基于无人机多光谱和热红外影像信息融合的小麦白粉病监测

doi:10.3864/j.issn.0578-1752.2022.05.005

Abstract

Abstract:

【Objective】Wheat growth and yield can be seriously affected by powdery mildew. Establishing the multi-source data fusion method for real-time monitoring of powdery mildew of wheat could provide technical support for accurate prevention and control of diseases and guaranteeing national food security. 【Method】During the wheat flowering and filling period, a six-rotor UAV equipped with multi-spectral sensor and thermal imager was used as a remote sensing data acquisition platform to obtain remote sensing images of different degrees of wheat powdery mildew. Then, vegetation index (VIs), texture feature (TFs) and temperature feature (T) were extracted from multi-spectral and thermal infrared images of different disease degrees on a low-altitude drone platform by ENVI software. Finally, the wheat powdery mildew disease index model were built by multiple linear regression (MLR), back propagation neural network (BP), random forest (RF) and extreme learning machine (ELM). 【Result】The precision of the RF model based on both single and multiple data sources was higher than that of the other models. Among the three data sources of the RF model, the vegetation indices (VIs-RF, R ²= 0.667, RMSE=5.712, RPD=1.572) were the most suitable for powdery mildew monitoring, followed by the temperature feature (T-RF, R ²= 0.559, RMSE=6.563, RPD=1.430) and texture features (TFs-RF, R ² = 0.495, RMSE=7.014, RPD=1.348). When combining multiple data sources, a precision for the RF model combining vegetation indices and texture features (VIs & TFs-RF) of 0.701 could be obtained, which was 5.101% higher than that of the VIs-RF model, while RMSE was 7.073% lower and RPD was 9.672% higher, whereas the precision parameters of the RF model combining vegetation indices and the temperature feature (VIs & T-RF) were R ² = 0.750, RMSE = 4.704, RPD = 1.912. For all three remote sensing data sources (VIs & TFs & T-RF), the following accuracies resulted: R ² = 0.820, RMSE = 4.677, RPD=1.996. As compared to the VIs-RF model, R ² improved by 12.453%, RMSE by 17.640% and RPD by 21.667% for the (VIs & T-RF) model, whereas for the three remote sensing sources, R ²improved by 23.181%, RMSE by 18.113% and RPD by 26.981%. At the same time, 10 fold cross validation of different models was carried out, which further confirmed that RF model had stable performance and good estimation results in multi-data source fusion modeling. 【Conclusion】 The precision of wheat powdery mildew monitoring could be improved by using multi-data-sources collaborative ML modeling. This research provided technical support for large-area and high-precision remote sensing of crop diseases.

Key words: powdery mildew, UAV, machine learning, information fusion, remote sensing monitoring

FENG ZiHeng,SONG Li,ZHANG ShaoHua,JING YuHang,DUAN JianZhao,HE Li,YIN Fei,FENG Wei. Wheat Powdery Mildew Monitoring Based on Information Fusion of Multi-Spectral and Thermal Infrared Images Acquired with an Unmanned Aerial Vehicle[J].Scientia Agricultura Sinica, 2022, 55(5): 890-906.

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植被指数 Vegetation index	计算公式 Calculation formula	参考文献References
绿度归一化植被指数GNDVI	GNDVI=(R_NIR-R_Green)/(R_NIR+R_Green)	[27]
氮反应指数NRI	NRI=(R_Green-R_Red)/(R_Green+R_Red)	[28]
花青素反射指数ARI	ARI=(1/R_Green)-(1/R_Red)	[29]
叶斑病指数 CLSI	CLSI=(R_Re-R_Green)/(R_Re+R_Green)-R_Re	[6]
植物色素比例PPR	PPR=(R_Green-R_Blue)/(R_Green+R_Blue)	[30]
绿度指数GI	GI=R_Green/R_Red	[31]
优化土壤调节植被指数TCARI	TCARI=3×[(R_NIR-R_Red)-0.2×(R_NIR-R_Green)×R_NIR/R_Red]	[32]
可见光大气阻抗指数VARI	VARI=(R_Green-R_Red)/(R_Green+R_Red-R_Blue)	[33]
植被衰减指数PSRI	PSRI=(R_Red-R_Green)/R_NIR	[34]

光谱参数 Spectral parameter	相关系数 Correlation coefficient (r)	光谱参数 Spectral parameter	相关系数 Correlation coefficient (r)
R₄₅₀	0.352**	ARI	0.580**
R₅₅₀	0.489**	CLSI	0.586**
R₆₈₅	0.413**	PPR	-0.578**
R₇₂₅	0.286*	GI	-0.614**
R₇₈₀	-0.321**	TCARI	-0.568**
GNDVI	0.504**	VARI	-0.552**
NRI	-0.602**	PSRI	0.640**

