CN109187552B - Wheat scab damage grade determination method based on cloud model - Google Patents
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- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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Abstract
The invention belongs to the technical field of crop disease monitoring, and particularly relates to a wheat scab damage grade determination method based on a cloud model, which comprises the steps of extracting a disease area, constructing color characteristics, generating a wheat scab color characteristic cloud model, establishing a comprehensive evaluation model and analyzing the evaluation model; the relation between the color change of a disease area and the grade of the wheat scab rate in the gradual lesion process of the wheat scab is utilized, the color feature cloud of the disease area is obtained by combining three digital features of the cloud model, and the cloud model is utilized to realize non-rigid division of the grade of the wheat scab so as to realize comprehensive evaluation.
Description
Technical Field
The invention belongs to the technical field of crop disease monitoring, and particularly relates to a wheat scab disease grade determination method based on a cloud model.
Background
The rapid development of computer technology improves the speed and efficiency of automatic detection and identification of plant diseases. Such as hyperspectral techniques, image processing techniques, etc. Like jade, glittering and translucent and the like use a hyperspectral imager to analyze spectrometer information reflectivity characteristics of healthy wheat grain images and gibberellic disease wheat grain images respectively. Eliminating spectral image noise through a series of spectral preprocessing methods, then identifying texture features, morphological parameters and average gray level feature wave bands, and finally establishing an identification model. Shaohqing, Zhang nan and the like perform image denoising on the diseases of the leaves of wheat, and then realize shape feature extraction on the disease parts, so that the method is an improvement on image processing and simultaneously perfects a feature database of the diseases of the wheat.
According to the technical standard of wheat scab prediction, the disease degree of the incidence degree of wheat scab is divided into 4 grades, but two objects with extremely close conceptual values are classified into different grades due to excessive hard division. For example: the detected ear disease rate is 25.8%, the single wheat plant belongs to grade 2 according to the classification standard of the standard, but the value is extremely close to 25%, and the classification is not reasonable; at present, the scab of wheat ears is removed from living bodies in the study, some wheat ears are even stripped to form wheat ear grains, and the ear disease rate is counted through a series of identification detection methods.
Disclosure of Invention
On the basis of cloud model knowledge representation, the color change from no morbidity to initial morbidity of single living wheat and the constructed characteristic parameters are added to the grading standard given by the standard to serve as another influence factor except the morbidity to generate a color characteristic cloud model, so that more reasonable evaluation on grading is realized.
The invention follows the growth process of living wheat, counts the color change degree and constructs characteristic parameters from no disease occurrence to different disease degrees, and provides a wheat scab damage grade determination method based on a cloud model as an index for identifying and evaluating the wheat disease degree. Compared with the detection of the extirpated wheat, the method is more convincing.
The invention is realized by the following technical scheme: a wheat scab damage grade judging method based on a cloud model comprises the following steps:
s1, extraction of disease area
S11, collecting an initial image of the wheat ear in the field, and denoising to obtain a primary processing image;
s12, performing image segmentation on the a component of the primary processing image in Lab space by adopting an Otsu method to obtain an a component image;
s13, obtaining an ear edge image by adopting an edge recognition algorithm on the primary processing image;
s14, performing bit operation on the gray values of the a component image and the ear edge image respectively, setting pixel points with the same gray values of the a component image and the ear edge image as 0 (black), setting pixel points with different gray values as 255 (white), and marking the pixel points with the gray values of 255 as disease pixel points to obtain a disease pixel point distribution graph;
s15, extracting initial pixel points at positions corresponding to the disease pixel point distribution graph in the initial image by utilizing the corresponding of the disease pixel point distribution graph and the initial image to obtain an initial pixel point distribution graph;
s16, marking red at the corresponding position in the initial image by using the initial pixel point distribution map to obtain a disease marking image;
s2 structure of color feature
S21, taking the color of the initial pixel point obtained in the step S15 as a basis for counting the color moment of the wheat scab incidence, and counting the color moment of the wheat scab incidence of the single plant by using a calculation formula of first order distance, second order distance and third order moment, wherein the calculation formula of the first order distance, the second order distance and the third order moment is as follows:
a formula for calculating the step distance:
of two stepsCalculating the formula:
the formula for calculating the third-order distance:
s22, constructing a plurality of intermediate color parameters according to the combination relationship among the color components in the color spaces HSV, RGB and Lab;
s23, recording the color moments and the intermediate color parameters as evaluation color parameters, and calculating the correlation coefficient of each evaluation color parameter by using a correlation function corr ();
s24, judging the significance level of the correlation coefficient of each evaluation color parameter;
table 1 evaluation of correlation coefficients of color parameters
S25, selecting an evaluation color parameter with a correlation coefficient significance level reaching 0.01 as a correlation color parameter, and selecting four correlation color parameters of h-mean, S-mean, h-std and S-std as data sources of the wheat scab color feature cloud model;
s3, generating a wheat gibberella color feature cloud model which is described by three digital features of expected Ex, entropy En and super-entropy He
S31, drawing frequency distribution curves corresponding to different wheat ear disease rate grades by using the related color parameters;
s32, solving peak points in each frequency distribution curve, and taking x values corresponding to the obtained peak points as expected Ex of the cloud model to be generated;
s33, calculating entropy En and super-entropy He according to formulas;
s4, establishing a comprehensive evaluation model
The cloud model is composed of cloud droplets, the cloud droplets contribute to concepts differently, the contribution value of all the cloud droplets on an infinite interval to the concepts is 1, and the contribution value of each cloud droplet to the concepts needs to be considered in the interval of the partition; according to the concept of 3En criterion, taking a partition space of X in the cloud model
Calculate the average contribution of cloud droplets on this interval to concept A
Mean contribution of
The formula of (1) is as follows:
after analysis, the contribution of elements in the domain of discourse [ Ex-3En, Ex +3En ] to the concept reaches 99.74%, and the elements distributed outside the domain of discourse [ Ex-3En, Ex +3En ] can be basically ignored due to the excessively low contribution value;
s41, calculating the values of Ex-0.67En and Ex +0.67En according to the digital characteristics corresponding to the relevant color parameters;
s42, respectively constructing an evaluation model according to the grades of different wheat ear diseases rates by using intervals [ Ex-0.67En, Ex +0.67En ];
s5 analysis of evaluation model
S51, evaluating the wheat ear disease rate of a certain grade, and judging an interval [ Ex-0.67En, Ex +0.67En ] corresponding to each relevant color parameter in the grade as a relevant color parameter evaluation interval, wherein if the relevant color parameter evaluation intervals corresponding to all relevant parameters in the grade are intersected at the same time, the grade of the wheat ear disease rate close to a higher grade in a wireless mode is defined in the current grade; otherwise, it may not be classified into the current level.
Wherein, the proportional value of R, G, B in the RGB color space represents chromaticity coordinates (R, G, B), and satisfies R + G + B =1, and the calculation formulas thereof are respectively: r = R/(R + G + B); g = G/(R + G + B); b = B/(R + G + B).
The denoising method in the step S11 is median filtering denoising; the frequency distribution curve is plotted using the hist () function in the python library.
Wherein the grade of the wheat ear disease rate comprises: the first-level ear disease rate is 20-25%, the second-level ear disease rate is 45-50%, and the third-level ear disease rate is 70-75%.
Wherein the peak point is a local maximum of the frequency distribution curve.
Compared with the prior art, the invention has the following advantages: according to the method, the comprehensive grading evaluation standard is utilized, the incidence situation of wheat head gibberellic disease caused by picking wheat head grains without using living bodies can be tracked, the limitation of hard grading can be effectively relieved, the standard can be provided for the evaluation of the wheat head gibberellic disease, and reasonable guidance is provided for the water application and the pesticide application of the wheat head gibberellic disease.
Drawings
FIG. 1 is a histogram of the a and b components of the Lab color space;
FIG. 2 is an a-component image;
FIG. 3 is an image of an ear edge;
FIG. 4 is a distribution diagram of diseased pixels;
FIG. 5 is a distribution diagram of initial pixel points;
FIG. 6 is a disease signature image;
FIGS. 7-10 are frequency distribution curves of four relevant color parameters for first-order ear-of-disease rate;
FIGS. 11-14 are frequency distribution curves of four relevant color parameters for secondary ear rate;
FIGS. 15-18 are four relevant color parameter frequency distribution curves for tertiary ear disease rate;
FIGS. 19-22 are cloud models formed by four relevant color parameters for primary ear rate;
FIGS. 23-26 are cloud models formed by four relevant color parameters for secondary ear rate;
FIGS. 27-30 are cloud models formed by four relevant color parameters for the tertiary prevalence;
note that the initial pixel points and the original image of the disease marker map in fig. 4 are color images.
