基于去包络线法的番茄叶霉病发病程度估测方法

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(D_c)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R~2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。

Continuum removal method for monitoring Fulvia fulva morbidity using hyperspectral data

Fulvia fulva is a major disease in tomato cultivation. Compared with traditional laboratory analysis method, hyper-spectral remote-sensing technology can provide simple, cost effective and non-destructive information that can offer process-ing methods for diagnosing and quantifying plant health. However, there are many limitations (e.g., large volume of data, re-dundant information and complex spectral) in dealing with hyperspectral data. This paper aimed to clarify the spectrum char-acteristics of tomato leaf infected byF. fulvaand estimate its morbidity degree to provide theoretic basis for large-scale moni-toring 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 spec-trometer (350-2500 nm). The continuum removal method was adopted to process the original spectrum reflectance of tomato leaf with different morbidity degrees ofF. 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 350-2500 nm. Besides, the reflectance, spectral sensitivity and relative reflectance decreased with increasingF. fulva morbidity degree. The most sensitive wave bands for distinguishingF. fulva severity were located in the visible region (550-730 nm) and shortwave infrared region (1860-2260 nm). With increasingF. 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. Par-ticularly, 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 maxi-mum absorption depth in visible band (λ1) and position of maximum absorption depth in shortwave infrared band (λ2). Conse-quently, a stepwise regression model forF. 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 ofF. fulva morbidity degree using hyperspectral remote-sensing data, but also had promising values of practical appli-cation in monitoring and preventing crop diseases.