CN117808900B - Method and device for classifying color development intensity of maize anthocyanin - Google Patents
Method and device for classifying color development intensity of maize anthocyanin Download PDFInfo
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- 239000004410 anthocyanin Substances 0.000 title claims abstract description 212
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- 235000010208 anthocyanin Nutrition 0.000 title claims abstract description 212
- 150000004636 anthocyanins Chemical class 0.000 title claims abstract description 212
- 235000002017 Zea mays subsp mays Nutrition 0.000 title claims abstract description 173
- 238000011161 development Methods 0.000 title claims abstract description 173
- 235000016383 Zea mays subsp huehuetenangensis Nutrition 0.000 title claims abstract description 160
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Abstract
The invention discloses a method and a device for classifying the anthocyanin color development intensity of a maize filament, wherein a sample set formed by a maize filament image is divided into an analysis sample and a verification sample, a relation model of the anthocyanin color development intensity of the maize filament and the CIELAB value is established by utilizing the visual classification and the measurement of the anthocyanin color development intensity of the analysis sample, and then the relation model is verified and optimized by utilizing the verification sample, so that the relation model has higher detection accuracy, and the anthocyanin color development intensity classification of the maize filament to be detected is realized by utilizing a relation table of each anthocyanin color development intensity corresponding to the relation model and the CIELAB value.
Description
Technical Field
The invention relates to the technical field of corn variety identification, in particular to a method, a device, equipment and a storage medium for classifying the color development intensity of maize anthocyanin.
Background
The color development intensity of anthocyanin refers to the intensity or shade of color exhibited under specific test conditions. In practical application, the detection of the color development intensity of anthocyanin is mainly used for evaluating and comparing the content of anthocyanin in different samples. Anthocyanin is a natural pigment which is widely used in plants and has various biological activities such as antioxidation, anti-inflammatory and the like, so that the detection of the color development intensity of anthocyanin has a certain practical significance for plant variety identification and screening. The anthocyanin content in the maize filaments is one of important indexes for measuring the maize quality, and the maize quality can be rapidly and accurately evaluated by detecting the color development intensity of the anthocyanin, so that references are provided for growers, processing enterprises and consumers.
The color development intensity detection of the existing maize silk anthocyanin mainly comprises a spectrophotometry method and a high performance liquid chromatography method; the spectrophotometry is used for carrying out spectrum scanning on the corn silk sample extracting solution in a visible light region to find out the maximum absorption wavelength of anthocyanin, measuring absorbance at the wavelength, and calculating the content of anthocyanin according to the absorbance and a standard curve; the high performance liquid chromatography is used for extracting anthocyanin in the corn silk sample, separating and detecting by using the high performance liquid chromatography, and comparing the retention time and peak area of different anthocyanin, so that the anthocyanin content can be qualitatively and quantitatively analyzed. Therefore, the existing detection scheme for the color development intensity of the maize anthocyanin has the defects of complex calculation, complex operation and the like.
Therefore, how to reduce the calculation complexity and the operation steps and provide the detection convenience while ensuring the detection precision of the color development intensity of the maize anthocyanin is a technical problem to be solved urgently.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for classifying the color development intensity of maize silk anthocyanin, which aim to solve the problems that the existing color development intensity detection scheme of maize silk anthocyanin has the defects of complex calculation, complex operation and the like.
In order to achieve the above purpose, the invention provides a method for classifying the color development intensity of maize silk anthocyanin, which comprises the following steps:
obtaining a maize filament sample set, and dividing the maize filament sample set into an analysis sample and a verification sample; the corn silk sample set comprises a plurality of corn silk images;
Placing the analysis sample and the verification sample into an image acquisition chamber, acquiring a color measurement image in the image acquisition chamber by using an image acquisition device, and determining CIELAB values of the analysis sample and the verification sample based on the color measurement image;
visual classification is carried out by utilizing the CIELAB value and anthocyanin color development intensity of the analysis sample, and a relation model of the anthocyanin color development intensity and the CIELAB value of the maize filament is established;
verifying the relation model by visual classification of CIELAB values and anthocyanin color development intensity of the verification samples, and optimizing the relation model according to verification results;
Based on the optimized relation model, a comparison relation table of the anthocyanin color development intensity of the maize filaments and CIELAB values is generated, and the comparison relation table is utilized to classify the anthocyanin color development intensity of the maize filaments to be detected.
Optionally, the step of obtaining a maize filament sample set, dividing the maize filament sample set into an analysis sample and a verification sample specifically includes:
Obtaining a maize filament sample set; the corn silk sample set comprises a plurality of corn silk images with anthocyanin color development intensities which are visually classified and uniformly distributed in 9 anthocyanin color development intensity levels;
dividing a maize filament image of each anthocyanin color development intensity level in the maize filament sample set into an analysis sample image and a verification sample image according to a preset proportion;
Taking the analysis sample image of each anthocyanin color development intensity level as an analysis sample of the corn silk sample set, and taking the verification sample image of each anthocyanin color development intensity level as a verification sample of the corn silk sample set.
