CAJ | 학술논문

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.比较了受稻于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.
대식피병해적정학식별시채취식보조시적전제,동시대분시농약야구유적겁적지도작용.비교료수도우첨선충협박수도협편화건강협편색소함량、광보반사솔、고광보특정삼수,수해수도협편여건강협편상비,협록소화류호라복소함량분별강저18%화22%;광보반사솔재람자광、록광화홍광보단분별증가1.5,1화2.3배,재근홍외화단파홍외구역분별강저약28.9%화26.3%,홍변화람변분별람이약8화10nm,록봉화홍곡분별홍이약8.5화6 nm.이홍변면적화홍변위치작위C-SVC(비선성연간격분류궤)적수입향량,대수해화건강협편진행식별,정도위100%.연구표명,수도협편광보대병해협박구유현저적향응특정,이용C-SVC대수해화건강협편진행변별적방법시가행적.
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.