基于成像高光谱的小麦冠层白粉病早期监测方法

doi:10.3864/j.issn.0578-1752.2022.06.005

Abstract

Abstract:

【Objective】In this study, the near-ground imaging spectrometer was used to obtain time-series images of wheat canopy after inoculation with powdery mildew, which aimed to explore the ability and performance of the combination of spectral feature and texture feature in the early detection of wheat powdery mildew at canopy scale. 【Method】 Based on the field trials of wheat varieties with different disease resistance in different years, the wavelet features sensitive to wheat powdery mildew were extracted by continuous wavelet transform (CWT) method, and the corresponding texture features were extracted based on spectral features to construct normalized difference texture index (NDTI). Meanwhile, the representative traditional vegetation indices (Vis) were selected. Then, based on these features and combinations, the partial least squares discriminant analysis (PLS-LDA) model was used to establish wheat canopy healthy and disease recognition model. The partial least squares regression (PLSR) was used to estimate the severity of wheat canopy disease. The technique was used to distinguish the healthy and disease wheat at different days after inoculation based on the optimal features and combinations. 【Result】 Based on CWT, the selected four wavelet features were 595 nm (yellow region) at 6 scales, 614 nm (red region) at 5 scales, 708 nm (near infrared region) at 3 scales, and 754 nm (near infrared region) at 4 scales respectively. The following texture features were selected for the best texture index combination: ENT₇₅₄, MEA₇₅₄, ENT₇₀₈, ENT₅₉₅, ENT₆₁₄, HOM₇₀₈, HOM₅₉₅, HOM₆₁₄, DIS₅₉₅, HOM₇₅₄ and DIS₆₁₄. Besides, it was found that the texture feature MEA₇₅₄ had the superior performance among all the texture, with the highest correlation between the severity of disease and texture (R²=0.67). The PLS-LDA model based on the combination of wavelet feature and texture feature had the highest accuracy, with the overall classification accuracy of 81.17% and the Kappa coefficient of 0.63. In addition, the PLSR model based on spectral index and texture index was the best, and the R ² of modeling and testing was 0.76 and 0.71, respectively. The severity of wheat canopy powdery mildew was about 26% (about 24 days after inoculation), which was identified in this study at the earliest time. 【Conclusion】 The wheat healthy and disease recognition model based on the combination of wavelet feature and texture feature could significantly improve the accuracy of disease classification, and the combination of spectral index and texture index could significantly improve the accuracy and stability of disease severity estimation. The method and results of this study could provide the reference for disease monitoring of other crops and technical support for accurate application of modern intelligent agriculture.

Key words: wheat powdery mildew, canopy, hyperspectral imaging, continuous wavelet transform, texture feature

CAI WeiDi,ZHANG Yu,LIU HaiYan,ZHENG HengBiao,CHENG Tao,TIAN YongChao,ZHU Yan,CAO WeiXing,YAO Xia. Early Detection on Wheat Canopy Powdery Mildew with Hyperspectral Imaging[J].Scientia Agricultura Sinica, 2022, 55(6): 1110-1126.