Optionally, the step of placing the analysis sample and the verification sample into an image acquisition chamber, acquiring a color measurement image in the image acquisition chamber by using an image acquisition device, and determining CIELAB values of the analysis sample and the verification sample based on the color measurement image specifically includes:
respectively placing the analysis sample and the verification sample into an image acquisition chamber together with a standard color card, and acquiring a first color measurement image and a second color measurement image which comprise two opposite positions of the analysis sample and the standard color card or the verification sample and the standard color card in the image acquisition chamber by using an image acquisition device;
Establishing a color value conversion matrix according to a first RGB value of each color block of a standard color card and a CIEXYZ value of the color block in the first color measurement image and the second color measurement image;
And determining the CIEXYZ value of the analysis sample or the verification sample according to the second RGB value of the analysis sample or the verification sample in the first color measurement image and the second color measurement image and the color value conversion matrix, and converting the CIEXYZ value of the analysis sample or the verification sample into the CIELAB value.
Optionally, the step of establishing a relationship model of anthocyanin color development intensity and CIELAB value of the maize filament by utilizing CIELAB value and anthocyanin color development intensity visual classification of the analysis sample specifically comprises the following steps:
Obtaining anthocyanin color development intensity visual classification and corresponding CIELAB values of a plurality of maize filament images in the analysis sample;
And carrying out least square unitary regression analysis on the anthocyanin color development intensity visual classification and the color value L in the corresponding CIELAB value, and establishing a relationship model of the anthocyanin color development intensity and the CIELAB value of the maize filament to generate a unitary regression line of the anthocyanin color development intensity and the CIELAB value.
Optionally, verifying the relationship model by visual classification of the CIELAB value and anthocyanin color development intensity of the verification sample, and optimizing the relationship model according to a verification result, which specifically includes:
Inputting CIELAB values of a plurality of corn silk images in the verification sample into the relation model, and calculating the numerical difference between anthocyanin chromogenic intensity corresponding to the corn silk images output by the relation model and visual classification of the corn silk images and the anthocyanin chromogenic intensity;
Judging whether a maize filament image with a numerical value difference exceeding a preset error allowable value exists in the verification sample, if so, dividing all color values L of the maize filament image in the analysis sample into sections once, and carrying out least square unitary regression analysis in the sections by utilizing the color values L of the maize filament image in the two sections after division and the corresponding anthocyanin color development intensity visual classification respectively to obtain a unitary regression line of each section;
repeating the verification process until no maize filament image with the numerical value difference exceeding the preset error allowable value exists in the verification sample, and generating a relation model of anthocyanin color development intensity and CIELAB value of the maize filament based on a unitary regression line of each interval.
Optionally, the dividing position is each color value L of the corn silk image in the analysis sample; performing a primary interval division step on all color values L of the maize filament image in the analysis sample, wherein the method specifically comprises the following steps:
Through repeated verification, if the interval after the interval division by taking the current color value L as the dividing position cannot meet the requirements of the numerical value difference and the preset error allowable value, the interval division is carried out by taking the next color value L as the dividing position;
If the interval corresponding to each dividing position cannot meet the requirements of the numerical value difference and the preset error allowable value, selecting interval division with the numerical value difference exceeding the preset error allowable value minimum occurrence number from all the interval division results, respectively carrying out interval division again on each interval obtained by the interval division result, and repeating the interval verification process.
Optionally, based on the optimized relation model, generating a comparison relation table of the anthocyanin color development intensity of the maize filaments and the CIELAB value, and performing the anthocyanin color development intensity classification step on the maize filaments to be detected by using the comparison relation table, wherein the method specifically comprises the following steps:
Acquiring a unitary regression line of each interval corresponding to the optimized relation model, inputting the intermediate value of the color development intensity of each two adjacent anthocyanin into the unitary regression line, and outputting a color value L corresponding to the intermediate value;
Taking the color value L of two adjacent intermediate values corresponding to each anthocyanin color development intensity as the range of the color value L corresponding to the anthocyanin color development intensity, and generating a comparison relation table of the maize filaments and CIELAB values under each anthocyanin color development intensity;
and classifying anthocyanin color development intensity of the maize filaments to be detected by using the comparison relation table.