纹理特征Texture feature	Blue	Green	Red	REDedge	NIR
方差 Variance (Var)	0.381**	0.277*	0.186	0.294**	0.267*
对比度Contrast ratio (Con)	0.289*	0.281*	0.227*	0.370**	0.273*
差异性Difference (Dis)	0.328**	0.274*	0.242*	0.353**	0.260*
熵 Entropy (Ent)	0.387**	0.240*	0.208	0.277*	0.219

自变量类型 Independent variable type	变量个数 Number of variables	模型算法 Model algorithm	训练集 Training set			验证集Validation set
自变量类型 Independent variable type	变量个数 Number of variables	模型算法 Model algorithm	R²	RMSE	RPD	R²	RMSE	RPD
植被指数VIs	9	MLR	0.509	5.796	1.437	0.528	8.594	0.957
		BP	0.613	5.780	1.450	0.596	6.917	1.353
		ELM	0.637	5.581	1.517	0.624	6.663	1.413
		RF	0.672	5.398	1.580	0.661	6.025	1.563
纹理特征TFs	4	MLR	0.247	7.651	1.210	0.216	10.515	0.703
		BP	0.279	8.004	1.064	0.289	9.241	0.864
		ELM	0.466	6.461	1.354	0.420	9.698	0.796
		RF	0.499	7.534	1.242	0.491	6.493	1.453
温度特征T	2	MLR	0.351	7.550	1.249	0.416	9.114	0.834
		BP	0.431	6.944	1.322	0.404	8.154	1.061
		ELM	0.540	6.065	1.368	0.495	8.495	0.980
		RF	0.556	6.988	1.324	0.561	6.138	1.536

自变量 Independent variable type	变量个数 Number of variables	模型算法 Model algorithm	训练集Training set			验证集Validation set
自变量 Independent variable type	变量个数 Number of variables	模型算法 Model algorithm	R²	RMSE	RPD	R²	RMSE	RPD
温度特征结合纹理特征T&TFs	6	MLR	0.504	7.527	1.251	0.501	7.492	1.217
		BP	0.563	6.205	1.407	0.601	6.384	1.478
		ELM	0.620	5.821	1.489	0.613	7.124	1.304
		RF	0.667	6.180	1.474	0.647	6.565	1.436
植被指数结合纹理特征VIs&TFs	13	MLR	0.555	6.600	1.516	0.541	6.861	1.366
		BP	0.587	5.274	1.725	0.607	6.187	1.525
		ELM	0.649	5.381	1.717	0.652	5.830	1.609
		RF	0.725	5.242	1.730	0.676	5.373	1.717
植被指数结合温度特征VIs&T	11	MLR	0.618	6.120	1.645	0.613	5.367	1.718
		BP	0.627	6.110	1.646	0.705	5.907	1.591
		ELM	0.695	5.924	1.736	0.744	6.190	1.574
		RF	0.741	4.686	1.953	0.758	4.722	1.871
植被指数结合温度和纹理特征VIs&T&TFs	15	MLR	0.603	6.499	1.694	0.638	4.932	1.821
		BP	0.667	5.404	1.896	0.718	5.820	1.796
		ELM	0.753	4.754	1.972	0.786	5.445	1.812
		RF	0.806	4.853	1.968	0.836	4.501	2.023

Wheat Powdery Mildew Monitoring Based on Information Fusion of Multi-Spectral and Thermal Infrared Images Acquired with an Unmanned Aerial Vehicle

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模型算法 Model algorithm		训练集Training set			验证集Validation set
模型算法 Model algorithm		R²	RMSE	RPD	R²	RMSE	RPD
多元线性回归MLR	最优模型Optimal model	0.739	4.956	1.607	0.705	4.753	1.832
	均值Mean	0.637	5.733	1.567	0.616	5.947	1.548
	方差Variance	0.044	0.391	0.105	0.081	1.348	0.319
	极差Range	0.167	1.189	0.387	0.238	4.215	1.145
反向传播神经网络BP	最优模型Optimal model	0.757	5.417	1.874	0.765	5.405	2.081
	均值Mean	0.706	5.411	1.628	0.725	5.226	1.626
	方差Variance	0.036	0.265	0.129	0.031	1.391	0.319
	极差Range	0.097	0.760	0.509	0.094	4.391	1.030
极限学习机ELM	最优模型Optimal model	0.767	4.657	1.819	0.803	4.780	2.206
	均值Mean	0.745	4.796	1.738	0.785	5.195	1.720
	方差Variance	0.020	0.195	0.088	0.046	1.384	0.377
	极差Range	0.062	0.622	0.268	0.160	4.217	1.001
随机森林RF	最优模型Optimal model	0.847	4.026	1.937	0.852	2.638	2.505
	均值Mean	0.841	4.055	1.813	0.863	4.183	1.843
	方差Variance	0.013	0.122	0.077	0.028	0.805	0.315
	极差Range	0.039	0.354	0.230	0.094	3.012	1.063

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