CN115115609A - Image analysis method and system for plant leaf positive phenotypic characters - Google Patents
Image analysis method and system for plant leaf positive phenotypic characters Download PDFInfo
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Abstract
The invention discloses an image analysis method for the positive phenotypic character of plant leaves, which comprises the following steps: acquiring an image in which plant leaves and a standard colorimetric card are arranged side by side; identifying a standard color block on a standard color comparison card in the image; correcting the color of the image through the standard color blocks on the standard color comparison card; performing color analysis on plant leaves in the image based on the image after color correction to obtain phenotype characters related to the colors of the plant leaves; and acquiring a certain number of plant leaf front images, marking the images to be used as samples, and classifying the phenotype characters related to the plant leaves and the expression state categories according to the samples to obtain the phenotype characters related to the character categories. According to the invention, phenotypic characters of the front surface of the plant leaf are obtained through the standard colorimetric card carrying the standard color block, the image is subjected to unified color correction through identifying the standard color block on the standard colorimetric card, so that the background of the image is unified, the plant leaf has unified color, and the accuracy of color and category analysis is improved.
Description
Technical Field
The invention belongs to the technical field of plant new variety testing, and particularly relates to an image analysis method and system for plant leaf positive phenotypic characters.
Background
The new plant variety test is a process of performing cultivation identification tests or indoor analysis tests (DUS tests for short) on new plant varieties requiring protection, wherein the cultivation identification tests or the indoor analysis tests are specific, consistent and stable, and the DUS tests are generally performed according to DUS test guidelines of corresponding plants, the DUS test guidelines have more characters to be tested, the traditional test characters are mostly obtained by depending on field tests and manual records, the cost is more, and the workload is larger. In recent years, image analysis methods are gradually introduced into DUS tests, but abundant color and category information contained in a captured plant image is not utilized efficiently, and phenotypic characters analyzed based on the image also have the problems of inaccurate description of character expression states and large errors.
Disclosure of Invention
In view of the above, the invention provides an image analysis method and system for plant leaf obverse phenotype traits, which are used for solving the problem that errors of test traits related to colors and categories analyzed based on images are too large.
In a first aspect of the invention, an image analysis method for the plant leaf positive phenotype character is disclosed, which comprises the following steps:
acquiring an image in which plant leaves and a standard colorimetric card are arranged side by side;
identifying a standard color block on a standard color comparison card in the image;
correcting the color of the image through the standard color blocks on the standard color comparison card;
performing color analysis on plant leaves in the image based on the image after color correction to obtain phenotype characters related to the colors of the plant leaves;
and acquiring a certain number of plant leaf front images, marking the images to be used as samples, and classifying the phenotype characters related to the plant leaves and the expression state categories according to the samples to obtain the phenotype characters related to the character categories.
On the basis of the above technical solution, preferably, the standard color block includes: black, dark gray, light gray, white, red, green, blue and yellow, each standard color block shape is fixed and is provided with fixed RGB values. On the basis of the above technical solution, preferably, the identifying a standard color block on a standard color card in the image specifically includes:
carrying out background separation on the image, and reserving the color of a standard colorimetric card area;
and identifying the actual position and the average RGB value of each standard color block on the standard color comparison card.
On the basis of the above technical solution, preferably, the correcting the color of the image by the standard color block on the standard colorimetric card specifically includes:
comparing the average RGB value of each standard color block with the RGB value set by each standard color block, and calculating the difference value of the two values;
determining a correction value of each RGB component according to the difference value of the two RGB components;
and carrying out color correction on the RGB components of each pixel in the image according to the correction value of each RGB component.
On the basis of the above technical solution, preferably, the obtaining of the phenotypic trait related to the color of the plant leaf by performing color analysis on the plant leaf in the image based on the color-corrected image specifically includes:
phenotypic trait criteria in a DUS test guide corresponding to a plant species that are correlated with color of the outer surface of a plant leaf;
and performing color analysis according to the color correction result of the plant leaves in the image and the phenotypic character standard related to the color of the outer surface of the plant leaves in the DUS test guide corresponding to the plant species to obtain the phenotypic character related to the color of the front surface of the plant leaves.