In addition, in order to achieve the above object, the present invention also provides a maize silk anthocyanin color development intensity classification device, comprising:
The dividing module is used for obtaining a corn silk sample set and dividing the corn silk sample set into an analysis sample and a verification sample; the corn silk sample set comprises a plurality of corn silk images;
The determining module is used for placing the analysis sample and the verification sample into an image acquisition chamber, acquiring a color measurement image in the image acquisition chamber by using an image acquisition device, and determining CIELAB values of the analysis sample and the verification sample based on the color measurement image;
The establishing module is used for visually classifying by utilizing the CIELAB value and anthocyanin color development intensity of the analysis sample and establishing a relation model of the anthocyanin color development intensity and the CIELAB value of the maize filament;
the optimizing module is used for verifying the relation model by visual classification of CIELAB values and anthocyanin color development intensity of the verification sample, and optimizing the relation model according to verification results;
and the grading module is used for generating a comparison relation table of the anthocyanin color development intensity of the maize filaments and the CIELAB value under each anthocyanin color development intensity based on the optimized relation model, and grading the anthocyanin color development intensity of the maize filaments to be tested by utilizing the comparison relation table.
In addition, in order to achieve the above object, the present invention also provides a maize silk anthocyanin color development intensity classification apparatus comprising: the system comprises a memory, a processor and a maize anthocyanin color intensity grading program which is stored in the memory and can run on the processor, wherein the maize anthocyanin color intensity grading program realizes the steps of the maize anthocyanin color intensity grading method when being executed by the processor.
In addition, in order to achieve the above object, the present invention also provides a storage medium having a maize anthocyanin color development intensity classification program stored thereon, which when executed by a processor, implements the steps of the maize anthocyanin color development intensity classification method described above.
The invention has the beneficial effects that: the invention provides a method, a device, equipment and a storage medium for classifying the anthocyanin color intensity of corn filaments, which divide a sample set formed by corn filament images into an analysis sample and a verification sample, utilize the anthocyanin color intensity of the analysis sample to visually classify and measure CIELAB values to establish a relation model of the anthocyanin color intensity of corn filaments and the CIELAB values, utilize the verification sample to verify and optimize the relation model, enable the relation model to have higher detection accuracy, and utilize each anthocyanin color intensity corresponding to the relation model to compare with the CIELAB values to realize the anthocyanin color intensity classification of corn filaments to be detected, thereby reducing the calculation complexity and the operation steps while guaranteeing the color intensity detection precision of corn filaments, and providing detection convenience.
Drawings
FIG. 1 is a schematic diagram of a device structure of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of an embodiment of the method for classifying the color development intensity of the maize anthocyanin according to the invention;
FIG. 3 is a block diagram showing a device for classifying the color development intensity of the anthocyanin in maize filaments according to the embodiment of the invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of an apparatus structure of a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the arrangement of the apparatus shown in fig. 1 is not limiting and may include more or fewer components than shown, or certain components may be combined, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a maize anthocyanin color intensity classification program may be included in a memory 1005 as a computer storage medium.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and processor 1001 may be configured to invoke the maize anthocyanin color intensity grading program stored in memory 1005 and perform the following operations:
obtaining a maize filament sample set, and dividing the maize filament sample set into an analysis sample and a verification sample; the corn silk sample set comprises a plurality of corn silk images;
Placing the analysis sample and the verification sample into an image acquisition chamber, acquiring a color measurement image in the image acquisition chamber by using an image acquisition device, and determining CIELAB values of the analysis sample and the verification sample based on the color measurement image;
visual classification is carried out by utilizing the CIELAB value and anthocyanin color development intensity of the analysis sample, and a relation model of the anthocyanin color development intensity and the CIELAB value of the maize filament is established;
verifying the relation model by visual classification of CIELAB values and anthocyanin color development intensity of the verification samples, and optimizing the relation model according to verification results;
Based on the optimized relation model, a comparison relation table of the anthocyanin color development intensity of the maize filaments and CIELAB values is generated, and the comparison relation table is utilized to classify the anthocyanin color development intensity of the maize filaments to be detected.
The specific embodiment of the present invention applied to the apparatus is substantially the same as the following embodiments of the method for classifying the color development intensity of the maize anthocyanin, and will not be described herein.
The embodiment of the invention provides a method for classifying the color development intensity of maize silk anthocyanin, and referring to fig. 2, fig. 2 is a flow chart of an embodiment of the method for classifying the color development intensity of maize silk anthocyanin.
In this embodiment, the method for classifying the color development intensity of the maize anthocyanin comprises the following steps:
S100: obtaining a maize filament sample set, and dividing the maize filament sample set into an analysis sample and a verification sample; the corn silk sample set comprises a plurality of corn silk images;
s200: placing the analysis sample and the verification sample into an image acquisition chamber, acquiring a color measurement image in the image acquisition chamber by using an image acquisition device, and determining CIELAB values of the analysis sample and the verification sample based on the color measurement image;
S300: visual classification is carried out by utilizing the CIELAB value and anthocyanin color development intensity of the analysis sample, and a relation model of the anthocyanin color development intensity and the CIELAB value of the maize filament is established;
s400: verifying the relation model by visual classification of CIELAB values and anthocyanin color development intensity of the verification samples, and optimizing the relation model according to verification results;
S500: based on the optimized relation model, a comparison relation table of the anthocyanin color development intensity of the maize filaments and CIELAB values is generated, and the comparison relation table is utilized to classify the anthocyanin color development intensity of the maize filaments to be detected.
