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作物学报

作物学报 ›› 2019, Vol. 45 ›› Issue (1): 81-90.doi: 10.3724/SP.J.1006.2019.84058

• 耕作栽培·生理生化 • 上一篇    下一篇

基于随机森林法的棉花叶片叶绿素含量估算

依尔夏提•阿不来提1,2,买买提•沙吾提1,2,3,*,白灯莎•买买提艾力4,*(),安申群1,2,马春玥1,2   

  1. 1 新疆大学资源与环境科学学院, 新疆乌鲁木齐 830046
    2 新疆绿洲生态教育部重点实验室, 新疆乌鲁木齐 830046
    3 新疆智慧城市与环境建模普通高校重点实验室, 新疆乌鲁木齐 830046
    4 新疆农业科学院核技术生物技术研究所, 新疆乌鲁木齐 830046
  • 收稿日期:2018-04-22 接受日期:2018-08-20 出版日期:2018-09-20 网络出版日期:2018-09-20
  • 通讯作者: 买买提?沙吾提,白灯莎?买买提艾力
  • 基金资助:
    本研究由国家自然科学基金项目(41361016);本研究由国家自然科学基金项目(41461051);大学生创新训练计划项目资助(201710755058)

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 Published:2018-09-20 Published online:2018-09-20
  • Contact: SAWUT Mamat,MAIMAITIAILI Baidengsha
  • 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)

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摘要：

为了高效和无损地估算棉花叶片的叶绿素含量, 本研究测定了棉花光谱反射率及叶绿素含量(soil and plant analyzer development, SPAD)值, 对光谱数据进行包络线去除处理、立方根转换和倒数转换, 以SPAD值与反射光谱之间的相关性为基础, 通过随机森林法筛选出对棉花叶片SPAD值影响较大的特征波段, 构建估算棉花叶片SPAD值的BP神经网络(back propagation artificial neural networks, BP ANN)、偏最小二乘回归(partial least squares regression, PLSR)两个模型。结果表明, 在605~690 nm范围内的反射率与SPAD值相关性达0.01显著水平, 均呈负相关, 相关系数最高值为-0.619。与原始光谱相比, 经过变换后的棉花反射率与SPAD值相关性结果相差较大, 其中去除包络线光谱在550~750 nm波段范围有效提高了相关性, 相关性效果优于倒数转换数据和立方根转换数据。随机森林法能够有效评出对SPAD值影响较大的特征波段, 进而提高模型估算精度。在两种模型中, 基于去除包络线光谱建立的PLSR和BP神经网络模型的决定系数R 2分别为0.92、0.83, 说明这两种模型的估算能力较好; 两种模型RMSE分别为0.88、1.26, RE分别为1.30%、1.89%, 表明PLSR模型的估算精度比BP神经网络模型高。从模型的验证效果来看, PLSR模型在估算棉花SPAD值方面有一定的优势和参考价值。

关键词: SPAD值, 棉花, 随机森林法, 高光谱估算模型

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

图1

研究区位置和采样点分布图"

图2

棉花叶片光谱"

图3

不同转换光谱曲线与叶SPAD值的相关性 R: 相关系数; RR: 原始光谱; Rcr: 包络线光谱; ?R: 立方根光谱; 1/R: 倒数光谱。"

图4

光谱自相关矩阵"

表1

特征波段的选取"

原始光谱RR 包络线去除光谱Rcr
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

图5

变量重要性评估"

表2

建模结果比较"

模型
Model		 建模Calibration (RR) 建模集Calibration (Rcr) 验证集Validation (RR) 验证集Validation (Rcr)
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

图6

PLSR和BP神经网络模型对实测值与预测值的拟合分析结果"

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