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作物学报 ›› 2022, Vol. 48 ›› Issue (9): 2300-2314.doi: 10.3724/SP.J.1006.2022.11089

• 耕作栽培·生理生化 • 上一篇    下一篇

基于特征波段选择和机器学习的小麦白粉病高光谱遥感监测

冯子恒1,2(), 李晓3,*(), 段剑钊2, 高飞4, 贺利2, 杨天聪2, 戎亚思2, 宋莉2, 尹飞1, 冯伟2   

  1. 1.河南农业大学信息与管理科学学院, 河南郑州 450046
    2.河南农业大学农学院 / 省部共建小麦玉米作物学国家重点实验室, 河南郑州 450046
    3.河南农业大学理学院, 河南郑州 450046
    4.河南农业大学植物保护学院, 河南郑州 450046
  • 收稿日期:2021-10-12 接受日期:2022-02-25 出版日期:2022-09-12 网络出版日期:2022-03-15
  • 通讯作者: 李晓
  • 作者简介:E-mail: fzhfzh88@163.com
  • 基金资助:
    国家自然科学基金项目(31971791);国家重点研发计划项目(2017YFD0301105)

Hyperspectral remote sensing monitoring of wheat powdery mildew based on feature band selection and machine learning

FENG Zi-Heng1,2(), LI Xiao3,*(), DUAN Jian-Zhao2, GAO Fei4, HE Li2, YANG Tian-Chong2, RONG Ya-Si2, SONG Li2, YIN Fei1, FENG Wei2   

  1. 1. Information and Management Science College of Henan Agricultural University, Zhengzhou 450046, Henan, China
    2. Agronomy College of Henan Agriculture University / State Key Laboratory of Wheat and Maize Crop Science, Zhengzhou 450046, Henan, China
    3. College of Science, Henan Agricultural University, Zhengzhou 450046, Henan, China
    4. Plant Protection College of Henan Agricultural University, Zhengzhou 450046, Henan, China
  • Received:2021-10-12 Accepted:2022-02-25 Published:2022-09-12 Published online:2022-03-15
  • Contact: LI Xiao
  • Supported by:
    National Natural Science Foundation of China(31971791);National Key Research and Development Program of China(2017YFD0301105)

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摘要：

白粉病严重危害小麦生长及制约产量形成, 精确监测该病害对精确防控及保障国家粮食安全具有重要意义。在小麦孕穗、开花和灌浆期使用地物高光谱仪获取小麦冠层光谱数据, 利用一阶导数(FD)、二阶导数(SD)、对数变换(LOG)、倒数变换(1/R)和连续去除法(CR)对原始光谱(OR)进行光谱变换, 基于CARS算法和SPA算法相结合对五种变换的光谱数据和原始光谱进行特征波段提取, 进而利用偏最小二乘回归(PLSR)、岭回归(RR)和高斯过程回归(GPR)建模方法确立小麦白粉病病情指数(mDI)监测模型。结果表明, 一阶导数在Pearson相关性、两波段优化组合以及机器学习方法建模中, 综合表现最好, 是一种处理病害光谱数据的较好预处理方法。经过光谱数据变换后, 再使用CARS-SPA算法可以更有效的提取特征波段, 特征波段为411、450、476、543、561、594、624、671、726、780、835和950 nm。在不同机器学习建模方法对比中, 高斯过程回归(GPR)模型表现最佳, 其次为岭回归(RR)和偏最小二乘法回归(PLSR)。其中, 一阶导数结合GPR模型的估算精度最高, 建模集和验证集的平均R2为0.805, RMSE和MAE分别为2.532和2.164, 相较于OR-GPR模型, R2提升12%, RMSE和MAE分别降低19.6%和17.6%, 表明GPR模型在小麦白粉病监测中具有良好的估算能力。可见, 使用一阶导数预处理光谱数据, 采用CARS-SPA结合算法提取特征波段, 再利用高斯过程回归建模方法能够提升小麦白粉病遥感监测精度。研究结果为实现遥感监测作物病害提供了思路与方法。

关键词: 小麦白粉病, 光谱变换, 特征波段选择, 机器学习, 遥感监测

Abstract:

Powdery mildew seriously harms wheat plant growth and restricts grain yield formation. In this regard, accurate monitoring powdery mildew is of great significance for the precise prevention and control and ensuring national food security. During the booting, anthesis and gain filling stages of wheat, wheat canopy spectrum data were obtained using a ground based hyperspectrometer, and the original spectrum was pretreated including first derivative, second derivative, logarithmic transformation, reciprocal transformation, and continuous removal method. The characteristic bands of five transformed spectral data and the OR data were extracted based on the combination of the CARS algorithm and the SPA algorithm. Finally, powdery mildew disease index (mDI) as a wheat monitoring model was established using Partial Least Squares Regression (PLSR), Ridge regression (RR), and Gaussian process regression (GPR). The results showed that the FD method gave the best comprehensive performance in Pearson correlation, two-band optimized combination, and machine learning technique, and it was a better preprocessing technique for processing of disease spectrum data. After spectral data transformation, the CARS-SPA combined algorithm could extract the characteristic bands more effectively, and the characteristic bands were 411, 450, 476, 543, 561, 594, 624, 671, 726, 780, 835, and 950 nm, respectively. Comparing all spectral preprocessing models and different machine learning methods, GPR model performed the best, followed by RR and PLSR methods. FD-GPR combined model generated the highest estimation accuracy, the averaged R2, RMSE, and MAE in the modeling set and the verification set under FD-GPR combination model were 0.805, 2.532, and 2.164 respectively, compared with the OR-GPR combined model. The averaged R2 increased by 12%, and the RMSE and MAE decreased by 19.6% and 17.6%, respectively, indicating that the GPR model had a good estimation ability to monitor wheat powdery mildew. In conclusion, using the FD method to preprocess the spectral data, the CARS-SPA combined algorithm to extract the characteristic bands, and the GPR method to build the estimated model, can improve the remote sensing monitoring accuracy of wheat powdery mildew. The research results provide ideas and methods for remote sensing monitoring of crop disease.

Key words: wheat powdery mildew, spectral transformation, feature band selection, machine learning, remote sensing monitoring

图1

试验地空间布局图"

表1

不同时期和不同试验实测病情指数的统计分析"

试验
Experiment		 时期
Growth stage		 平均值
Mean		 最小值
Min.		 最大值
Max.		 标准差
Standard deviation		 样本数量
Number of samples
试验1
Exp.1		 孕穗期Booting 7.13 0 14.80 4.13 21
开花期Anthesis 11.05 0 20.70 5.22 44
灌浆期Filling 14.40 1.48 24.04 5.04 33
全生育期All stages 11.34 0 24.04 5.57 98
试验2
Exp.2		 孕穗期Booting 7.13 0 14.59 5.01 10
开花期Anthesis 10.56 1.03 19.05 5.20 20
灌浆期Filling 12.89 3.04 23.36 5.29 17
全生育期All stages 10.67 0 23.36 5.51 47

表2

本文所用高光谱植被指数"

植被指数
Vegetation index		 公式
Formula		 参考文献
Reference
PRI PRI=(R531-R570)/(R531+R570) [29]
RVSI RVSI=(R714+R752)/2-R733 [9]
SIPI SIPI=(R800-R445)/(R800+R680) [30]
ARI ARI=(R550)-1-(R700)-1 [31]
HI HI=(R534-R698)/(R534+R698)-0.5R704 [7]
PMI PMI=(R520-R584)/(R520+R584)+R724 [7]
NSRI NSRI=R890/R780 [32]
DGND DGND=(RD584×RH550-RD550×RH584)/(RD584×RH550+RD550×RH584) [8]
NDVI1 NDVI1=(R784-R636)/(R784+R636) [33]
RPMI RPMI=R744/R762-0.5R710 [34]

图2

不同病情指数下光谱反射率变化(a)、不同预处理方法的光谱变化(b)和相关性分析(c)"

图3

光谱变换数据与病情指数的相关系数绝对值"

图4

原始光谱、一阶导数和二阶导数的两波段优化组合"

图5

对数、倒数以及连续去除法变换的两波段优化组合"

表3

优化的最佳两波段组合形式及监测表现"

光谱变换Spectrum transform 归一化植被指数
Normalized difference
vegetation index (ND)		 简单比值植被指数
Simple ratio vegetation index
(SR)		 简单差值植被指数
Simple difference vegetation index (SD)
波段Wavelength R2 波段Wavelength R2 波段Wavelength R2
OR 786,785 0.398 786,785 0.398 450,444 0.462
FD 785,766 0.537 767,785 0.515 655,560 0.375
SD 705,691 0.298 749,691 0.344 709,664 0.392
LOG 450,444 0.430 444,450 0.430 786,785 0.398
1/R 786,785 0.398 785,786 0.398 786,785 0.384
CR 698,411 0.300 411,700 0.303 706,404 0.287