The existing detection scheme for the color development intensity of the maize anthocyanin has the problems of complex calculation, complex operation and the like. In order to solve the above problems, in this embodiment, a sample set formed by a corn silk image is divided into an analysis sample and a verification sample, a relationship model of the anthocyanin color development intensity of the corn silk and the CIELAB value is established by visual classification and measurement of the anthocyanin color development intensity of the analysis sample, and then the relationship model is verified and optimized by the verification sample, so that the relationship model has higher detection accuracy, and the anthocyanin color development intensity classification of the corn silk to be detected is realized by comparing each anthocyanin color development intensity corresponding to the relationship model with the CIELAB value, thereby reducing the calculation complexity while ensuring the color development intensity detection precision of the anthocyanin of the corn silk, reducing the operation steps, providing detection convenience, and providing a corn silk anthocyanin color development intensity classification method with more effective boundaries in practical application scenes.
In a preferred embodiment, the step of obtaining a maize filament sample set, dividing said maize filament sample set into an analysis sample and a verification sample, specifically comprises: obtaining a maize filament sample set; the corn silk sample set comprises a plurality of corn silk images with anthocyanin color development intensities which are visually classified and uniformly distributed in 9 anthocyanin color development intensity levels; dividing a maize filament image of each anthocyanin color development intensity level in the maize filament sample set into an analysis sample image and a verification sample image according to a preset proportion; taking the analysis sample image of each anthocyanin color development intensity level as an analysis sample of the corn silk sample set, and taking the verification sample image of each anthocyanin color development intensity level as a verification sample of the corn silk sample set.
In this embodiment, a maize filament image including a plurality of anthocyanin color development intensities visually classified uniformly distributed in 9 anthocyanin color development intensity levels is obtained as a maize filament sample set, and the maize filament sample set is divided into an analysis sample and a verification sample. Specifically, in practical application, standard images of filaments of 225 corn varieties can be collected as research materials, and anthocyanin color development intensity is 1-9 grade and 25 varieties respectively. Wherein, 20 varieties are selected for regression analysis for each grade, and 180 varieties are used for analysis; 5 varieties per grade were selected as validated varieties, with 45 varieties in total for validation.
In a preferred embodiment, the step of placing the analysis sample and the verification sample in an image acquisition room, acquiring a color measurement image in the image acquisition room by using an image acquisition device, and determining CIELAB values of the analysis sample and the verification sample based on the color measurement image specifically includes: respectively placing the analysis sample and the verification sample into an image acquisition chamber together with a standard color card, and acquiring a first color measurement image and a second color measurement image which comprise two opposite positions of the analysis sample and the standard color card or the verification sample and the standard color card in the image acquisition chamber by using an image acquisition device; establishing a color value conversion matrix according to a first RGB value of each color block of a standard color card and a CIEXYZ value of the color block in the first color measurement image and the second color measurement image; and determining the CIEXYZ value of the analysis sample or the verification sample according to the second RGB value of the analysis sample or the verification sample in the first color measurement image and the second color measurement image and the color value conversion matrix, and converting the CIEXYZ value of the analysis sample or the verification sample into the CIELAB value.
In this embodiment, for the measurement of anthocyanin color of the maize filament image corresponding to the analysis sample and the verification sample, the following method is adopted: placing the maize filament image and the standard color card into an image acquisition chamber, wherein a shooting light source is arranged in the image acquisition chamber, the whole sealing design is adopted, the external light source is prevented from entering the image acquisition chamber, the influence caused by environmental change in the anthocyanin color measurement process of the maize filament image is reduced, and a first color measurement image and a second color measurement image which are positioned at two opposite positions in the image acquisition chamber and comprise a maize filament image photo and the standard color card are acquired in the image acquisition chamber; taking the average value of the first RGB value of each color block in the first color measurement image and the first RGB value of each color block in the second color measurement image as the RGB value of each color block in the standard color card for removing environmental interference, establishing a color value conversion matrix according to the RGB value and the CIEXYZ value of the color block, determining the CIEXYZ value of the corn silk image according to the second RGB value of the corn silk image in the first color measurement image and the second color measurement image and the color value conversion matrix, and converting the CIEXYZ value of the corn silk image into the CIELAB value of the corn silk image; therefore, high-precision detection of anthocyanin color of the corn silk image corresponding to the analysis sample and the verification sample is realized, the problem of low detection precision caused by a human eye observation mode is avoided, and further, a relation model can be established according to visual classification of anthocyanin color value of the corn silk image detected with high precision and corresponding anthocyanin color development intensity, and data support is provided for anthocyanin color value measurement of corn silk to be detected and quality fine classification of corn.
