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Acta Agronomica Sinica ›› 2019, Vol. 45 ›› Issue (1): 81-90.doi: 10.3724/SP.J.1006.2019.84058


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

ABLET Ershat1,2,SAWUT Mamat1,2,3,*,MAIMAITIAILI Baidengsha4,*(),Shen-Qun AN1,2,Chun-Yue MA1,2   

  1. 1 College of Resources and Environmental Science, Xinjiang University, Urumqi 830064, Xinjiang, China
    2 Key Laboratory of Oasis Ecology of Ministry of Education, Urumqi 830064, Xinjiang, China
    3 Key Laboratory for Wisdom City and Environmental Modeling, Xinjiang University, Urumqi 830064, Xinjiang, China
    4 Institute of Nuclear and Biotechnologies, Xinjiang Academy of Agricultural Sciences, Urumqi 830064, Xinjiang, China
  • Received:2018-04-22 Accepted:2018-08-20 Online:2018-09-20 Published:2018-09-20
  • Contact: SAWUT Mamat,MAIMAITIAILI Baidengsha E-mail:korxat@xju.edu.cn
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(41361016);This study was supported by the National Natural Science Foundation of China(41461051);the Student Innovation Training Program(201710755058)


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

Fig. 1

Location of study area and distribution of sampling plots"

Fig. 2

Spectra of cotton leaves"

Fig. 3

Correlation of different conversion spectral curves with SPAD value R: correlation coefficient; RR: raw reflectance; Rcr: continuum-removal reflectance; ?R: cube-root reflectance; 1/R: reciprocal reflectance."

Fig. 4

Inter-correlation matrix of spctra"

Table 1

Characteristic band selection"

原始光谱RR 包络线去除光谱Rcr
band (nm)
VIM value
band (nm)
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

Fig. 5

Variable importance measure"

Table 2

Comparison of modeling results"

建模Calibration (RR) 建模集Calibration (Rcr) 验证集Validation (RR) 验证集Validation (Rcr)
BP神经网络模型BP ANN

Fig. 6

Fitting analysis results between measured values and predicted values by PLSR and BP neural network models"

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