On the basis of the above technical solution, preferably, the method further comprises:
obtaining a certain number of plant leaf front images, marking the images to be used as samples, classifying the plant leaves and phenotype characters related to expression state categories according to the samples to obtain the phenotype characters related to the character categories, and specifically comprising the following steps:
acquiring a certain number of leaf front images which are of the same type as the plant leaves and have the same growth stage as the plant leaves as samples;
determining the phenotypic characters related to the character types of the front surface of the plant leaves and the corresponding expression state types, and carrying out expression state type marking on the sample;
and based on the sample class marking result, carrying out expression state class classification on each phenotypic character of the plant leaf, which is related to the character class, in a template matching or machine learning mode to obtain the phenotypic character, which is related to the character class, of the front surface of the plant leaf.
On the basis of the technical scheme, preferably, the plant leaves are cucumber leaves;
phenotypic traits related to geometric size include: length and edge indentation depth;
phenotypic traits associated with color include: degree of greenness;
phenotypic traits associated with classes include: shape, tip shape.
In a second aspect of the present invention, an image analysis system for plant leaf phenotypic trait is disclosed, the system comprising:
an image acquisition module: the device is used for acquiring an image in which plant leaves and a standard colorimetric card are arranged side by side;
an image recognition module: the color block recognition module is used for recognizing a standard color block on a standard color comparison card in an image;
a color correction module: the color correction device is used for correcting the color of the image through the standard color blocks on the standard color comparison card;
a color analysis module: the color analysis device is used for carrying out color analysis on plant leaves in the image based on the image after color correction to obtain phenotype characters related to the colors of the plant leaves;
a category analysis module: the method is used for obtaining a certain number of plant leaf front images and marking the images as samples, and classifying the phenotype characters related to the plant leaves and the expression state categories according to the samples to obtain the phenotype characters related to the character categories.
In a third aspect of the present invention, an electronic device is disclosed, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor which are invoked to implement the method according to the first aspect of the invention.
In a fourth aspect of the invention, a computer-readable storage medium is disclosed, which stores computer instructions for causing a computer to implement the method according to the first aspect of the invention.
Compared with the prior art, the invention has the following beneficial effects:
1) the phenotypic characters of the front surface of the plant leaves are obtained through a standard color comparison card carrying standard color blocks, and the images are subjected to unified color correction through identifying the standard color blocks on the standard color comparison card, so that the backgrounds of the images are unified, the plant leaves have unified colors, and the accuracy of color analysis is improved;
2) according to the method, the phenotypic character related to the color of the plant leaf can be identified according to the plant leaf image, the color character of the plant leaf is prevented from being blurred, and the efficiency of obtaining the DUS test character is improved;
3) according to the method, a certain number of plant leaf front images are obtained and marked to serve as samples, the phenotype characters related to the plant leaves and the expression state categories are classified according to the samples, the phenotype characters related to the character categories are obtained through template matching or machine learning, and the accuracy of obtaining the DUS test characters is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an image analysis method of the plant leaf positive phenotypic trait of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1, the present invention provides a method for analyzing an image of a plant leaf phenotypic trait, the method comprising:
and S1, acquiring an image in which the plant leaves and the standard colorimetric card are arranged side by side.
The plant leaves and the standard colorimetric card are placed side by side, shooting background, light, object placement and camera parameters are adjusted, and then an image containing the plant leaves and the standard colorimetric card is shot by a camera.
The standard colorimetric card used in the invention is printed with a plurality of standard color blocks, for example, black, dark gray, light gray, white, red, green, blue and yellow standard color blocks are printed in sequence, each standard color block is set with a fixed RGB value during printing, and each standard color block has a fixed shape, such as a circle, a square and the like.
And S2, identifying the scale and the standard color block on the standard color comparison card in the image.
Step S2 specifically includes the following sub-steps:
and S21, performing background separation on the image, and reserving the color of the standard colorimetric card area.
