Liberobacter asiaticum detection method based on visible light images

A citrus huanglongbing and detection method technology, which is applied in image analysis, image data processing, instruments, etc., can solve the problems of long cycle, high detection cost, and difficult popularization and application in grassroots production

Active Publication Date: 2014-08-27
SOUTH CHINA AGRI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although there are some diagnostic methods for citrus HLB, such as the most reliable PCR detection method, it is difficult to popularize and apply them in grassroots production
The main reason is that the PCR detection process is cumbersome, the cycle is long, the detection cost is high, and it needs to be carried out in a specific laboratory environmen

Method used

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  • Liberobacter asiaticum detection method based on visible light images
  • Liberobacter asiaticum detection method based on visible light images
  • Liberobacter asiaticum detection method based on visible light images

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Experimental program
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Embodiment 1

[0042] Present embodiment uses SLR camera to carry out the image acquisition of citrus leaf, and the image that gathers is color image, is connected with computer by data line, and image input computer (or sends to remote computer through network, carries out remote diagnosis). By analyzing the texture feature and color feature of the leaf image, extracting the relevant feature data, judging the diseased condition of the leaf plant (on Matlab software), and giving the final recognition result.

[0043] Using the Matlab software platform and the collected citrus leaf image information, the visible light image processing algorithm for the detection of citrus Huanglongbing disease is realized. First, the texture feature and color feature theory involved in this embodiment are specifically explained as follows.

[0044] Texture features: Since the texture is formed by the repeated occurrence of grayscale distribution in the spatial position, there will be a certain grayscale relati...

Embodiment 2

[0057] Present embodiment except following feature other structures are with embodiment 1:

[0058] Because the color features of HLB leaves are difficult to distinguish, sometimes the color features under HSV cannot achieve a good resolution effect. In this embodiment, the color moment of HSI space is also added when judging whether the leaves are healthy or yellow. The steps are: using the HSI color space of the color image, calculating the first-order moment, the second-order moment and the third-order moment in the color space, and using the above three values ​​as HSI detection feature values. Finally, the grayscale detection feature value, HSV detection feature value, and HSI detection feature value are all input into the BP neural network for training and learning, and the optimal BP neural network model is obtained. Realize the identification of whether the leaves are yellow and healthy.

Embodiment 3

[0060] Present embodiment except following feature other structures are with embodiment 1:

[0061] In actual operation, although leaf yellowing is unhealthy, it may not be caused by infection of Huanglongbing. At the same time, the types of Huanglongbing are divided into three types: uniform yellowing, mosaic, and mottled. If it can be further classified , which is more practical for agricultural testing. Therefore, the present embodiment is further supplemented on the basis of embodiment 2, such as figure 1 As shown, the identification steps are as follows.

[0062] 1. Training stage

[0063] (1) Select images with uniform yellowing of the known species of Huanglongbing, mosaic images of known species of Huanglongbing, mottled images of known species of Huanglongbing, and yellowing images of non-Huanglongbing, and extract the value of each image Grayscale detection eigenvalues, HSV detection eigenvalues, HSI detection eigenvalues.

[0064] (2) Select the images with unif...

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Abstract

The invention discloses a liberobacter asiaticum detection method based on visible light images. In the training stage, a large number of images of citrus leaves infected with liberobacter asiaticum and images of normal leaves are collected, the feature values of the texture feature and the color feature are extracted, the above feature values and the feature values of normal leaves are trained and learned through a BP neural network, and the optimal BP neural network model is obtained; in the identification stage, the features of the leaf images to be identified are extracted and input into the optimal BP neural network model, and then whether a citrus tree is healthy is judged. By means of the liberobacter asiaticum detection method based on visible light images, the type of the liberobacter asiaticum and whether the citrus tree is not yellowed due to liberobacter asiaticum are further judged, early, accurate and non-destructive diagnosis can be carried out on the liberobacter asiaticum, and the liberobacter asiaticum detection method based on visible light images has the advantage of being high in detection accuracy.

Description

technical field [0001] The invention relates to the field of image processing research, in particular to a method for detecting citrus huanglongbing based on visible light images. Background technique [0002] Citrus is one of the fruits with the largest output in the world, and it is also one of the fruits with the largest planting scale in southern my country. Citrus Huanglongbing (HLB) is a devastating disease in citrus production, which is harmful and spreads rapidly. Once citrus is infected with HLB, the light one will seriously affect the yield and quality, and the severe one will cause the citrus tree to die. So far, there is no effective drug treatment. [0003] In order to prevent the spread of HLB pathogens, the primary measure adopted in citrus production is to root out diseased plants. In my country, more than one million acres of citrus orchards have been destroyed due to HLB. Therefore, HLB is considered to be a cancer of citrus, causing huge economic losses...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/46G06N3/02
Inventor 邓小玲刘佳凯邢夏琼梅慧兰
Owner SOUTH CHINA AGRI UNIV
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