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名称Name	函数表达式Function expression	描述Description
均值 Mean, MEA	$\sum_{i, j=0}^{N=j} i \times P_{i, j}$	反映了灰度平均值情况 Reflect the gray mean value
方差 Variance, VAR	$\sum\limits_{i,j=0}^{N-1}{i\times {{P}_{i,j}}{{(i-MEA)}^{2}}}$	反映了灰度变化的大小Reflect the change of grayscale
均一性 Homogeneity, HOM	$\sum_{i=0}^{N-1} \sum_{j=0}^{N-1} P_{i, j} P_{i, j} /\left[1+(i+j)^{2}\right]$	反映了纹理局部同质性Reflect local homogeneity of texture
对比度 Contrast, CON	$\sum_{n=0}^{N-1} n^{2}\left\{\sum_{i=0}^{N-1} \sum_{\substack{j=0 \\\|i-j\|=n}}^{N-1} P_{i, j}\right\}$	反映了纹理的清晰度Reflect texture sharpness
异质性 Dissimilarity, DIS	$\sum_{i, j=0}^{N-1} i \times P_{i, j}\|i-j\|$	反映纹理的相似性Reflect texture similarity
熵 Entropy, ENT	$\sum_{i=0}^{N-1} \sum_{j=0}^{N-1} P_{i, j} \log P_{i, j}$	反映图像具有的信息量Reflect the amount of image information
角二阶矩 Second Moment, SEM	$\sum_{i, j=0}^{N=j} i \times P_{i, j}^{2}$	反映了图像灰度分布的均匀性 Reflect the uniformity of image gray distribution
相关性 Correlation, COR	$\left[\sum_{i=0}^{N-1} \sum_{j=0}^{N-1} i j P_{i, j}-u_{1} u_{2}\right] / \sigma_{1} \sigma_{2}$	反映某种灰度值沿某个方向的延伸长度 Reflect the extension length of some gray value along a certain direction

定义 Definition	方程 Equation	相关生理参数 Relative physical parameter
白粉病指数 Powdery mildew index, PMI	(R₅₁₅-R₆₉₈)/(R₅₁₅+R₆₉₈)-0.5×R₇₃₈	小麦病害Wheat disease^[49]
简单修改比 Modified simple ratio, MSR	(R₈₀₀/R₆₇₀ -1)/ (R₈₀₀/R₆₇₀ + 1)1/2	叶面积Leaf area^[50]
光化学反射指数 Photochemical reflectance index, PRI	(R₅₇₀- R₅₃₁)/ (R₅₇₀ + R₅₃₁)	光合辐射Photosynthetic radiation^[51]
光合辐射 Photosynthetic radiation, PhRI	(R₅₅₀ - R₅₃₁)/ (R₅₅₀+ R₅₃₁)	光合利用效率Light use efficiency^[51]
改进的叶绿素吸收比指数 Modified chlorophyll absorption ratio index, MCARI	[(R₇₀₁-R₆₇₁)-0.2(R₇₀₁-R₅₄₉)]/(R₇₀₁/R₆₇₁)	叶绿素吸收Chlorophyll absorption^[52]
花青素反射指数 Anthocyanin reflectance index, ARI	R₅₅₀^-1-R₇₀₀^-1	花青素含量Anthocyanin content^[53]
与结构无关的色素指数 Structure independent pigment index, SIPI	(R₈₀₀ - R₄₄₅)/(R₈₀₀ - R₆₈₀)	色素含量Pigment content^[54]
归一化色素叶绿素比值指数 Normalized pigment chlorophyll ration index, NPCI	(R₆₈₀- R₄₃₀)/(R₆₈₀ + R₄₃₀)	叶绿素比值Chlorophyll ratio^[54]
红边植被胁迫指数 Red-edge vegetation stress index, RVSI	[(R₇₁₂ + R₇₅₂)/2] - R₇₃₂	生物量Biomass^[55]

光谱区域 Spectral region	入选波段 Selected wavelength	尺度 Scale	决定系数 R²
黄光区域Yellow region	595	6	0.82
红光区域Red region	614	5	0.83
近红外区域Near infrared region	708	3	0.85
近红外区域Near infrared region	754	4	0.84

模型 Model	纹理指数 Texture index	入选纹理 Selected texture		决定系数 Determinant (R²)
模型 Model	纹理指数 Texture index	T1	T2	决定系数 Determinant (R²)
线性模型 Linear model	NDTI (T1, T2)	ENT₇₅₄	MEA₇₅₄	0.51
		MEA₇₅₄	ENT₇₀₈	0.50
		MEA₇₅₄	ENT₅₉₅	0.50
		MEA₇₅₄	ENT₆₁₄	0.50
		MEA₇₅₄	HOM₇₀₈	0.47
		MEA₇₅₄	HOM₅₉₅	0.47
		MEA₇₅₄	HOM₆₁₄	0.46
		HOM₇₅₄	MEA₇₅₄	0.46
		MEA₇₅₄	DIS₅₉₅	0.46
		MEA₇₅₄	DIS₆₁₄	0.45

