The Key Diagnosis Technology Of Rice Blast Based On Hyper-spectral Image

The system of precision agriculture technology consists of 4 major components, which includes:data acquisition & data collection, data analysis and visualization, decision analysis and decision making, the implementation of agricultural job control. In these key technology areas, rapid acquiring and diagnoses of plant growth information is of great importance to the improvement of crop yield and quality.Traditional detection of rice blast disease is mainly based on molecular biology methods, which militate against on-line and real time detection. The main focus of this paper is on the key technology of acquiring and identifying the diseases stress in the plant growth stage. The early rice blast disease information was detected by optical imaging sensing device rapidly and non-destructively, and by the study of different prediction models, the incidence of rice blast development was monitored timely. The blast forecasting system constructed will provide the decision support for the variable spraying system.The main research contents of this paper and conclusions are as follows:(1) The high spectral feature extraction method was proposed for rice blast detection, the proposed method realized the high-dimensional spectral data compression and feature extraction. The Blast disease recognition model was constucted based on spectral feature and realized the identification of rice blast nondestructively and pricisely. The gaussian function fitting spectrum feature extraction, vegetation index spectrum feature extraction and wavelet approximate coefficient spectrum feature extraction are evaluated systematically and the classification discrimination model of rice blast based on gaussian fitting parameters, vegetation index and Wavelet approximate coefficient was constructed respectively.Research shows that three spectrum feature extraction method can effectively extract characteristics spectrum information for rice blast detection. The Optimization identification model based on spectral feature obtained by analysis,The classification accuracy of LDA identification model based on gaussian fitting parameters (peak high, peak width and peak area)extracted from 1 st derivative spectra is 100% on correction set and 96% on pridiction set, respectively.(2) The rice blast hyperspectral image feature informationwas acquired based on digital image processing techniques.The Blast disease recognition model was constucted based on image feature. Principal component image feature extraction, probability statistics filter image feature extraction and second order probability statistics filter image feature extraction are evaluated systematically.The optimization model based on feature extraction was obtained. The classification accuracy of stepwised LDA identification model based on principal component image statistical information (image mean and variance) is 98.3% on correction set and 97.5% on pridiction set, respectively.(3) The feature wavelengths to rice blast recognition sensitive was extracted based on spectrum and high hyperspectral imaging technology. The Blast disease recognition model was constucted based on spectrum and image information of feature wavelengths. The blast disease identification model based on image information was optimized. The optimimation identification of disease based on image feature wavelengths was acquired using principal component analytical-load coefficient method.The classification performance of PCA-LDA discriminant analysis model based on feature wavelengths (419nm,502nm,569nm,659nm,675nm,699nm, 742nm) image information, the classification accuracy is 92.7% on correction set and 92.5% on pridiction set, respectively.(4) The canopy spectral information and antioxidant enzymes activity relationship forecast model was set up for the first time.The early disease recognition was realized before the visible symptoms of the disease. The antioxidant enzyme (POD SOD CAT) enzyme activity value prediction model was constructed based on canopy spectral information using PLS regression method. Based on visible-infared band (400-1100 nm) canopy spectral diffuse information, the POD prediction correlation coefficient is 97.5% on calibration set and 90.79% on pridiction set; the SOD prediction correlation coefficient is 96.82% on calibration set and 86.65% on pridiction set. the CAT prediction correlation coefficient is 85.98% on calibration set and 66.63% on pridiction set. The antioxidants enzyme (POD SOD CAT) activity forecasting model based on was set up based feature wavelengths spectrum information for the first time,and simplified the forecasting model. Based on diffuse value of feature wavelengths (491 nm,545 nm,676nm,707 nm,741nm), the POD prediction correlation coefficient is 83.35% on calibration set and 75.19% on pridiction set. Based on diffuse value of feature wavelengths (526nm,550nm,672nm, 697nm,738nm,747nm), the SOD prediction correlation coefficient is 69.45% on calibration set and 54.88% on pridiction set. Based on diffuse value of feature wavelengths (491 nm,503nm,544nm,673nm,709nm,744nm), the SOD prediction correlation coefficient is 66.09% on calibration set and 46.91% on pridiction set.The research results indicated that the rice blast disease can identified quickly and non-destructively based on hyper-spectral image technology. For developing the rice blast disease fast detection instruments and sensors, the research laid a theoretical foundation and has the broad application prospect.