In a preferred embodiment, the step of establishing a model of the relationship between anthocyanin development intensity and CIELAB value of the maize filaments by visual classification using CIELAB value and anthocyanin development intensity of the analysis sample specifically comprises: obtaining anthocyanin color development intensity visual classification and corresponding CIELAB values of a plurality of maize filament images in the analysis sample; and (3) carrying out least square unitary regression analysis on the visual classification of anthocyanin color development intensity and the color value L in the corresponding CIELAB value, and establishing a relationship model of anthocyanin color development intensity and the CIELAB value of the maize filament to generate a unitary regression line of anthocyanin color development intensity and the CIELAB value.
In this embodiment, after the CIELAB value and the anthocyanin color development intensity of the analysis sample are obtained and visually classified, the CIELAB value and the anthocyanin color development intensity of each maize filament image in the analysis sample may be visually classified as a set of data pairs, and according to a plurality of sets of data pairs of a plurality of maize filament images, a least square unitary regression analysis is performed by using the visual classification of the maize filament anthocyanin color development intensity and the color value L of the maize filament image to generate a unitary regression line for representing the relationship between the anthocyanin color development intensity and the CIELAB value of the maize filament, and the relationship model of the anthocyanin color development intensity and the color value L of the maize filament is primarily characterized by using the unitary regression line.
Further, the relationship model is verified by visual classification of CIELAB values and anthocyanin color development intensities of the verification samples, and the relationship model is optimized according to verification results, specifically comprising the following steps: inputting CIELAB values of a plurality of corn silk images in the verification sample into the relation model, and calculating the numerical difference between anthocyanin chromogenic intensity corresponding to the corn silk images output by the relation model and visual classification of the corn silk images and the anthocyanin chromogenic intensity; judging whether a maize filament image with a numerical value difference exceeding a preset error allowable value exists in the verification sample, if so, dividing all color values L of the maize filament image in the analysis sample into sections once, and carrying out least square unitary regression analysis in the sections by utilizing the color values L of the maize filament image in the two sections after division and the corresponding anthocyanin color development intensity visual classification respectively to obtain a unitary regression line of each section; repeating the verification process until no maize filament image with the numerical value difference exceeding the preset error allowable value exists in the verification sample, and generating a relation model of anthocyanin color development intensity and CIELAB value of the maize filament based on a unitary regression line of each interval.
The dividing position is each color value L of the corn silk image in the analysis sample; performing a primary interval division step on all color values L of the maize filament image in the analysis sample, wherein the method specifically comprises the following steps: through repeated verification, if the interval after the interval division by taking the current color value L as the dividing position cannot meet the requirements of the numerical value difference and the preset error allowable value, the interval division is carried out by taking the next color value L as the dividing position; if the interval corresponding to each dividing position cannot meet the requirements of the numerical value difference and the preset error allowable value, selecting interval division with the numerical value difference exceeding the preset error allowable value minimum occurrence number from all the interval division results, respectively carrying out interval division again on each interval obtained by the interval division result, and repeating the interval verification process.
Considering that the unitary regression line obtained by performing least square unitary regression analysis on the color value L of the maize silk anthocyanin color development intensity visual classification and the maize silk image may have a large error in practical application, in this embodiment, the relationship model is verified and optimized by using the verification sample CIELAB value and the anthocyanin color development intensity visual classification, and the specific verification and optimization process is as follows: firstly, generating anthocyanin color development intensities of a plurality of corn filament images in a verification sample by utilizing a relation model, calculating the numerical difference of visual classification of the anthocyanin color development intensities and the anthocyanin color development intensities, if the corn filament images with the numerical difference exceeding a preset error allowable value are in the verification sample, indicating that the error of the unitary regression line is larger in a certain range, at the moment, sequentially selecting the color value L from all the color values L of the corn filament images in the analysis sample as a division position to divide the whole color value L into intervals, and then carrying out the least square unitary regression and verification process on each divided interval until no corn filament image with the numerical difference exceeding the preset error allowable value exists in the verification sample, wherein the relation model formed by all the unitary regression lines of each interval is the relation model of the anthocyanin color development intensities and CIELAB values with smaller error and higher detection accuracy, and the relation model is used for classifying the anthocyanin color development intensities of corn filaments to be detected, and has higher detection accuracy and higher detection efficiency.
In a specific example of practical application, after least square unitary regression and verification optimization are performed by using an analysis sample, the obtained analysis result is as follows:
| L≧30 | L<30 | |
| Multiple R | 0.996684515 | 0.932113420 |
| R Square | 0.993380023 | 0.868835428 |
| Adjusted R Square | 0.993265885 | 0.867723864 |
Table 1: regression effect parameters
Wherein, multiple R, R Square and Adjusted R Square are 3 evaluation indexes of regression equation, 3 values are between 0 and 1, and the closer to 1, the better the effect.
