Research On The Image Recognition Method Of Pebrine Disease Based On Computer Vision

Silkworm pebrine disease is one kind of infectious protozoal disease caused by nosema which parasite inside silkworm cells. It is an ancient, widely distributed and destructive strength of silkworm disease, which is commonly known as "silkworm cancer" and it is the sole statutory quarantine silkworm disease in the sericulture producing countries. In accordance with the principle which is preferred silkworm, silkworm eggs made none of the disease and the elimination of the virus, the female moth microscopic method has been used to prevent maternal transmission of virus particles, and now it has become the particulate detection means of preventing the silkworm diseases.As the manual examination has the problem of high labor intensity and the results can not reproduce and error-prone or undetected, the research to identification technology to the silkworm pebrine based on computer vision have been studied in this doctoral dissertation, and brought computer vision technology into the silkworm disease detection.The main contents are as following:(1) According to the characteristics that the microscopic image of particles has low-contrast and it is not clear, fuzzy image enhancement preprocessing method is proposed based on the fuzzy information. This method combines two algorithms which is the theory of Pal fuzzy enhancement based on global and the fuzzy contrast enhancement based on local and improved global contrast image and enhance the local details of the target image, this is conducive to the subsequent image segmentation.(2) In the light of technical problems for the microscopic image segmentation of particles under complex background and conditions, the technology of image segmentation for particles based on the model of HSI was proposed. According to the extraction criteria of color image feature for particles,the target of color image with a direct non-target separation of impurities was achieved, thus the possibility of false positives for the spores which the form is similar to particles was reduced; as well as the adverse effects for the segmentation which was influenced by the some of the background image of impurities was eliminated, the adaptability of image segmenta-tion which is the H component for color target based on two-dimensional Otsu segmentation method was improved.(3) According to the morphological characteristics of the image particles, the multi-feature extraction techniques of image feature for particles was studied and the original parameters set of features was extracted; in the light of images for particles more than the optimal combination of feature selection problem, a multi-feature fusion of particles in the image feature selection technique was proposed. By the method of correlation analysis, the removal of early start features and redundant information was achieved, the best classification sets of image feature for particles were determined based on the selection method of optimization feature by learning-based classifier.(4) The learning process of BP neural network is analyzed, and the improved BP optimal algorithm was proposed. Because of the problems within the BP neural network which are "local minimum" and "slow convergence", the hybrid training method of genetic neural network was studied, and the recognition technology for silkworm pebrine disease based on genetic neural network was proposed, furthermore the optimal network structure of the recognition system was determined. Based on the genetic neural network, the overall design of the recognition system about hardware and software implementation technology was conducted, and the validity and accuracy of the system was verified.