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

doi:10.3724/SP.J.1006.2022.11089

Abstract

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

FENG Zi-Heng, LI Xiao, DUAN Jian-Zhao, GAO Fei, HE Li, YANG Tian-Chong, RONG Ya-Si, SONG Li, YIN Fei, FENG Wei. Hyperspectral remote sensing monitoring of wheat powdery mildew based on feature band selection and machine learning[J].Acta Agronomica Sinica, 2022, 48(9): 2300-2314.

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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

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=(R_D584×R_H550-R_D550×R_H584)/(R_D584×R_H550+R_D550×R_H584)	[8]
NDVI₁	NDVI₁=(R₇₈₄-R₆₃₆)/(R₇₈₄+R₆₃₆)	[33]
RPMI	RPMI=R₇₄₄/R₇₆₂-0.5R₇₁₀	[34]

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)
光谱变换Spectrum transform	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

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

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

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

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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
光谱变换Spectrum transform	变量个数Number of variables	模型算法Modeling method	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

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