Monitoring The Rice Disease And Insect Stress With Remote Sensing

The specific objectives of this research were threefold. The first objective was to select the spectral region and wavebands, which were sensitive to rice disease and insect stress, and to develop the stressed spectral indices with the multiform data processing methods through five species of rice disease and insect in Zhejiang Province and Heilongjiang Province. The second objective was to explore the discrimination of different endangered categories of rice disease and insect and estimation of harm indices such as pigment content and disease severity index. The third was to extract the endangered paddy area caused by rice planthopper and assess the yield loss from the QuickBird images. The summary study content and result were presented as follows:(1) Change analysis of hytperspectral characterization caused by rice disease and insect stressWhen disease and insect stressed rice, with the exception of lodged rice caused by rice planthopper and rice panicle blast, the hyperspectrl reflectance increased in the visible region and the hyperspectrl reflectance decreased in the near infrared and shortwave infrared regions. The red edge and blue edge also shifted toward the short spectral region about 10 nanometers. The green peak and red trough also shifted toward the longer waveband 8 nanometers.(2) Selection of sensitive spectral region and development of stressed spectral indices responding to rice disease and insect stressThe raw spectra, inverse logarithmatic spectra, first-order and second-order derivative spectra of healthy and non-healthy rice leaves caused by disease and insect was analyzed with three analysis methods, which are the continuum removal, spectral sensitivity analysis and correlation coefficients between agricultural parameters and spectral reflectance. The sensitive spectral regions, which were more sensitive to rice disease and insect, were located in the blue region (460 -520 nm), green region (530-590 nm), red region (620 - 680nm) and the red edge region (690-730 nm) in despite of different transformation spectral reflectance. And then 22 stress indices of disease and insect were developed in this study.(3) Study of discrimination method on the endangered categories of different rice disease and insect stressCluster analysis (CA), probabilistic neural network (PNN), learning vector quantization neural network (LVQ) and C-support vector classification (C-SVC) machine were utilized to discriminate the endangered categories of different rice disease and insect stress consisting of rice brown spot, rice Aphelenchoides besseyi Christie, rice panicle blast, rice planthopper and rice leaf roller. Three indices such as classification accuracy, convenient utilization degree and consuming time were applied to assess the above-mentioned four discrimination methods. The successive order of classification accuracy were PNN (93.5%),C-SVC (90.5%),CA (84.3%) and LVQ (83.2%). The successive order of convenient utilizations degree were PNN>C-SVC>LVQ>CA, the anterior the position, the higher the convenient utilization degree. The successive order of consuming time were C-SVC