Development And Research Of Visualized Pest And Disease Information Collection And Processing System

At present,the level of infrastructure and information service for agricultural production is lagging behind.The performance of agricultural environmental factors,crop growth situation,the occurrence of disasters can not be real-time monitoring;Decision-making management and front-line producers and scientists are difficult to achieve real-time information sharing.Therefore,based on the current development of agricultural information service system at home and abroad,this paper aims at the shortcomings of domestic agricultural information construction,under the ASP.NET platform,based on SQL Server database,network communication,microprocessor,streaming media communication Technology,combined with the image pattern recognition theory,we research and develop a visualization of pest and disease information collection and processing system.The main research contents include the following aspects:1.We identify a visualization of pest and disease information collection and processing system solution,designe and set up the acquisition system hardware platform,and realize real-time acquisition of pest and disease information hardware on-chip software and data storage software program.2.Aiming at the problem of microscopic image adhesion and sporulation of spore pathogens,it is difficult to divide and overdrive.We take the wheat flour powder as an example,analyzed the main factors affecting the accuracy of segmentation.A new method is proposed to enhance the image by using the high and low cap transform,then the residual filter is removed by the median filter,the binary image before the distance transformation is processed by the improved morphological filter,and the gray scale image of the distance transformation is extended Minimization and morphological reconstruction.Finally,the method of watershed segmentation is applied to realize the accurate segmentation of spore image.According to the characteristics of six kinds of spore images to identify the region of interest to be identified,removed most of the pseudo-area,reducing the amount of identification to determine the ROI regions.3.We experimentally select HOG characteristics,LBP features,Haar characteristics,respectively,using AdaBoost classifier for training.Which will be used in the region of interest identification.Compared with the analysis,it is concluded that the classifier based on LBP is the best.4.In the ASP.NET platform,we achieved based on the B / S architecture PC system software,completed the entire system development.The software has been field tested and analyzed the test results.The average accuracy rate of the actual software identification test is 76% and the average false alarm rate is 19%.