Table 2: regression analysis results
Wherein intersett is a constant term and L is a coefficient of the color value L. Wherein, the P value of the constant term and L is less than 0.05, and the difference is obvious.
Thus, a relation model of anthocyanin color development intensity of maize filaments and CIELAB value in a specific example is obtained, wherein = -0.0829 L+ 5.5711 (L value +.30); anthocyanin development intensity= -0.6038 l+ 21.728 (L value < 30).
In a preferred embodiment, based on the optimized relation model, a comparison relation table of the anthocyanin color development intensity of each maize filament and CIELAB value is generated, and the comparison relation table is utilized to carry out the anthocyanin color development intensity classification step on the maize filament to be detected, which specifically comprises the following steps: acquiring a unitary regression line of each interval corresponding to the optimized relation model, inputting the intermediate value of the color development intensity of each two adjacent anthocyanin into the unitary regression line, and outputting a color value L corresponding to the intermediate value; taking the color value L of two adjacent intermediate values corresponding to each anthocyanin color development intensity as the range of the color value L corresponding to the anthocyanin color development intensity, and generating a comparison relation table of the maize filaments and CIELAB values under each anthocyanin color development intensity; and classifying anthocyanin color development intensity of the maize filaments to be detected by using the comparison relation table.
In this embodiment, the anthocyanin color development intensity classification is performed on the maize filaments to be tested by using the comparison relation table, and the comparison relation table between the maize filaments and the CIELAB value under each anthocyanin color development intensity is as follows:
| Hierarchical code | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| L value | L>49.10 | 37.04<L≦49.10 | 30≦L≦37.04 | 28.53<L<30 | 26.87<L≦28.53 | 25.22<L≦26.87 | 23.56<L≦25.22 | 21.90<L≦23.56 | ≦21.90 |
Table 3: table of comparison relation
According to the method, a sample set formed by a corn silk image is divided into an analysis sample and a verification sample, a relation model of the anthocyanin color development intensity of the corn silk and the CIELAB value is established by utilizing the CIELAB value of the analysis sample for visual classification and measurement, and then the relation model is verified and optimized by utilizing the verification sample, so that the relation model has higher detection accuracy, and the anthocyanin color development intensity classification of the corn silk to be detected is realized by utilizing a relation table of each anthocyanin color development intensity corresponding to the relation model and the CIELAB value, so that the calculation complexity is reduced, the operation steps are reduced while the color development intensity detection precision of the corn silk anthocyanin is ensured, the detection convenience is improved, and a corn silk anthocyanin color development intensity classification method with more effective boundaries in a practical application scene is provided.
Referring to fig. 3, fig. 3 is a block diagram showing an embodiment of the apparatus for classifying the color development intensity of maize anthocyanin according to the present invention.
As shown in FIG. 3, the maize silk anthocyanin color development intensity grading device provided by the embodiment of the invention comprises:
The dividing module 10 is used for obtaining a maize filament sample set and dividing the maize filament sample set into an analysis sample and a verification sample; the corn silk sample set comprises a plurality of corn silk images;
A determining module 20, configured to put the analysis sample and the verification sample into an image acquisition room, acquire a color measurement image in the image acquisition room by using an image acquisition device, and determine CIELAB values of the analysis sample and the verification sample based on the color measurement image;
a building module 30, configured to visually classify the analysis sample according to the CIELAB value and the anthocyanin color development intensity, and build a relationship model of the anthocyanin color development intensity and the CIELAB value of the maize filament;
the optimizing module 40 is configured to verify the relationship model by using a CIELAB value and anthocyanin color development intensity visual classification of the verification sample, and optimize the relationship model according to a verification result;
and the grading module 50 is used for generating a comparison relation table of the anthocyanin color development intensity of the maize filaments and the CIELAB value under each anthocyanin color development intensity based on the optimized relation model, and grading the anthocyanin color development intensity of the maize filaments to be tested by using the comparison relation table.
Other embodiments or specific implementation manners of the maize anthocyanin color development intensity grading device can refer to the above method embodiments, and are not described herein.
In addition, the invention also provides a maize silk anthocyanin color development intensity grading device, which comprises: the system comprises a memory, a processor and a maize anthocyanin color intensity grading program which is stored in the memory and can run on the processor, wherein the maize anthocyanin color intensity grading program realizes the steps of the maize anthocyanin color intensity grading method when being executed by the processor.
The specific embodiment of the maize silk anthocyanin color development intensity classification equipment is basically the same as the above examples of the maize silk anthocyanin color development intensity classification method, and is not repeated here.
