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Image processing and mode recognition technology-based rice blast spore microscopic image recognition method

A pattern recognition and image processing technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as a lot of time, eye fatigue, and increase the difficulty of early disaster detection.

Inactive Publication Date: 2015-07-22
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to solve the inaccurate discrimination of field detection usually based on naked eye observation, the current experiment requires a huge number of samples, and the individual spores are very small, traditional microscope spore counting takes a lot of time, and long-term observation is easy to cause eye fatigue and increase the burden on the eyes. Due to the difficulty of finding the initial disaster situation, a microscopic image recognition method of rice blast spores based on image processing and pattern recognition technology is proposed

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  • Image processing and mode recognition technology-based rice blast spore microscopic image recognition method

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specific Embodiment approach 1

[0038] Specific embodiment one: a kind of microscopic image recognition method of rice blast spores based on image processing and pattern recognition technology of the present embodiment is specifically prepared according to the following steps:

[0039] Step 1. The imaging system acquires the image, that is, the original image is converted into a grayscale image;

[0040] Step 2: Perform image enhancement processing on the grayscale image, and the obtained histogram equalization effect image is as follows: Figure 4 ;

[0041] Step 3, performing local adaptive threshold segmentation on the histogram equalization effect map to obtain a binarized effect map;

[0042] Step 4, performing denoising processing on the binarized effect image through morphological transformation to obtain the denoising effect image;

[0043] Step 5. Pass the denoising effect image through Canny edge detection to obtain a foreground image containing only edge information, extract the contours of the ...

specific Embodiment approach 2

[0051] Embodiment 2: The difference between this embodiment and Embodiment 1 is that in step 1, the imaging system acquires an image, that is, the specific process of converting the original image to a grayscale image is as follows:

[0052] Since the color recognition degree of microscopic images is not high, the identification of spores is mainly based on the brightness of the light intensity; and the single-channel image data is more conducive to subsequent image processing, which can shorten the processing time;

[0053] In RGB models such as Figure 16 In , each color occurs in the red, green, and blue primary color spectral components. This model is based on a Cartesian coordinate system. The considered color subspace is Figure 16 The cube shown; in the figure, R, G, B (red, green, and blue) are located at 3 corners of the cube; cyan, magenta, and yellow are located at the other 3 corners, black is at the origin, and white is located away from the origin in the farthes...

specific Embodiment approach 3

[0057] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that in step 2, the grayscale image is subjected to image enhancement processing, and the specific process is as follows:

[0058] In general, the image acquired by the imaging system, that is, the original image, is often not directly used in the vision system due to various conditions and random interference. It is necessary to use the early stage of visual information processing to perform image preprocessing such as grayscale correction and noise filtering on the original image. Processing. For machine vision systems, the image preprocessing method used does not consider the cause of image degradation, but only selectively highlights the features of interest in the image and attenuates unnecessary features, so the output image after preprocessing There is no need to approach the original image. This type of image preprocessing method is collectively referred to as image enhancement. There a...

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Abstract

The invention discloses an image processing and mode recognition technology-based rice blast spore microscopic image recognition method, relates to the field of rice blast spore microscopic image recognition, and aims at solving the problems that the field detection is incorrect due to visual observation and judgment, a current experiment is huge in demand for samples and the traditional microscope spore counting needs plenty of time and is high in initial disaster situation finding difficulty. The method comprises the following steps: I, converting an original image into a grey-scale map; II, carrying out image enhancement processing on the grey-scale map; III, obtaining a binarization effect picture; IV, obtaining a denoising effect picture; I, obtaining a graph outline suspected to be rice blast spores; VI, recognizing a rice blast spore microscopic image, finally completing the recognition of the rice blast spores and counting the quantity of the rice blast spores. The image processing and mode recognition technology-based rice blast spore microscopic image recognition method is applied to the field of rice blast spore microscopic images.

Description

technical field [0001] The invention relates to a microscopic image recognition method of rice blast spores, in particular to a microscopic image recognition method of rice blast spores based on image processing and pattern recognition technology. Background technique [0002] The early detection of rice blast disease and the judgment of disease degree are the basis and key to the prediction and chemical control of rice blast disease. Due to the inconspicuous symptoms in the early stage of the disease, agricultural producers often lack the professional knowledge of crop disease diagnosis, so that the affected crops cannot be effectively controlled, the affected area of ​​crops expands rapidly, and the disease aggravates. The identification and grading detection of rice blast disease are mainly carried out in two aspects: field detection and laboratory detection. Field testing is usually done based on the naked eye observation of agricultural experts or agricultural producer...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06V10/758G06F18/285G06F18/211G06F18/253
Inventor 赵洪林童源马永奎张佳岩呼大明张中兆
Owner HARBIN INST OF TECH
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