The Application Of Image Processing For Pebrine Detection

Pebrine is a kind of ancient, wildly distributed and strong destructive silkworm disease. It has done great damage to the silk industry in history. Now, it has been the main disease of silkworm in our country. And Pebrine is the disease of silkworm uniquely quarantined in international market. Since Louis Pasteur developed the method to dectect the pebrine by microscopic a hundred years ago, it has been used exclusively, so do in the custom today. In this paper image recognition technique is attempted to detect pebrine disease to replace the behindhand microscopic method.Dectection by microscopic has many defects, for example, it results in physically or mentally fatigued of human inspectors, so it can not guarantee the objectivity. And the dectection can not always implemented with great efficiency and high speed ,and so it can not meet the requirement of social legal system and standardization.In this research, according to the character of the micrograph, the original image is enhanced in brightness and contrast .Based on the threshold ,a new filtering algorithm with multiwavelet was proposed. And the mathematical morphology is considered as a effective way being applied for segmenting the pebrine and background according to the local gray feature. A mass of noises and small impurities are filtered by morphology filtering method and the preparatory separation of pebrine with impurities is realized. Eight parameters, such as perimeter, area, width, height, roundness, elongation-rate, complexity and color feature are extracted to remove the remainder impurities . Then pebrine was recognized and classified by neural network based on genetic algorithm.150 pebrine images have been tested in the experiment. The complete recognition exactness is 89%.This work is benefit to detecting the pebrine and achieve a good result.