CN117197479A - Image analysis method, device, computer equipment and storage medium applying corn ear outer surface - Google Patents
Image analysis method, device, computer equipment and storage medium applying corn ear outer surface Download PDFInfo
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Abstract
The embodiment of the application belongs to the technical field of testing of new varieties of plants, and relates to an image analysis method and related equipment for applying the outer surface of corn ears. According to the application, operations such as Gaussian smoothing operation, downsampling operation, color separation operation, threshold screening operation, integral drawing operation, numbering marking and the like are performed on the corn ear outer surface image, 50 basic phenotype data are obtained, and the requirements in GB/T19557.24-2018 plant variety specificity, consistency and stability test guide corn standard can be effectively covered, so that the technical problem that all characters cannot be considered in the traditional plant new variety testing method is effectively solved, and meanwhile, the original value error can be kept within 5%, and the grading accuracy is up to 100%.
Description
Technical Field
The application relates to the technical field of testing of new varieties of plants, in particular to an image analysis method, an image analysis device, computer equipment and a storage medium applying the outer surface of corn ears
Background
The specificity, consistency and stability (DUS for short) test is a legal technical basis for granting the right, approval and registration of new varieties of plants in China, and is also an internationally-popular new variety identification method of plants. The international new plant variety protection consortium (briefly called UPOV) was established in 1961, and the aim was to promote the unification of the international new plant variety protection system and the DUS testing technology. The alliance was in the 1983 state of the automation and computer program technology work group (TWC), holding a technical communication meeting, communicating and discussing DUS test procedures and data analysis technology each year. In the DUS test field, the application of the image analysis technology can be traced back to 1987, and in the 6 th meeting of TWC, british expert introduced the application of the machine vision technology in variety identification for the first time, effectively calculating the height, diameter and widest distance of onion, and in the 14 th meeting of TWC in 1995, british expert introduced the image analysis technology to analyze color values and distribution thereof. Image analysis techniques have been widely used in the field of DUS testing to date. In order to obtain more accurate measured values and color values, the professionals in each country mainly adopt coins or other round objects as references to conduct scale identification and color correction, and also have application research on 24-color cards, and the analyzed simple characters are more, such as length, width, area, perimeter, quantity and the like, but the complicated characters are less, such as shape, color and the like.
However, the applicant found that, because of the multiple traits in one image, all shapes cannot be considered by the existing new plant variety testing method, so that the problem that all the traits cannot be considered by the conventional new plant variety testing method exists.
Disclosure of Invention
The embodiment of the application aims to provide an image analysis method, an image analysis device, computer equipment and a storage medium applied to new plant variety testing, so as to solve the problem that all properties cannot be considered in the traditional new plant variety testing method.
In order to solve the above technical problems, the embodiment of the application provides an image analysis method using the outer surface of corn ears, which adopts the following technical scheme:
acquiring an image of the outer surface of a corn ear to be detected;
carrying out Gaussian smoothing operation on the corn ear outer surface image to obtain a Gaussian smoothing image;
performing downsampling operation on the Gaussian smooth image to obtain a downsampled image;
performing color separation operation on the downsampled image according to a color deconvolution algorithm analysis to obtain a cluster contour set, wherein the cluster contour set comprises integral ROI contour data of the whole cluster and grain ROI contour data of the cluster grains;
threshold screening operation is carried out on the cluster profile set, and screening profile data are obtained;
carrying out overall drawing operation according to the screening profile data to obtain the overall profile of the corn ears;
numbering and marking the whole corn ear outline, and respectively drawing a minimum rectangular frame of the marked whole corn ear outline to obtain corn ear rectangular data, wherein the corn ear rectangular data comprises the length of the minimum rectangular frame and the width of the minimum rectangular frame;
basic phenotype data of the corn ear rectangular data are obtained;
and carrying out transformation operation on the basic phenotype data according to the grading standard to obtain a target detection result.
Further, the basic phenotype data comprises color analysis data, plant area data and quarter width data, and the step of obtaining the basic phenotype data of the corn ear rectangular data specifically comprises the following steps:
performing color analysis operation on the corn ear rectangular data to obtain color analysis data;
performing area extraction operation on the corn ear rectangular data to obtain plant area data;
and carrying out quarter width extraction operation on the corn ear rectangular data to obtain quarter width data.
Further, after the step of obtaining the corn ear outer surface image to be detected and before the step of performing gaussian smoothing operation on the corn ear outer surface image to obtain a gaussian smoothed image, the method further comprises the following steps:
performing image correction operation on the corn ear outer surface image according to the eight-color scale to obtain corrected image data;
the step of performing Gaussian smoothing operation on the corn ear outer surface image to obtain a Gaussian smoothed image specifically comprises the following steps:
and carrying out Gaussian smoothing operation on the corrected image data to obtain the Gaussian smoothed image.
Further, the step of performing image correction operation on the corn ear outer surface image according to the eight-color scale to obtain corrected image data specifically includes the following steps:
acquiring real-time shooting optical three primary colors of a plant to be detected in real time from the corn ear outer surface image;
acquiring color block information of the eight-color scale, and acquiring target color block information corresponding to the real photographing optical three primary colors in the color block information;
and carrying out nonlinear fitting on the three primary colors of the real shooting optics according to the target color block information to obtain the corrected image data.