Detailed Description
The following describes in detail specific embodiments of the present invention. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
A wheat scab damage grade judging method based on a cloud model comprises the following steps:
s1, extraction of disease area
S11, collecting an initial image of the wheat ear in the field, and denoising to obtain a primary processing image;
s12, performing image segmentation on the a component of the primary processing image in Lab space by adopting an Otsu method to obtain an a component image as shown in FIG. 2;
compared with other color spaces, the Lab color space has the widest color representation range and is not influenced by illumination, the component a in the Lab color space has obvious peaks and troughs when a color histogram is drawn, and compared with the component b, the outline of an object can be seen to be clearer, and the detail is shown in FIG. 1; the target is easier to be divided by using the color, so the a component in the Lab color space realizes image division in the invention;
s13, obtaining an ear edge image by adopting an edge recognition algorithm on the primary processing image as shown in figure 3;
s14, performing bit operation on the gray values of the a-component image and the ear edge image, setting the pixel points with the same gray value as the gray value of the a-component image and the ear edge image as 0 (black), setting the pixel points with different gray values as 255 (white), and then marking the pixel points with the gray value of 255 as disease pixel points to obtain a disease pixel point distribution map as shown in fig. 4;
s15, extracting initial pixel points at positions corresponding to the disease pixel point distribution graph in the initial image by utilizing the corresponding relationship between the disease pixel point distribution graph and the initial image, and obtaining the initial pixel point distribution graph as shown in FIG. 5;
s16, marking red at the corresponding position in the initial image by using the initial pixel point distribution map to obtain a disease marking image as shown in FIG. 6;
s2 structure of color feature
S21, taking the color of the initial pixel point obtained in the step S15 as a basis for counting the color moment of the wheat scab incidence, and counting the color moment of the wheat scab incidence of the single plant by using a calculation formula of first-order distance, second-order distance and third-order moment;
s22, constructing a plurality of intermediate color parameters according to the combination relationship among the color components in the color spaces HSV, RGB and Lab; the proportional values of R, G, B in the RGB color space represent chromaticity coordinates (R, G, B), and satisfy R + G + B =1, and the calculation formulas thereof are: r = R/(R + G + B); g = G/(R + G + B); b = B/(R + G + B);
s23, recording the color moments and the intermediate color parameters as evaluation color parameters, and calculating the correlation coefficient of each evaluation color parameter by using a correlation function corr ();
s24, judging the significance level of the correlation coefficient of each evaluation color parameter, wherein the specific content is shown in Table 1;
through analysis of the correlation coefficients of the evaluation color parameters, it can be known that the correlation between only 21 color parameters and the ear disease rate of wheat reaches more than 50%, and 14 color parameters are negatively correlated with the ear disease rate; it is generally believed that: the correlation coefficient is no correlation between 0 and 0.09, weak correlation between 0.1 and 0.3, medium correlation between 0.3 and 0.5, and strong correlation between 0.5 and 1.0. However, the correlation relationship cannot be directly explained because the magnitude of the correlation coefficient is unstable; because the correlation coefficient is related to the nature of the data, the correlation coefficient is variable, and the calculated correlation coefficient may change along with the increase and decrease of the sample size, the strength of the judgment needs to be determined according to not only the correlation coefficient but also the significance; the significance level is the probability that the estimated overall parameter falls in a certain interval and can make errors, and is expressed by alpha; the smaller alpha is, the smaller the probability of indicating that the overall parameter falls in the wrong interval is, the more easily correct the estimation of the correlation is indicated, and the larger alpha is, the greater the probability of indicating that the overall parameter falls in the wrong interval is, namely, the more easily the estimation of the correlation is wrong; typically, α is considered to be less than 0.1. In table 1, the left of the correlation coefficient represents that the significance level reached 0.01, indicating that the significance level reached 0.05.
