Hyperspectral retrieval for chlorophyll contents of Syringa oblata leaves based on RF-VR

doi:10.3969/j.issn.1004-1524.2021.11.19

Acta Agriculturae Zhejiangensis ›› 2021, Vol. 33 ›› Issue (11): 2164-2173.DOI: 10.3969/j.issn.1004-1524.2021.11.19

• Biosystems Engineering • Previous Articles Next Articles

Hyperspectral retrieval for chlorophyll contents of Syringa oblata leaves based on RF-VR

XIAO Zhiyun(), WANG Yining

College of Electricity Power, Inner Mongolia Key Laboratory of Mechatronic Control, Inner Mongolia University of Technology, Huhhot 010051, China

Received:2020-08-25 Online:2021-11-25 Published:2021-11-26

Abstract

Abstract:

Accurate estimation of the chlorophyll content of plant leaves by using hyperspectral technology is of great significance to monitor and manage plant growth trends and nutritional status. Taking Syringa oblata as the research object, aiming at the phenomenon that the large number of bands and strong correlation between bands lead to the increase of redundant information in the data, the spectral data were processed by convolution smoothing and second-order differentiation (SG-SD), the random leapfrog (RF) algorithm was used to screen the characteristic bands, and finally combined with partial least squares (PLSR) and voting regression (VR). The inversion model of chlorophyll content in plant leaves was established and compared with full band spectroscopy and five classical variable extraction methods. The results showed that compared with the original spectral data, SG-SD was an effective spectral pretreatment method to improve modeling accuracy; compared with full-band spectrum and 5 classical variable selection methods, the sensitive bands selected by the RF algorithm had the best modeling accuracy; compared with PLSR model, the prediction accuracy and stability of the VR model were better. In the present paper, after the SG-SD pretreatment of the original spectral data, a VR model was established for the sensitive bands selected by the RF algorithm, the variable number reduced from 204 to 35, the determination coefficients of modeling set and validation set were 0.944 2 and 0.951 4 respectively. Finally, using RF-VR model and pseudo color map technology, the inversion map of chlorophyll distribution of Syringa oblata leaves was obtained, which provided more intuitive information expression for nutrient distribution of Syringa oblata leaves. It was concluded that this method could provide technical support for the diagnosis and growth monitoring of the nutrient content of Syringa oblata leaves.

Key words: Syringa oblata, chlorophyll content, hyperspectral image, spectral data processing, random frog, vote regressor

CLC Number:

TP391.41

XIAO Zhiyun, WANG Yining. Hyperspectral retrieval for chlorophyll contents of Syringa oblata leaves based on RF-VR[J]. Acta Agriculturae Zhejiangensis, 2021, 33(11): 2164-2173.

Figures/Tables 11

Fig.1 Hyperspectral imaging monitoring system 1, Leaf sample; 2, 3, Light source; 4, Storage platform; 5, The Specim IQ hyperspectral camera; 6, Transmission data line; 7, Computer; 8, Tripod.

Fig.2 Voting regression algorithm flow chart

Fig.4 original spectral curve Rraw (A) and pretreated spectral curve RSG-SD (B) of syringa oblata leaves

Table 1 Statistics and division of samples SPAD value

样本集 Samples set	样本数 Samples number	最大值 Maximum	最小值 Minimum	平均值 Mean	标准差 Standard derivation
总样本Overall	200	44.3	18.3	31.55	7.085 2
建模集Modeling set	160	44.3	18.3	30.09	7.167 7
验证集Validation set	40	43.6	19.7	28.87	6.770 7

Fig.4 Distribution map of variables selected by CA(A), CARS(B), UVE(C) and MWPLS(D) method

Fig.5 Results of of RF, Probability of each wavelength selected (A) and wavelength selected results (B)

