稻干尖线虫病胁迫水稻叶片波谱响应特征及识别研究

对植被病害的精确识别是采取植保措施的前提,同时对喷施农药也具有积极的指导作用。比较了受稻干尖线虫胁迫水稻叶片和健康叶片色素含量、光谱反射率、高光谱特征参数,受害水稻叶片与健康叶片相比,叶绿素和类胡萝卜素含量分别降低18%和22%;光谱反射率在蓝紫光、绿光和红光谱段分别增加1.5,1和2.3倍,在近红外和短波红外区域分别降低约28.9%和26.3%,红边和蓝边分别蓝移约8和10nm,绿峰和红谷分别红移约8.5和6 nm。以红边面积和红边位置作为C-SVC(非线性软间隔分类机)的输入向量,对受害和健康叶片进行识别,精度为100%。研究表明,水稻叶片光谱对病害胁迫具有显著的响应特征,利用C-SVC对受害和健康叶片进行辨别的方法是可行的。

Discrimination and Spectral Response Characteristic of Stress Leaves Infected by Rice Aphelenchoides besseyi Christie

An ASD Field Spec Pro Full Range spectrometer was used to acquire the spectral reflectance of healthy and diseased leaves infected by rice Aphelenchoides besseyi Christie,which were cut from rice individuals in the paddy field.Firstly,foliar pigment content was investigated.As compared with healthy leaves,the total chlorophyll and carotene contents (nag·g~(-1)) of diseased leaves decreased 18% and 22%,respectively.The diseased foliar content ratio of total chlorophyll to carotene was nearly 82% of the healthy ones.Secondly,the response characteristics of hyperspectral reflectance of diseased leaves were analyzed.The spectral reflectance in the blue (450-520 nm),green (520-590 nm) and red (630-690 nm) regions were 2.5,2 and 3.3 times the healthy ones respectively due to the decrease in foliar pigment content,whereas in the near infrared (NIR,770-890 nm) region was 71.7 of the healthy ones because of leaf twist,and 73.7% for shortwave infrared (SWIR,1 500-2 400 nm) region,owing to water loss.Moreover,the hyperspectral feature parameters derived from the raw spectra and the first derivative spectra were analyzed.The red edge position (REP) and blue edge position (BEP) shifted about 8 and 10 nm toward the short wavelengths respectively.The green peak position (GPP) and red trough position (RTP) shifted about 8.5 and 6 nm respectively toward the longer wavelengths.Finally,the area of the red edge peak (the sum of derivative spectra from 680 to 740 nm) and red edge position (REP) as the input vectors entered into C-SVC,which was an soft nonlinear margin classification method of support vector machine,to recognize the healthy and diseased leaves.The kernel function was radial basis function (RBF) and the value of punishment coefficient (C) was obtained from the classification model of training data sets (n=138).The performance of C-SVC was examined with the testing sample (n=126),and healthy and diseased leaves could be successfully differentiated without errors.This research demonstrated that the response feature of spectral reflectance was obvious to disease stress in rice leaves,and it was feasible to discriminate diseased leaves from healthy ones based on C-SVC model and hyperspectral reflectance.