S25, selecting an evaluation color parameter with a correlation coefficient significance level reaching 0.01 as a correlation color parameter, and selecting four correlation color parameters of h-mean, S-mean, h-std and S-std as data sources of the wheat scab color feature cloud model;
s3, generating a wheat gibberella color feature cloud model which is described by three digital features of expected Ex, entropy En and super-entropy He
S31, drawing frequency distribution curves corresponding to different wheat ear disease rate levels by using data sources corresponding to four relevant color parameters of h-mean, S-mean, h-std and S-std;
the grade of the wheat ear disease rate comprises: the first-level ear disease rate is 20-25%, the second-level ear disease rate is 45-50%, and the third-level ear disease rate is 70-75%;
the frequency distribution curve of four related color parameters of the first-level ear disease rate is shown in figures 7-10;
the frequency distribution curves of four related color parameters of the secondary ear disease rate are shown in FIGS. 11-14;
the frequency distribution curves of four related color parameters of the third-level ear disease rate are shown in the figure 15-18;
s32, solving peak points in each frequency distribution curve, and taking x values corresponding to the obtained peak points as expected Ex of the cloud model to be generated;
s33, calculating entropy En and super-entropy He according to formulas;
TABLE 2 three digital characteristic cloud parameters corresponding to the relevant color parameters at different ear-disease rate levels
The cloud model formed by the four relevant color parameters of the primary ear disease rate is shown in figures 19-22;
the cloud model formed by the four relevant color parameters of the secondary ear disease rate is shown in figures 23-26;
the cloud model formed by the four relevant color parameters of the third-level ear disease rate is shown in the figures 27-30;
s4, establishing a comprehensive evaluation model
S41, calculating the values of Ex-0.67En and Ex +0.67En according to the digital characteristics corresponding to the relevant color parameters;
TABLE 3 associated color parameters correspond to Ex-0.67En and Ex +0.67En values
S42, respectively constructing an evaluation model according to the grades of different wheat ear diseases rates by using intervals [ Ex-0.67En, Ex +0.67En ];
obtaining the evaluation models corresponding to the three grades of the wheat spike disease rate as follows:
first-level ear rate evaluation model
Secondary disease spike rate evaluation model
Three-stage ear disease rate evaluation model
S5 analysis of evaluation model
S51, according to the above models for evaluating the ear disease rate with different grades, it can be seen that:
(1) when the sick spike rate value is 20% -25%:
: the value of the color feature h-mean lies in the interval
；
: the values of the color features s-mean lie in intervals
；
: the value of the color feature h-std is in the interval
；
: the value of the color feature s-std lies in the interval
；
If the condition is met, the grade of the wheat ear infinitely approaching to the second grade can be determined to be in the first grade range; if the condition is not met, the elements cannot be classified into one level, because the membership degree of the elements which do not meet the condition to the concept value of the first level is lower;
(2) when the ear disease rate value is 45% -50%:
: the value of the color feature h-mean lies in the interval
；
: the value of the color feature s-mean lies in
；
: the value of the color feature h-std is located at
；
: the value of the color feature s-std is located at
；
If the condition is satisfied, the grade of the ear infinitely close to the third grade can be determined to be in the second grade range. If the conditions are not met, the method cannot be divided into two stages.
(3) When the sick spike rate value is 70% -75%:
: the value of the color feature h-mean lies in the interval
；
: the value of the color feature s-mean lies in
；
: the value of the color feature h-std is located at
；
: the value of the color feature s-std is located at
；
If the condition is satisfied, the grade of the ear of the plant infinitely close to the fourth grade (the fourth grade ear disease rate is not specified, and the fourth grade ear disease rate is specified to be > 80%) can be determined to be within the range of the third grade. If the conditions are not met, the method cannot be divided into three levels.
The method can effectively relieve the limitation of hard division according to the evaluation standard of comprehensive grading, and provides reasonable guidance for water application and pesticide application of wheat scab.