And S22, identifying the actual position and the average RGB value of each standard color block on the standard color comparison card.
Each standard color block on the standard color comparison card has a fixed shape, for example, when the standard color block is circular, the actual position of each standard color block on the standard color comparison card can be determined according to the circular shape and the corresponding fixed RGB value, and then the average RGB value of each standard color block on the image is obtained.
And S3, correcting the colors of the image through the standard color blocks on the standard colorimetric card.
Step S3 specifically includes the following sub-steps:
s31, comparing the average RGB value of each standard color block with the RGB value set by each standard color block, and calculating the difference value of the two values;
s32, determining the correction value of each RGB component according to the difference value of the two RGB components;
s33, color correcting the RGB components of each pixel in the image according to the correction value of each RGB component.
According to the invention, phenotypic characters of the front surface of the plant leaf are obtained through the standard colorimetric card carrying the standard color block, the image is subjected to unified color correction through identifying the standard color block on the standard colorimetric card, so that the background of the image is unified, and the plant leaf has unified color, so that a good foundation is provided for subsequent color analysis, and the accuracy of color analysis is improved.
And S4, carrying out color analysis on the plant leaves in the image based on the image after color correction to obtain the phenotype characters related to the colors of the plant leaves.
Step S4 specifically includes the following sub-steps:
s41, acquiring phenotypic character standards related to the colors of the outer surfaces of the plant leaves in the DUS test guide corresponding to the plant species;
and S42, performing color analysis according to the color correction result of the plant leaves in the image and the phenotypic character standard related to the color of the outer surface of the plant leaves in the DUS test guide corresponding to the plant type to obtain the phenotypic character related to the color of the front surface of the plant leaves.
Specifically, color analysis can be performed through analysis means such as color picking, comparison, statistics, classification and the like, so as to obtain the phenotype characters related to color.
And S5, acquiring a certain number of plant leaf front images, marking the images to be used as samples, and classifying the phenotype characters related to the plant leaves and the expression state categories according to the samples to obtain the phenotype characters related to the character categories.
Step S5 specifically includes:
s51, acquiring a certain number of leaf front images which are of the same type as the plant leaves and have the same growth stage as the plant leaves as samples;
s52, determining the phenotype characters related to the character types of the front surface of the plant leaf and the corresponding expression state types, and marking the expression state types of the sample;
and S53, based on the sample class marking result, carrying out expression state class classification on each phenotype character of the plant leaf, which is related to the character class, in a template matching or machine learning mode, and obtaining the phenotype character of the front surface of the plant leaf, which is related to the character class.
According to the method, a certain number of plant leaf front images are obtained and marked to serve as samples, the phenotype characters related to the plant leaves and the expression state categories are classified according to the samples, the phenotype characters related to the character categories are obtained through template matching or machine learning, and the accuracy of obtaining the DUS test characters is improved.
The following further describes the embodiments of the present invention by taking plant leaves as cucumber leaves as an example.
The phenotypic traits related to the outer surface of cucumber strips in the NY/T2235-2012 plant variety specificity, consistency and stability test guide cucumber include: length, edge notch depth, green level, shape, tip shape.
Wherein the phenotypic traits related to color include: degree of greenness; phenotypic traits associated with a category include: shape, tip shape.
The cucumber leaves and the standard colorimetric card are placed side by side according to the step S1, an image is shot, then the standard color blocks in the image are identified through the step S2, color correction is carried out through the step S3, color analysis is carried out through the step S4, and the type analysis can be carried out through the step S5, so that the DUS test characters of the plant leaves are obtained.