In addition, the invention also provides a readable storage medium, which comprises a computer readable storage medium, and a maize anthocyanin color development intensity grading program is stored on the computer readable storage medium. The readable storage medium may be a Memory 1005 in the terminal of fig. 1, or may be at least one of a ROM (Read-Only Memory)/RAM (Random Access Memory ), a magnetic disk, and an optical disk, and the readable storage medium includes a plurality of instructions for causing a maize anthocyanin color intensity grading device having a processor to perform the maize anthocyanin color intensity grading method according to the embodiments of the present invention.
The specific embodiments in the readable storage medium of the present application are substantially the same as the above-described examples of the method for classifying the color development intensity of the maize anthocyanin, and will not be described herein.
It is appreciated that in the description herein, reference to the terms "one embodiment," "another embodiment," "other embodiments," or "first through nth embodiments," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (6)
1. The method for classifying the color development intensity of the maize anthocyanin is characterized by comprising the following steps of:
obtaining a maize filament sample set, and dividing the maize filament sample set into an analysis sample and a verification sample; the corn silk sample set comprises a plurality of corn silk images;
Placing the analysis sample and the verification sample into an image acquisition chamber, acquiring a color measurement image in the image acquisition chamber by using an image acquisition device, and determining CIELAB values of the analysis sample and the verification sample based on the color measurement image;
visual classification is carried out by utilizing the CIELAB value and anthocyanin color development intensity of the analysis sample, and a relation model of the anthocyanin color development intensity and the CIELAB value of the maize filament is established;
Verifying the relation model by visual classification of CIELAB values and anthocyanin color development intensity of the verification samples, and optimizing the relation model according to verification results; the method specifically comprises the following steps:
Inputting CIELAB values of a plurality of corn silk images in the verification sample into the relation model, and calculating the numerical difference between anthocyanin chromogenic intensity corresponding to the corn silk images output by the relation model and visual classification of the corn silk images and the anthocyanin chromogenic intensity;
Judging whether a maize filament image with a numerical value difference exceeding a preset error allowable value exists in the verification sample, if so, dividing all color values L of the maize filament image in the analysis sample into sections once, and carrying out least square unitary regression analysis in the sections by utilizing the color values L of the maize filament image in the two sections after division and the corresponding anthocyanin color development intensity visual classification respectively to obtain a unitary regression line of each section;
Repeating the verification process until no maize filament image with the numerical value difference exceeding the preset error allowable value exists in the verification sample, and generating a relation model of anthocyanin color development intensity and CIELAB value of the maize filament based on a unitary regression line of each interval;
The dividing position of the interval division is each color value L of the corn silk image in the analysis sample; performing a primary interval division step on all color values L of the maize filament image in the analysis sample, wherein the method specifically comprises the following steps:
Through repeated verification, if the interval after the interval division by taking the current color value L as the dividing position cannot meet the requirements of the numerical value difference and the preset error allowable value, the interval division is carried out by taking the next color value L as the dividing position;
If the interval corresponding to each dividing position cannot meet the requirements of the numerical value difference and the preset error allowable value, selecting interval division with the minimum number of times that the numerical value difference exceeds the preset error allowable value from all interval division results, respectively carrying out interval division again on each interval obtained by the interval division result, and repeating the interval verification process;
Based on the optimized relation model, a comparison relation table of the anthocyanin color development intensity of the maize filaments and CIELAB values is generated, and the comparison relation table is utilized to classify the anthocyanin color development intensity of the maize filaments to be detected.
2. The method for classifying the color development intensity of the anthocyanin in maize filaments according to claim 1, wherein the step of obtaining a maize filament sample set and dividing the maize filament sample set into an analysis sample and a verification sample comprises the steps of:
Obtaining a maize filament sample set; the corn silk sample set comprises a plurality of corn silk images with anthocyanin color development intensities which are visually classified and uniformly distributed in 9 anthocyanin color development intensity levels;
dividing a maize filament image of each anthocyanin color development intensity level in the maize filament sample set into an analysis sample image and a verification sample image according to a preset proportion;
Taking the analysis sample image of each anthocyanin color development intensity level as an analysis sample of the corn silk sample set, and taking the verification sample image of each anthocyanin color development intensity level as a verification sample of the corn silk sample set.
3. The method for classifying the color development intensity of maize silk anthocyanin according to claim 2, wherein the step of placing the analysis sample and the verification sample into an image collection room, collecting a color measurement image in the image collection room by using an image collection device, and determining the CIELAB values of the analysis sample and the verification sample based on the color measurement image specifically comprises:
respectively placing the analysis sample and the verification sample into an image acquisition chamber together with a standard color card, and acquiring a first color measurement image and a second color measurement image which comprise two opposite positions of the analysis sample and the standard color card or the verification sample and the standard color card in the image acquisition chamber by using an image acquisition device;
Establishing a color value conversion matrix according to a first RGB value of each color block of a standard color card and a CIEXYZ value of the color block in the first color measurement image and the second color measurement image;
And determining the CIEXYZ value of the analysis sample or the verification sample according to the second RGB value of the analysis sample or the verification sample in the first color measurement image and the second color measurement image and the color value conversion matrix, and converting the CIEXYZ value of the analysis sample or the verification sample into the CIELAB value.