The preferred embodiments of the present invention have been described in detail, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (7)
1. A wheat scab damage grade judging method based on a cloud model is characterized by comprising the following steps:
s1, extraction of disease area
S11, collecting an initial image of the wheat ear in the field, and denoising to obtain a primary processing image;
s12, performing image segmentation on the a component of the primary processing image in Lab space by adopting an Otsu method to obtain an a component image;
s13, obtaining an ear edge image by adopting an edge recognition algorithm on the primary processing image;
s14, performing bit operation on the gray values of the a component image and the ear edge image respectively, setting the pixel points with the same gray values of the a component image and the ear edge image as 0, setting the pixel points with different gray values as 255, and then marking the pixel points with the gray values of 255 as disease pixel points to obtain a disease pixel point distribution graph;
s15, extracting initial pixel points at positions corresponding to the disease pixel point distribution graph in the initial image by utilizing the corresponding of the disease pixel point distribution graph and the initial image to obtain an initial pixel point distribution graph;
s16, marking red at the corresponding position in the initial image by using the initial pixel point distribution map to obtain a disease marking image;
s2 structure of color feature
S21, taking the color of the initial pixel point obtained in the step S15 as a basis for counting the color moment of the wheat scab incidence, and counting the color moment of the wheat scab incidence of the single plant by using a calculation formula of first moment, second moment and third moment, wherein the calculation formula of the first moment, the second moment and the third moment is as follows:
calculation formula of first moment:
calculation formula of second moment:
the formula for calculating the third moment:
s22, constructing a plurality of intermediate color parameters according to the combination relationship among the color components in the color spaces HSV, RGB and Lab;
s23, recording the color moments and the intermediate color parameters as evaluation color parameters, and calculating the correlation coefficient of each evaluation color parameter by using a correlation function corr ();
s24, judging the significance level of the correlation coefficient of each evaluation color parameter;
s25, selecting an evaluation color parameter with a correlation coefficient significance level reaching 0.01 as a correlation color parameter, and taking the correlation color parameter as a data source of a wheat scab color feature cloud model;
s3, generating a wheat gibberella color feature cloud model which is described by three digital features of expected Ex, entropy En and super-entropy He
S31, drawing frequency distribution curves corresponding to different wheat ear disease rate grades by using the related color parameters;
s32, solving peak points in each frequency distribution curve, and taking x values corresponding to the obtained peak points as expected Ex of the cloud model to be generated;
s33, calculating entropy En and super-entropy He according to formulas;
s4, establishing a comprehensive evaluation model
S41, calculating the values of Ex-0.67En and Ex +0.67En according to the digital characteristics corresponding to the relevant color parameters;
s42, respectively constructing an evaluation model according to the grades of different wheat ear diseases rates by using intervals [ Ex-0.67En, Ex +0.67En ];
s5 analysis of evaluation model
S51, evaluating the ear disease rate of wheat at a certain grade, and judging an interval [ Ex-0.67En, Ex +0.67En ] corresponding to each relevant color parameter in the grade as a relevant color parameter evaluation interval, wherein if the relevant color parameter evaluation intervals corresponding to all relevant parameters in the grade are intersected at the same time, the grade infinitely approaching the ear disease rate of wheat at a higher grade is defined in the current grade; otherwise, it may not be classified into the current level.
2. The method for determining the wheat scab damage level based on the cloud model as claimed in claim 1, wherein the denoising method in the step S11 is median filtering denoising.
3. The method for determining wheat scab damage level according to claim 1, wherein said R, G, B scale values in RGB color space represent chromaticity coordinates (R, G, B) satisfying R + G + B =1, and the calculation formulas are: r = R/(R + G + B); g = G/(R + G + B); b = B/(R + G + B).
4. The method for judging the wheat scab damage level based on the cloud model as claimed in claim 1, wherein the evaluation color parameters with the significance level of the correlation coefficient reaching 0.01 are h-mean, s-mean, h-std and s-std.
5. The method for determining the level of wheat scab damage based on the cloud model as claimed in claim 1, wherein said frequency distribution curve is plotted using a hist () function in a python library.
6. The method for judging the grade of wheat scab damage based on the cloud model as claimed in claim 1, wherein the grade of the wheat scab damage rate comprises: the first-level ear disease rate is 20-25%, the second-level ear disease rate is 45-50%, and the third-level ear disease rate is 70-75%.
7. The method for determining the level of wheat scab damage based on the cloud model as claimed in claim 1, wherein said peak point is a local maximum of a frequency distribution curve.
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| CN110346312B (en) * | 2019-07-19 | 2021-08-10 | 安徽大学 | Winter wheat head gibberellic disease identification method based on Fisher linear discrimination and support vector machine technology |
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