.934	0.142	0.741	1.119	0.923	0.645	0.922	1.110	0.841	1.060	0.956	0.684
多元散射校正+标准正态变量变换+ 平滑MSC+FD+SG	0.952	0.127	0.826	0.995	0.923	0.640	0.912	1.160	0.734	1.540	0.960	0.651
标准正态变量变换+一阶导数+平滑 SNV+FD+SG	0.952	0.127	0.830	0.986	0.924	0.639	0.915	1.150	0.867	0.964	0.956	0.683

Table 3 The modeling results of different pretreatment combinations

预处理方法 Pretreatment method	泥炭Peat				蛭石Vermiculite				珍珠岩Perlite
	铵态氮 Ammonium nitrogen		硝态氮 Nitrate nitrogen		铵态氮 Ammonium nitrogen		硝态氮 Nitrate nitrogen		铵态氮 Ammonium nitrogen		硝态氮 Nitrate nitrogen
	$R P 2$	RMSEP	$R P 2$	RMSEP	$R P 2$	RMSEP	$R P 2$	RMSEP	$R P 2$	RMSEP	$R P 2$	RMSEP
原始光谱Raw spectrum	0.928	0.149	0.770	1.117	0.912	0.678	0.790	1.650	0.883	0.907	0.868	1.140
多元散射校正MSC	0.914	0.171	0.888	0.798	0.921	0.633	0.680	2.060	0.832	1.030	0.939	0.786
标准正态变量变换SNV	0.914	0.171	0.871	0.864	0.924	0.625	0.682	2.050	0.853	0.952	0.943	0.756
平滑SG	0.927	0.149	0.795	1.100	0.912	0.675	0.786	1.670	0.883	0.905	0.868	1.140
一阶导数FD	0.932	0.142	0.686	1.300	0.923	0.652	0.946	0.938	0.893	0.917	0.929	0.845
一阶导数+平滑FD+SG	0.961	0.110	0.797	1.060	0.926	0.635	0.943	0.949	0.890	0.923	0.927	0.854
多元散射校正+平滑MSC+SG	0.916	0.166	0.899	0.752	0.921	0.631	0.679	2.060	0.832	1.040	0.938	0.791
SNV+平滑SNV+SG	0.917	0.166	0.867	0.870	0.923	0.630	0.680	2.050	0.853	0.952	0.940	0.776
多元散射校正+一阶导数MSC+FD	0.934	0.142	0.737	1.200	0.922	0.646	0.919	1.120	0.706	1.670	0.957	0.666
标准正态变量变换+一阶导数SNV+FD	0.934	0.142	0.741	1.119	0.923	0.645	0.922	1.110	0.841	1.060	0.956	0.684
多元散射校正+标准正态变量变换+ 平滑MSC+FD+SG	0.952	0.127	0.826	0.995	0.923	0.640	0.912	1.160	0.734	1.540	0.960	0.651
标准正态变量变换+一阶导数+平滑 SNV+FD+SG	0.952	0.127	0.830	0.986	0.924	0.639	0.915	1.150	0.867	0.964	0.956	0.683

Fig.3 Scatter plot of predicted values and observed values of substrate ammonium nitrogen content based on PLSR and SVM models A, PLSR model for peat; B, PLSR model for vermiculite; C, PLSR model for perlite; D, SVM model for peat; E, SVM model for vermiculite; F, SVM model for perlite.

Fig.4 Scatter plot of predicted values and observed values of substrate nitrate nitrogen content based on PLSR and SVM models A, PLSR model for peat; B, PLSR model for vermiculite; C, PLSR model for perlite; D, SVM model for peat; E, SVM model for vermiculite; F, SVM model for perlite.

Table 4 Accuracy of prediction model of ammonium nitrogen content and nitrate nitrogen content in different substrates

基质类型 Substrate type	氮处理 Nitrogen type	建模方式 Model	建模集Modeling set		预测集Prediction set
基质类型 Substrate type	氮处理 Nitrogen type	建模方式 Model	$R c 2$	RMSEC	$R p 2$	RMSEP	RPD
泥炭 Peat	铵态氮Ammonium nitrogen	PLSR	0.988	0.064	0.961	0.110	5.15
	铵态氮Ammonium nitrogen	SVM	0.990	0.058	0.983	0.073	7.74
	硝态氮Nitrate nitrogen	PLSR	0.985	0.286	0.797	1.060	2.19
	硝态氮Nitrate nitrogen	SVM	0.999	0.036	0.912	0.716	3.23
蛭石Vermiculite	铵态氮Ammonium nitrogen	PLSR	0.973	0.399	0.926	0.635	3.74
	铵态氮Ammonium nitrogen	SVM	0.997	0.115	0.936	0.528	4.50
	硝态氮Nitrate nitrogen	PLSR	0.973	0.572	0.943	0.949	3.69
	硝态氮Nitrate nitrogen	SVM	0.986	0.362	0.956	0.933	3.75
珍珠岩 Vermiculite	铵态氮Ammonium nitrogen	PLSR	0.959	0.531	0.890	0.923	2.81
	铵态氮Ammonium nitrogen	SVM	0.973	0.477	0.925	0.540	4.80
	硝态氮Nitrate nitrogen	PLSR	0.989	0.333	0.927	0.834	3.77
	硝态氮Nitrate nitrogen	SVM	0.993	0.143	0.921	0.976	3.30

Table 4 Accuracy of prediction model of ammonium nitrogen content and nitrate nitrogen content in different substrates

基质类型 Substrate type	氮处理 Nitrogen type	建模方式 Model	建模集Modeling set		预测集Prediction set
基质类型 Substrate type	氮处理 Nitrogen type	建模方式 Model	$R c 2$	RMSEC	$R p 2$	RMSEP	RPD
泥炭 Peat	铵态氮Ammonium nitrogen	PLSR	0.988	0.064	0.961	0.110	5.15
	铵态氮Ammonium nitrogen	SVM	0.990	0.058	0.983	0.073	7.74
	硝态氮Nitrate nitrogen	PLSR	0.985	0.286	0.797	1.060	2.19
	硝态氮Nitrate nitrogen	SVM	0.999	0.036	0.912	0.716	3.23
蛭石Vermiculite	铵态氮Ammonium nitrogen	PLSR	0.973	0.399	0.926	0.635	3.74
	铵态氮Ammonium nitrogen	SVM	0.997	0.115	0.936	0.528	4.50
	硝态氮Nitrate nitrogen	PLSR	0.973	0.572	0.943	0.949	3.69
	硝态氮Nitrate nitrogen	SVM	0.986	0.362	0.956	0.933	3.75
珍珠岩 Vermiculite	铵态氮Ammonium nitrogen	PLSR	0.959	0.531	0.890	0.923	2.81
	铵态氮Ammonium nitrogen	SVM	0.973	0.477	0.925	0.540	4.80
	硝态氮Nitrate nitrogen	PLSR	0.989	0.333	0.927	0.834	3.77
	硝态氮Nitrate nitrogen	SVM	0.993	0.143	0.921	0.976	3.30

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Rapid detection of contents of different types of nitrogen in substrates using near-infrared spectrum and machine learning

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