Table 2 Accuracies of PLSR modeling with different variable selection methods

光谱 Spectrum	筛选方法 Selection method	波段数量 Bands number	主成分数 Principal component	建模集 $R C 2$	验证集 $R V 2$
R_raw	FULL	204	10	0.846 6	0.896 9
R_SG-SD		204	7	0.865 2	0.913 8
R_raw	CA	31	8	0.857 9	0.893 6
R_SG-SD		31	18	0.885 7	0.911 0
R_raw	RF	49	10	0.899 7	0.921 1
R_SG-SD		35	10	0.944 2	0.951 4
R_raw	CARS	48	14	0.857 7	0.924 3
R_SG-SD		29	9	0.910 6	0.928 8
R_raw	UVE	25	15	0.854 2	0.872 7
R_SG-SD		40	7	0.906 4	0.927 8
R_raw	MWPLS	190	8	0.856 9	0.897 0
R_SG-SD		190	5	0.905 2	0.927 7

Table 2 Accuracies of PLSR modeling with different variable selection methods

光谱 Spectrum	筛选方法 Selection method	波段数量 Bands number	主成分数 Principal component	建模集 $R C 2$	验证集 $R V 2$
R_raw	FULL	204	10	0.846 6	0.896 9
R_SG-SD		204	7	0.865 2	0.913 8
R_raw	CA	31	8	0.857 9	0.893 6
R_SG-SD		31	18	0.885 7	0.911 0
R_raw	RF	49	10	0.899 7	0.921 1
R_SG-SD		35	10	0.944 2	0.951 4
R_raw	CARS	48	14	0.857 7	0.924 3
R_SG-SD		29	9	0.910 6	0.928 8
R_raw	UVE	25	15	0.854 2	0.872 7
R_SG-SD		40	7	0.906 4	0.927 8
R_raw	MWPLS	190	8	0.856 9	0.897 0
R_SG-SD		190	5	0.905 2	0.927 7

Fig.6 Scatter diagram of predicted and measured values for the RF-PLSR model

Table 3 Accuracies of VR modeling with different variable selection methods

预处理 Spectrum	筛选方法 Selection method	波段数量 Bands number	建模集 $R C 2$	验证集 $R V 2$
R_raw	FULL	204	0.919 3	0.952 5
R_SG-SD		204	0.949 7	0.953 4
R_raw	CA	31	0.853 1	0.899 8
R_SG-SD		31	0.949 3	0.942 0
R_raw	RF	49	0.919 3	0.965 8
R_SG-SD		35	0.952 9	0.964 3
R_raw	CARS	48	0.866 4	0.957 8
R_SG-SD		29	0.940 5	0.954 6
R_raw	UVE	25	0.881 3	0.947 7
R_SG-SD		40	0.944 8	0.960 3
R_raw	MWPLS	190	0.926 1	0.953 2
R_SG-SD		190	0.945 9	0.948 6

Table 3 Accuracies of VR modeling with different variable selection methods

预处理 Spectrum	筛选方法 Selection method	波段数量 Bands number	建模集 $R C 2$	验证集 $R V 2$
R_raw	FULL	204	0.919 3	0.952 5
R_SG-SD		204	0.949 7	0.953 4
R_raw	CA	31	0.853 1	0.899 8
R_SG-SD		31	0.949 3	0.942 0
R_raw	RF	49	0.919 3	0.965 8
R_SG-SD		35	0.952 9	0.964 3
R_raw	CARS	48	0.866 4	0.957 8
R_SG-SD		29	0.940 5	0.954 6
R_raw	UVE	25	0.881 3	0.947 7
R_SG-SD		40	0.944 8	0.960 3
R_raw	MWPLS	190	0.926 1	0.953 2
R_SG-SD		190	0.945 9	0.948 6

Fig.7 Scatter plot of predicted and measured values for the RF-VR model

Fig.8 Distribution of chlorophyll in Syringa oblata leaves

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Hyperspectral retrieval for chlorophyll contents of Syringa oblata leaves based on RF-VR

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