The Construction Of Diagnostic Model For Rice Leaf Diseases Based On Near Infrared Spectra Technologies

This paper studies the spectral data of rice flax leaf spot and sheath blight disease; find the optimal spectral pre-processing and analysis algorithms; and established the optimal diagnostic model and severity identify model; to provide theoretic foundation for further monitoring rice flax leaf spot and sheath blight disease at large scale using airborne and airspace remote sensing as flats and reference for monitoring rice other disease. In this study the works were completed as follows:Spectral data acquisition stage, the influence of external environment, machine and sample to study fully considers for the spectral reflectance; use tools of blade grippers and naked fiber optic to measure rice leaf spectra data, and find which tool have the better result; find the change on the width of near infrared spectral reflectance have a big affect on rice leaf; also puts forward the matters needing attention when collect rice disease spectrum data, choose spectrum data for pre-processing stage.Spectral data pretreatment stage, for the problem of spectra data have noise and scattering, this pepar studied the S-G smoothing, kernel smoothing, derivative algorithm, multiplicative scatter correction of the data pre-preprocessing algorithm, and compared the results of preprocessing algorithms, and ultimately to find suitable preprocessing algorithm of rice diseases.Rice disease spectrum characteristic analysis stage, compared and analyzed rice leaves of normal and have difference disease which have the same width and variety, we found that: In the range of 400 ~ 700nm, with the level of flax leaf spot disease and the sheath blight disease was gradually increased to increase reflectivity, the increased speed are more rapid than flax spot; In the range of 700 ~ 1300 nm near-infrared region, with the level of flax spot disease and the sheath blight disease increase leaf reflectance gradually decreased; In the range of 1900nm ~ 2000nm, with the level of the sheath blight disease increase the leaf reflectance gradually increase, while the other band no law.Feature extraction stage, for the problems of near infrared spectrum have large amount of data and band numerous, according to the correlation of severity and reflectivity data, select rice sensitive band of the spot and sheath blight, then using principal component analysis algorithm select two component which accumulated more than 85 percent contribution; finally select five main features bands used for modeling: 990nm, 1850nm, 660nm,1921nm, 1933nm; after the original spectral reflectance process with the first derivative, select the best correlation with the severity of red edge area as a health and sick leaves important parameter.Model establishment stage, rice spot and sheath blight disease severity diagnosis model were established through 5 obtained the characteristic bands and the red edge parameters. Through the model validation showed that the following model has the highest: (1) rice spot, R1933nm- R990nm, y=11.4971x+5.0313, r=0.8912;(2) sheath blight, red edge area parameters and R660nm-R990nm, y=-11.1037x+3.5195,r=0.89502;y=6.2834x+2.8464, r=0.8920;use stepwise regression method and the BP neural network method to Identify the rice spot and sheath blight disease, including 60 samples used for modeling, 41 samples used to model test, the results show that compared to stepwise regression analysis, when 660nm, 990nm, 1933nm three features bands combined, the BP neural network identification accuracy than stepwise regression analysis.