基于随机森林法的棉花叶片叶绿素含量估算

doi:10.3724/SP.J.1006.2019.84058

Abstract

Abstract:

The main objective of this study is the estimation of the leaf chlorophyll content efficiently and harmlessly. SPAD values and spectral data were collected from field observation. Original spectra processed to continuum-removal transformation, cube-root transformation and reciprocal transformation. Based on the correlation between SPAD values and canopy spectral reflectance, we selected characteristic bands by random forest approach to establish two kinds of estimating models, including back propagation artificial neural network (BP ANN) model and partial least squares regression (PLSR) model. The reflectivity in the range of 605-690 nm was negatively correlated with the SPAD value at P < 0.01, with the correlation coefficient of -0.619. After transformations, the spectral reflectance exhibited different correlations with SPAD value, continuum-removal spectra improved the correlation in the range of 550-750 nm, and had a better correlation with SPAD value than cube-root and reciprocal transformations. Random forest approach effectively evaluated the characteristic bands with large influence on SPAD value, which can help improve the estimation accuracy of the model. R ² of the PLSR and BP neural network model based on continuum-removal spectra was 0.92 and 0.83 respectively, show the two models with good stability in estimation of cotton SPAD values. The RMSE of the two models was 0.88, 1.26, and RE was 1.30% and 1.89% respectively, which indicates that estimation accuracy of PLSR model is higher that of BP neural network model. From the validation of the model, PLSR model has certain advantages and reference value in estimating chlorophyll content of cotton.

Key words: SPAD value, cotton, random forest method, hyper-spectral estimation model

ABLET Ershat,SAWUT Mamat,MAIMAITIAILI Baidengsha,Shen-Qun AN,Chun-Yue MA. Estimation of leaf chlorophyll content in cotton based on the random forest approach[J].Acta Agronomica Sinica, 2019, 45(1): 81-90.

Figures/Tables 8

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原始光谱RR				包络线去除光谱R_cr
变量名Variable name	特征波段Characteristic band (nm)	相关系数Correlation coefficient	VIM值 VIM value	变量名Variable name	特征波段Characteristic band (nm)	相关系数Correlation coefficient	VIM值 VIM value
X₁	614	-0.498	0.03420	X'₁	407	-0.670	0.00729
X₂	616	-0.520	0.00652	X'₂	479	-0.681	0.00326
X₃	653	-0.603	0.01130	X'₃	488	-0.694	0.00326
X₄	670	-0.633	0.01031	X'₄	508	-0.699	0.00461
X₅	689	-0.628	0.00461	X'₅	577	-0.732	0.00326
X₆	697	-0.544	0.01458	X'₆	585	-0.750	0.00326
X₇	700	-0.482	0.00652	X'₇	612	-0.779	0
X₈	759	0.513	0.00565	X'₈	621	-0.799	0.00461
X₉	786	0.516	0.00461	X'₉	651	-0.793	0.00326
X₁₀	826	0.519	0.00799	X'₁₀	695	-0.792	0.00799
X₁₁	905	0.529	0.00799	X'₁₁	712	-0.760	0.00652
X₁₂	941	0.509	0.05238	X'₁₂	723	-0.697	0.00790

模型 Model	建模Calibration (RR)		建模集Calibration (R_cr)		验证集Validation (RR)			验证集Validation (R_cr)
模型 Model	R²	RMSE	R²	RMSE	R²	RMSE	RE (%)	R²	RMSE	RE (%)
偏最小二乘模型PLSR BP神经网络模型BP ANN	0.68 0.69	1.62 1.59	0.63 0.90	1.73 0.91	0.64 0.78	2.06 1.60	3.01 2.27	0.92 0.83	0.88 1.26	1.30 1.89

Estimation of leaf chlorophyll content in cotton based on the random forest approach

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