According to the invention, multiple phenotype traits related to the color and the category of the plant leaf can be identified according to one plant leaf image, and the efficiency of obtaining the DUS test traits is improved;
corresponding to the embodiment of the method, the invention also discloses an image analysis system for the plant leaf positive phenotype character, which comprises the following steps:
an image acquisition module: the device is used for acquiring an image in which plant leaves and a standard colorimetric card are arranged side by side;
an image recognition module: the color block recognition module is used for recognizing a standard color block on a standard color comparison card in an image;
a color correction module: the color correction device is used for correcting the color of the image through the standard color blocks on the standard color comparison card;
a color analysis module: the color analysis module is used for carrying out color analysis on the plant leaves in the image based on the image after color correction to obtain the phenotype traits related to the colors of the plant leaves;
a category analysis module: the method is used for obtaining a certain number of plant leaf front images and marking the images as samples, and classifying the phenotype characters related to the plant leaves and the expression state categories according to the samples to obtain the phenotype characters related to the character categories.
The above system embodiments and method embodiments are in one-to-one correspondence, and please refer to the method embodiments for brief description of the system embodiments.
The present invention also discloses an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the memory stores program instructions executable by the processor which invokes the method of the invention as described above.
The invention also discloses a computer readable storage medium which stores computer instructions for causing the computer to implement all or part of the steps of the method of the embodiment of the invention. The storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a read-only memory ROM, a random access memory RAM, a magnetic disk, or an optical disk.
The above-described system embodiments are merely illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts shown as units may or may not be physical units, i.e. may be distributed over a plurality of network units. Without creative labor, a person skilled in the art can select some or all of the modules according to actual needs to achieve the purpose of the solution of the embodiment.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A method for image analysis of the phenotypic trait of plant leaf positivity, said method comprising:
acquiring an image in which plant leaves and a standard colorimetric card are arranged side by side;
identifying a standard color block on a standard color comparison card in the image;
correcting the color of the image through the standard color blocks on the standard color comparison card;
performing color analysis on plant leaves in the image based on the image after color correction to obtain phenotype characters related to the colors of the plant leaves;
and acquiring a certain number of plant leaf front images, marking the images to be used as samples, and classifying the phenotype characters related to the plant leaves and the expression state categories according to the samples to obtain the phenotype characters related to the character categories.
2. The method for image analysis of plant leaf phenotypic trait of claim 1, wherein the standard color block comprises: black, dark gray, light gray, white, red, green, blue and yellow, each standard color block shape is fixed and is provided with fixed RGB values.
3. The method for analyzing the image of the phenotypic trait of plant leaf obverse surface of claim 2, wherein the identifying of the standard color block on the standard color card in the image specifically comprises:
carrying out background separation on the image, and reserving the color of a standard colorimetric card area;
and identifying the actual position and the average RGB value of each standard color block on the standard color comparison card.
4. The method for analyzing the image of the phenotypic trait of plant leaf obverse surface as claimed in claim 3, wherein the correcting the color of the image by the standard color block on the standard color chart specifically comprises:
comparing the average RGB value of each standard color block with the fixed RGB value of each standard color block, and calculating the difference value of the two values;
determining a correction value of each RGB component according to the difference value of the two RGB components;
and carrying out color correction on the RGB components of each pixel in the image according to the correction value of each RGB component.
5. The method for image analysis of plant leaf phenotypic trait of claim 4, wherein the color analysis of plant leaf in the image based on the color corrected image to obtain the phenotypic trait related to the color of plant leaf comprises:
phenotypic trait criteria in a DUS test guide corresponding to a plant species that are correlated with color of the outer surface of a plant leaf;
and performing color analysis according to the color correction result of the plant leaves in the image and the phenotypic character standard related to the color of the outer surface of the plant leaves in the DUS test guide corresponding to the plant species to obtain the phenotypic character related to the color of the front surface of the plant leaves.
6. The method for analyzing images of phenotypic traits on plant leaf fronts as claimed in claim 1, wherein the method comprises the steps of obtaining a certain number of images of plant leaf fronts, marking the images to be used as samples, classifying the phenotypic traits of the plant leaves related to the expression state category according to the samples, and obtaining the phenotypic traits related to the trait category, specifically comprising:
acquiring a certain number of leaf front images which are of the same type as the plant leaves and have the same growth stage as the plant leaves as samples;
determining the phenotypic characters related to the character types of the front surface of the plant leaves and the corresponding expression state types, and carrying out expression state type marking on the sample;
and based on the sample class marking result, carrying out expression state class classification on each phenotypic character of the plant leaf, which is related to the character class, in a template matching or machine learning mode to obtain the phenotypic character, which is related to the character class, of the front surface of the plant leaf.