表4

植被指数与病情指数间决定系数R2在不同时期的表现"

植被指数
Vegetation index		 孕穗期
Booting		 开花期
Anthesis		 灌浆期
Filling		 全时期
All stages
PRI 0.329 0.452 0.392 0.329
RVSI 0.378 0.221 0.250 0.313
SIPI 0.314 0.281 0.280 0.283
ARI 0.358 0.432 0.470 0.276
HI 0.340 0.443 0.492 0.362
PMI 0.249 0.252 0.116 0.223
NSRI 0.219 0.492 0.154 0.303
DGND 0.453 0.624 0.533 0.604
RPMI 0.387 0.552 0.498 0.471
NDVI1 0.300 0.477 0.142 0.301

图6

基于CARS算法的特征波段筛选过程(a)及选择波段(b)"

图7

基于SPA算法的变量数与RMSE变化(a)及最佳选择波段(b)"

表5

基于不同光谱变换数据选择的特征波段"

光谱变换
Spectrum transform		 主要波段选择
Major band selection (CARS)		 最佳波段选择
Optimum band selection (SPA)
OR 400, 422, 452, 453, 470, 473, 528, 529, 549, 574, 586, 604, 620, 621, 674, 680, 683, 725, 726, 728, 761, 768, 780, 783, 830, 947, 950, 962 400, 470, 529, 586, 604, 621, 674, 683, 728, 761, 783, 830, 947
FD 411, 412, 450, 451, 453, 470, 476, 488, 494, 543, 544, 561, 574, 594, 610, 624, 660, 671, 721, 726, 746, 780, 813, 824, 835, 862, 950 411, 450, 476, 543, 561, 594, 624, 671, 726, 780, 835, 950
SD 420, 435, 502, 526, 550, 576, 650, 686, 705, 718, 720, 745, 749, 760, 768, 880, 894, 896, 920, 940, 943, 974, 976, 980 435, 576, 686, 705, 720, 760, 880, 896, 820, 976, 980
LOG 423, 458, 466, 467, 471, 560, 564, 568, 569, 570, 623, 658, 659, 682, 706, 711, 743, 762, 765, 768, 770 423, 458, 471, 564, 623, 682, 711, 743, 765
1/R 423, 430, 470, 493, 500, 514, 520, 564, 566, 567, 568, 590, 592, 613, 634, 661, 662, 718, 722, 749, 750, 755, 757, 758, 769, 875, 886 423, 493, 520, 568, 568, 613, 634, 661, 722, 755, 875
CR 431, 471, 472, 499, 500, 501, 584, 610, 613, 637, 663, 679, 683, 719, 798, 819, 847, 851, 937, 938 431, 472, 501, 584, 637, 719, 798, 812, 847, 938

表6

基于不同光谱变换筛选输入变量的机器学习模型表现"

光谱变换Spectrum transform 变量个数Number of variables 模型算法Modeling method 建模集Calibration set 验证集Validation set
R2 RMSE MAE R2 RMSE MAE
OR 13 PLSR 0.684 3.317 2.734 0.676 3.303 2.713
RR 0.703 3.140 2.666 0.686 3.108 2.641
GPR 0.722 3.108 2.641 0.715 3.189 2.608
FD 12 PLSR 0.748 2.886 2.466 0.757 2.775 2.340
RR 0.789 2.710 2.319 0.782 2.737 2.357
GPR 0.806 2.635 2.303 0.804 2.429 2.024
SD 11 PLSR 0.702 3.187 2.712 0.687 3.298 2.725
RR 0.721 3.116 2.698 0.703 3.201 2.684
GPR 0.743 2.985 2.524 0.725 3.016 2.606
LOG 9 PLSR 0.647 3.765 3.365 0.629 3.796 3.385
RR 0.668 3.658 3.213 0.659 3.764 3.367
GPR 0.701 3.165 2.726 0.692 3.231 2.786
1/R 11 PLSR 0.642 3.748 3.327 0.635 3.783 3.363
RR 0.675 3.416 2.964 0.667 3.517 3.051
GPR 0.695 3.349 2.879 0.681 3.389 2.863
CR 10 PLSR 0.746 2.901 2.466 0.732 2.968 2.487
RR 0.751 2.853 2.319 0.741 2.916 2.423
GPR 0.764 2.785 2.303 0.756 2.814 2.379

图8

基于一阶导数数据变换的机器学习模型检验结果"

图9

基于FD数据转换的三种建模方法在不同时期的模型表现"

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