纹理特征 Texture feature	小波特征 Wavelet feature
纹理特征 Texture feature	595 nm	614 nm	708 nm	754 nm
均值 MEA	0.00ns	0.00ns	0.01ns	0.67***
方差 VAR	0.29***	0.31***	0.29***	0.01ns
均一性 HOM	0.25**	0.23**	0.27**	0.27**
对比度 CON	0.27***	0.29***	0.27***	0.01ns
异质性 DIS	0.28**	0.30***	0.28***	0.08**
熵 ENT	0.30ns	0.26ns	0.35ns	0.38ns
角二阶矩 SEM	0.28***	0.27***	0.28***	0.28***
相关性 COR	0.01ns	0.02ns	0.03ns	0.01ns

Early Detection on Wheat Canopy Powdery Mildew with Hyperspectral Imaging

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2018	测试日期 Test date
2018	04-09	04-17	04-19	04-25	04-28	05-03	05-09
接种后天数DAI	8	16	18	24	27	32	38
样本数量Sample number	14	13	14	14	14	14	13
2017	测试日期 Test date
2017	03-27	04-01	04-13	04-22	04-28	05-10
接种后天数DAI	6	11	23	32	38	50
样本数量Sample number	6	6	10	12	12	12

输入特征 Input feature	特征数量 Number of features	分类精度 Classification accuracy (%)		总体分类精度 OAA (%)	卡帕系数 Kappa
输入特征 Input feature	特征数量 Number of features	健康 Healthy	感病 Infected	总体分类精度 OAA (%)	卡帕系数 Kappa
小波特征与纹理特征结合 WFs & TFs	36	92.54	72.41	81.17	0.63
光谱指数 VIs	9	89.55	70.11	78.57	0.58
光谱指数与纹理指数结合 VIs & NDTIs	19	86.57	71.26	77.92	0.56
纹理特征 TFs	32	85.07	71.26	77.27	0.55
纹理指数 NDTIs	10	77.61	70.11	73.38	0.47
小波特征 WFs	4	82.09	64.37	72.08	0.45

输入特征 Input feature	特征数量 Number of features	建模 Calibration		检验 Validation
输入特征 Input feature	特征数量 Number of features	R²	RMSE	R²	RMSE	RRMSE
小波特征与纹理特征结合WFs & TFs	36	0.8	8.13	0.68	11.84	0.39
光谱指数VIs	9	0.76	8.70	0.69	11.54	0.38
光谱指数与纹理指数结合VIs & NDTIs	19	0.76	8.89	0.71	11.30	0.38
纹理特征TFs	32	0.72	9.42	0.42	15.94	0.53
小波特征WFs	4	0.64	9.72	0.61	12.93	0.43
纹理指数NDTIs	10	0.59	9.85	0.47	15.31	0.51

年份 Year	分类精度 Classification accuracy (%)
年份 Year		8d(7.4%)	16d(15.9%)	18d(26.2%)	24d(27.7%)	27d(32.3%)	32d(41.7%)	38d(63.2%)
2018	健康Healthy	83.33	100	83.33	100	100	100	100
	感病Infected	75.00	100	75.00	100	100	100	100
	Kappa	0.57	0.56	0.57	0.84	1	1	1
		6d(1.5%)	11d(8.9%)	23d(25.2%)	32d(26.2%)	38d(33%)	50d(33%)
2017	健康Healthy	66.67	61.67	71.67	100	100	100
	感病Infected	66.67	75.00	77.50	100	100	100
	Kappa	0.33	0.5	0.55	0.85	1	1

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