7. The method for image analysis of plant leaf phenotypic trait according to claim 6, wherein the plant leaf is cucumber leaf;
phenotypic traits associated with color include: degree of greenness;
phenotypic traits associated with classes include: shape, tip shape.
8. An image analysis system for the phenotypic traits of plant leaf fronts, said system comprising:
an image acquisition module: the device is used for acquiring an image in which plant leaves and a standard colorimetric card are arranged side by side;
an image recognition module: the color block recognition module is used for recognizing a standard color block on a standard color comparison card in an image;
a color correction module: the color correction device is used for correcting the color of the image through the standard color blocks on the standard color comparison card;
a color analysis module: the color analysis device is used for carrying out color analysis on plant leaves in the image based on the image after color correction to obtain phenotype characters related to the colors of the plant leaves;
a category analysis module: the method is used for obtaining a certain number of plant leaf front images and marking the images as samples, and classifying the phenotype characters related to the plant leaves and the expression state categories according to the samples to obtain the phenotype characters related to the character categories.
9. An electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to implement the method of any one of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a computer to implement the method of any one of claims 1 to 7.
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| CN202210854864.4A CN115115609B (en) | 2022-07-18 | Image analysis method and system for plant leaf positive phenotype character |
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| CN202210854864.4A CN115115609B (en) | 2022-07-18 | Image analysis method and system for plant leaf positive phenotype character |
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| CN115115609B CN115115609B (en) | 2024-07-16 |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117372790A (en) * | 2023-12-08 | 2024-01-09 | 浙江托普云农科技股份有限公司 | Plant leaf shape classification method, system and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101398898A (en) * | 2008-10-20 | 2009-04-01 | 中国科学院合肥物质科学研究院 | Plant leaf identification method based on manifold learning |
| CN104132897A (en) * | 2014-08-16 | 2014-11-05 | 西北农林科技大学 | Measuring method and device for nitrogen content of plant leaf on basis of handheld equipment |
| CN110458882A (en) * | 2019-08-17 | 2019-11-15 | 陈�峰 | A kind of fruit phenotype test method based on computer vision |
| CN111508037A (en) * | 2020-04-14 | 2020-08-07 | 宿迁学院 | Color grade evaluation method for color-leafed succulent plant leaves |
| CN112507890A (en) * | 2020-12-14 | 2021-03-16 | 南京林业大学 | Bamboo leaf sheet classification and identification method based on SVM classifier |
| CN113132693A (en) * | 2019-12-31 | 2021-07-16 | 长沙云知检信息科技有限公司 | Color correction method |
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101398898A (en) * | 2008-10-20 | 2009-04-01 | 中国科学院合肥物质科学研究院 | Plant leaf identification method based on manifold learning |
| CN104132897A (en) * | 2014-08-16 | 2014-11-05 | 西北农林科技大学 | Measuring method and device for nitrogen content of plant leaf on basis of handheld equipment |
| CN110458882A (en) * | 2019-08-17 | 2019-11-15 | 陈�峰 | A kind of fruit phenotype test method based on computer vision |
| CN113132693A (en) * | 2019-12-31 | 2021-07-16 | 长沙云知检信息科技有限公司 | Color correction method |
| CN111508037A (en) * | 2020-04-14 | 2020-08-07 | 宿迁学院 | Color grade evaluation method for color-leafed succulent plant leaves |
| CN112507890A (en) * | 2020-12-14 | 2021-03-16 | 南京林业大学 | Bamboo leaf sheet classification and identification method based on SVM classifier |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117372790A (en) * | 2023-12-08 | 2024-01-09 | 浙江托普云农科技股份有限公司 | Plant leaf shape classification method, system and device |
| CN117372790B (en) * | 2023-12-08 | 2024-03-08 | 浙江托普云农科技股份有限公司 | Plant leaf shape classification method, system and device |
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