Microscopic image anthracnose spore density calculation method based on image processing technology

Abstract

The invention provides a microscopic image anthracnose spore density calculation method based on an image processing technology, and the method comprises the steps: 1, marking an anthracnose spore image, and constructing a Gaussian mixture model; 2, segmenting the anthracnose spore image by adopting a Gaussian separation degree-based adaptive Otsu threshold; 3, performing open filtering, closed filtering, shape filtering and area filtering on the segmented image; 4, extracting a target edge contour; 5, calculating the similarity between each pixel in the image and the Gaussian mixture model; 6, calculating an edge stop function based on symbol logarithm similarity; 7, carrying out contour evolution on the edge contour obtained in the step 4 to obtain an accurate contour of the anthrax spores; and 8, counting the area of connected domains and the number of the connected domains of the result image to obtain the number of the anthrax spores. The density statistical information obtained by the method disclosed by the invention can provide a basis for disease condition estimation of crops or activity and drug resistance analysis of anthrax spores.

Description

technical field [0001] The invention relates to the fields of digital image image processing and pattern recognition, in particular to a method for calculating the density of anthrax spores in a microscopic image based on image processing technology. Background technique [0002] In the process of crop growth and storage and transportation of fruits and vegetables, diseases are the main reason for reducing the efficiency of agricultural production. About 50% of citrus peels are destroyed by diseases every year; in tropical regions, half of the harvested fruits and vegetables are lost to rot. These diseases are mainly attributed to the pathogenic action of fungal spores. If fungal spores can be detected and counted at an early stage of the disease, appropriate preventive measures can be taken in advance. In addition, during the research and development of disease control drugs, the density statistics of fungal spores can reveal the drug resistance and activity of spores, wh...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is based on the adaptive Otsu gray scale feature segmentation method of Gaussian separation and the filtering method based on shape and area, which can also effectively segment the anthrax spore images with diversity distribution and class imbalance distribution, and solve the problem of solves the problem of being limited to a single scene distribution in traditional methods

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