4. The method for classifying anthocyanin color development intensity of maize filaments according to claim 3, wherein the step of establishing a model of the relationship between anthocyanin color development intensity and CIELAB value of maize filaments by visual classification using CIELAB value and anthocyanin color development intensity of the analysis sample comprises:
Obtaining anthocyanin color development intensity visual classification and corresponding CIELAB values of a plurality of maize filament images in the analysis sample;
And carrying out least square unitary regression analysis on the anthocyanin color development intensity visual classification and the color value L in the corresponding CIELAB value, and establishing a relationship model of the anthocyanin color development intensity and the CIELAB value of the maize filament to generate a unitary regression line of the anthocyanin color development intensity and the CIELAB value.
5. The method for classifying anthocyanin color intensity of maize filaments according to claim 4, wherein based on the optimized relation model, a comparison relation table of anthocyanin color intensity of maize filaments and CIELAB value is generated, and the comparison relation table is utilized to classify the anthocyanin color intensity of maize filaments to be detected, which specifically comprises the following steps:
Acquiring a unitary regression line of each interval corresponding to the optimized relation model, inputting the intermediate value of the color development intensity of each two adjacent anthocyanin into the unitary regression line, and outputting a color value L corresponding to the intermediate value;
Taking the color value L of two adjacent intermediate values corresponding to each anthocyanin color development intensity as the range of the color value L corresponding to the anthocyanin color development intensity, and generating a comparison relation table of the maize filaments and CIELAB values under each anthocyanin color development intensity;
and classifying anthocyanin color development intensity of the maize filaments to be detected by using the comparison relation table.
6. The utility model provides a maize silk anthocyanin color development intensity grading plant which characterized in that includes:
The dividing module is used for obtaining a corn silk sample set and dividing the corn silk sample set into an analysis sample and a verification sample; the corn silk sample set comprises a plurality of corn silk images;
The determining module is used for placing the analysis sample and the verification sample into an image acquisition chamber, acquiring a color measurement image in the image acquisition chamber by using an image acquisition device, and determining CIELAB values of the analysis sample and the verification sample based on the color measurement image;
The establishing module is used for visually classifying by utilizing the CIELAB value and anthocyanin color development intensity of the analysis sample and establishing a relation model of the anthocyanin color development intensity and the CIELAB value of the maize filament;
The optimizing module is used for verifying the relation model by visual classification of CIELAB values and anthocyanin color development intensity of the verification sample, and optimizing the relation model according to verification results; the method specifically comprises the following steps:
Inputting CIELAB values of a plurality of corn silk images in the verification sample into the relation model, and calculating the numerical difference between anthocyanin chromogenic intensity corresponding to the corn silk images output by the relation model and visual classification of the corn silk images and the anthocyanin chromogenic intensity;
Judging whether a maize filament image with a numerical value difference exceeding a preset error allowable value exists in the verification sample, if so, dividing all color values L of the maize filament image in the analysis sample into sections once, and carrying out least square unitary regression analysis in the sections by utilizing the color values L of the maize filament image in the two sections after division and the corresponding anthocyanin color development intensity visual classification respectively to obtain a unitary regression line of each section;
Repeating the verification process until no maize filament image with the numerical value difference exceeding the preset error allowable value exists in the verification sample, and generating a relation model of anthocyanin color development intensity and CIELAB value of the maize filament based on a unitary regression line of each interval;
the dividing position of the interval division is each color value L of the corn silk image in the analysis sample; dividing all color values L of the maize filament image in the analysis sample into intervals, wherein the method specifically comprises the following steps:
Through repeated verification, if the interval after the interval division by taking the current color value L as the dividing position cannot meet the requirements of the numerical value difference and the preset error allowable value, the interval division is carried out by taking the next color value L as the dividing position;
If the interval corresponding to each dividing position cannot meet the requirements of the numerical value difference and the preset error allowable value, selecting interval division with the minimum number of times that the numerical value difference exceeds the preset error allowable value from all interval division results, respectively carrying out interval division again on each interval obtained by the interval division result, and repeating the interval verification process;
and the grading module is used for generating a comparison relation table of the anthocyanin color development intensity of the maize filaments and the CIELAB value under each anthocyanin color development intensity based on the optimized relation model, and grading the anthocyanin color development intensity of the maize filaments to be tested by utilizing the comparison relation table.
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