Continuum removal method for monitoring Fulvia fulva morbidity using hyperspectral data
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摘要: 阐明番茄叶霉病（Fulvia fulva）光谱特征并对其发病程度进行估测，可为番茄叶霉病大面积遥感监测提供依据。本研究通过分析番茄叶霉病不同发病程度下叶片光谱变化特征，筛选对发病程度识别的敏感波段。并利用去包络线法对光谱反射率进行处理，构建基于光谱特征吸收参量的发病程度估测模型。研究结果表明：随着叶霉病病害等级的加深，番茄叶片的原始光谱反射率、光谱敏感度、相对反射率均呈逐渐降低趋势；可见光波段（550~730 nm）和短波红外波段（1 860~2 260 nm）是识别番茄叶霉病发病程度的最佳波段；且随着病害等级的增加，吸收波段位置（λ）向短波方向移动，最大吸收深度（Dc）和吸收面积（A）均呈递增规律。利用光谱参数构建的番茄叶霉病病害等级预测的逐步回归模型R2达0.81，且模型验证结果较好。研究结果对利用高光谱遥感技术定量估测番茄叶霉病发病程度以及监测、防治农作物病虫害均具有较高的实用价值。Abstract: Fulvia fulva is a major disease in tomato cultivation. Compared with traditional laboratory analysis method, hyperspectral remote-sensing technology can provide simple, cost effective and non-destructive information that can offer processing methods for diagnosing and quantifying plant health. However, there are many limitations (e.g., large volume of data, redundant information and complex spectral) in dealing with hyperspectral data. This paper aimed to clarify the spectrum characteristics of tomato leaf infected by F. fulva and estimate its morbidity degree to provide theoretic basis for large-scale monitoring of F. fulva using hyperspectral remote sensing. To this end, experiments were carried out in 2016 in with disease nursery of tomato F. fulva in Shangqiu. In the research, leaf spectral reflectance of tomato was acquired via ASD FieldSpec 3 spectrometer (350-2 500 nm). The continuum removal method was adopted to process the original spectrum reflectance of tomato leaf with different morbidity degrees of F. fulva. The bands sensitive to F. fulva morbidity degree were selected and an inversion model of morbidity degree established based on absorption parameters of the spectrum features. The results showed that spectral reflectance of healthy tomato plants was higher than that of disease plants in the wavelength range of 350-2 500 nm. Besides, the reflectance, spectral sensitivity and relative reflectance decreased with increasing F. fulva morbidity degree. The most sensitive wave bands for distinguishing F. fulva severity were located in the visible region (550-730 nm) and shortwave infrared region (1 860-2 260 nm). With increasing F. fulva morbidity degree, the absorption position (λ) of both visible spectrum and shortwave infrared spectrum moved to the short wavelength band, while the maximum absorption depth (Dc) and area (A) increased. Particularly, the morbidity degree had a very significant correlation with maximum absorption depth in visible band (Dc1), maximum absorption area in shortwave infrared band (A2), maximum absorption depth in shortwave infrared band (Dc2), position of maximum absorption depth in visible band (λ1) and position of maximum absorption depth in shortwave infrared band (λ2). Consequently, a stepwise regression model for F. fulva morbidity degree was built based on the spectral absorption parameters. The model had good validation results, with determination coefficient (R2) of 0.81. The results of the study not only contributed to the estimation of F. fulva morbidity degree using hyperspectral remote-sensing data, but also had promising values of practical application in monitoring and preventing crop diseases.
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Keywords:
- Tomato /
- Fulvia fulva /
- High spectrum /
- Continuum removal /
- Morbidity degree
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图 1 番茄叶片的原始光谱、包络线和去包络线
Figure 1. Curves of original spectrum, continuum and continuum removal of tomato leaf
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图 2 不同叶霉病病害等级的番茄叶片光谱反射率值
Figure 2. Spectral reflectances at different Fulvia fulva morbidity levels of tomato leaf
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图 3 不同病害等级的番茄叶霉病光谱敏感度曲线
Figure 3. Spectral sensitivity curves at different Fulvia fulva morbidity levels of tomato leaf
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图 4 光谱敏感度与番茄叶霉病严重度的相关性
Figure 4. Correlation between spectral sensitivity and Fulvia fulva morbidity levels of tomato leaf
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图 5 图 5可见光波段(a)和短波红外波段(b)不同等级番茄叶霉病的番茄叶片相对反射率R'
Figure 5. Relative reflectance of tomato leaf with different Fulvia fulva morbidity levels in visible band (a) and shortwave infrared band (b)
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图 6 番茄叶霉病模型的预测发病程度与实测发病程度拟合
Figure 6. Comparison of Fulvia fulva morbidity level of tomato leaf between the actual values and the estimated values of the stepwise regression mode
表 1 不同病害等级的番茄叶片光谱吸收特征参量
Table 1 Spectral absorption parameters of tomato leaf with different Fulvia fulva morbidity levels
发病程度
Morbidity degree可见光 Visible band (550~-730 nm) 短波红外 Shortwave infrared band (1 860~-2 260 nm) Dc1 A1 λ1 Dc2 A2 λ2 0级 Health 0.71 74.25 682 0.74 213.78 1 921 1级 Level 1 0.76 84.19 681 0.75 226.19 1 915 2级 Level 2 0.78 89.00 679 0.71 219.19 1 914 3级 Level 3 0.81 97.83 677 0.79 237.31 1 914 4级 Level 4 0.83 108.13 677 0.81 260.99 1 913 Dc1:可见光波段最大波段深度; A1:可见光波段吸收峰面积; λ1:可见光波段最大波段深度对应的波长位置; Dc2:短波红外波段最大波段深度; A2:短波红外波段吸收峰面积; λ2:短波红外波段最大波段深度对应的波长位置。Dc1: the maximum absorption depth in visible band; A1: the maximum absorption area in visible band; λ1: the position of the maximum absorption depth in visible band; Dc2: the maximum absorption depth in shortwave infrared band; A2: the maximum absorption area in shortwave infrared band; λ2: the position of the maximum absorption depth in shortwave infrared band. 
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表 2 番茄叶霉病病害等级的光谱吸收特征参数逐步回归模型
Table 2 Stepwise regression model of Fulvia fulva morbidity level of tomato leaf by spectral absorption parameters
回归方程
Regression equation自变量
Independent回归系数相伴概率
Regression coefficient probability回归方程决定系数(R2)
Regression determination ratioy=45.95-15.69x1+0.09x2-15.80x3-0.15x4+0.04x5 x1(Dc1) 0.00 0.81** x2(A2) 0.00 x3(Dc2) 0.00 x4(λ1) 0.00 x5(λ2) 0.02 Dc1:可见光波段最大波段深度; A1:可见光波段吸收峰面积; λ1:可见光波段最大波段深度对应的波长位置; Dc2:短波红外波段最大波段深度; A2:短波红外波段吸收峰面积; λ2:短波红外波段最大波段深度对应的波长位置。Dc1: the maximum absorption depth in visible band; A1: the maximum absorption area in visible band; λ1: the position of the maximum absorption depth in visible band; Dc2: the maximum absorption depth in shortwave infrared band; A2: the maximum absorption area in shortwave infrared band; λ2: the position of the maximum absorption depth in shortwave infrared band.
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