Further, the step of performing image correction operation on the real shot image data according to the eight-color scale to obtain corrected image data specifically includes the following steps:
acquiring the real beat average diameter in the corn ear outer surface image according to the eight color scale;
and carrying out pixel-scale conversion operation on the real beat average diameter according to the standard diameter to obtain the corrected image data.
In order to solve the technical problems, the embodiment of the application also provides an image analysis device applying the outer surface of the corn ear, which adopts the following technical scheme:
the acquisition module is used for acquiring an image of the outer surface of the corn ear to be detected;
the Gaussian smoothing module is used for carrying out Gaussian smoothing operation on the corn ear outer surface image to obtain a Gaussian smoothing image;
the downsampling module is used for performing downsampling operation on the Gaussian smooth image to obtain a downsampled image;
the color separation module is used for performing color separation operation on the downsampled image according to color deconvolution algorithm analysis to obtain a cluster contour set, wherein the cluster contour set comprises overall ROI contour data of the whole cluster and grain ROI contour data of the cluster grains;
the threshold screening module is used for carrying out threshold screening operation on the cluster profile set to obtain screening profile data;
the profile drawing module is used for carrying out overall drawing operation according to the screening profile data to obtain the overall profile of the corn ears;
the marking module is used for numbering and marking the whole corn ear outline and respectively drawing the minimum rectangular frame of the whole corn ear outline after marking to obtain corn ear rectangular data, wherein the corn ear rectangular data comprise the length of the minimum rectangular frame and the width of the minimum rectangular frame;
the phenotype data acquisition module is used for acquiring basic phenotype data of the corn ear rectangular data;
and the data conversion module is used for carrying out conversion operation on the basic phenotype data according to the grading standard to obtain a target detection result.
Further, the base phenotype data includes color analysis data, plant area data, and quarter width data, and the phenotype data acquisition module includes:
the color analysis sub-module is used for performing color analysis operation on the corn ear rectangular data to obtain color analysis data;
the area extraction sub-module is used for carrying out area extraction operation on the corn ear rectangular data to obtain plant area data;
and the quarter width extraction submodule is used for carrying out quarter width extraction operation on the corn ear rectangular data to obtain quarter width data.
Further, the device further comprises: an image correction module, the gaussian smoothing module comprising: gao Siping slide sub-module, wherein:
the image correction module is used for performing image correction operation on the corn ear outer surface image according to the eight-color scale to obtain corrected image data;
and the Gao Siping sliding sub-module is used for carrying out Gaussian smoothing operation on the corrected image data to obtain the Gaussian smoothed image.
In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:
the method comprises a memory and a processor, wherein the memory stores computer readable instructions, and the processor executes the computer readable instructions to realize the steps of the image analysis method using the outer surface of the corn ear.
In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:
the computer readable storage medium has stored thereon computer readable instructions which when executed by a processor implement the steps of the image analysis method as described above using the external surface of corn ears.
The application provides an image analysis method using the outer surface of corn ears, which comprises the following steps: acquiring an image of the outer surface of a corn ear to be detected; carrying out Gaussian smoothing operation on the corn ear outer surface image to obtain a Gaussian smoothing image; performing downsampling operation on the Gaussian smooth image to obtain a downsampled image; performing color separation operation on the downsampled image according to a color deconvolution algorithm analysis to obtain a cluster contour set, wherein the cluster contour set comprises integral ROI contour data of the whole cluster and grain ROI contour data of the cluster grains; threshold screening operation is carried out on the cluster profile set, and screening profile data are obtained; carrying out overall drawing operation according to the screening profile data to obtain the overall profile of the corn ears; numbering and marking the whole corn ear outline, and respectively drawing a minimum rectangular frame of the marked whole corn ear outline to obtain corn ear rectangular data, wherein the corn ear rectangular data comprises the length of the minimum rectangular frame and the width of the minimum rectangular frame; basic phenotype data of the corn ear rectangular data are obtained; and carrying out transformation operation on the basic phenotype data according to the grading standard to obtain a target detection result. Compared with the prior art, the application has the advantages that the obtained basic phenotype data is up to 50, and the application can effectively cover the character 30 in the standard of GB/T19557.24-2018 plant variety specificity, consistency and stability test guideline corn: ear length, trait 31: ear diameter, trait 33: ear shape, trait 34: number of color and shape of cluster seeds 35: the yellow degree of the cluster seeds and the characteristics 40: the method can effectively solve the technical problem that all properties cannot be considered in the traditional new plant variety testing method due to the requirements of main color at the top end of the seed, length of bald tip and number of seeds in each row, and can keep the original value error within a range of 5% and the grading accuracy is up to 100%.
Drawings
In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
fig. 2 is a flowchart of an implementation of an image analysis method using the external surface of corn ears according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an image analysis device using the external surface of corn ears according to a second embodiment of the present application;
FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the image analysis method applied to the external surface of the corn ear provided by the embodiment of the application is generally executed by a server/terminal device, and correspondingly, the image analysis device applied to the external surface of the corn ear is generally arranged in the server/terminal device.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow chart of one embodiment of a method of image analysis using the outer surface of a corn ear in accordance with the present application is shown. The image analysis method using the outer surface of the corn ear comprises the following steps: step S201, step S202, step S203, step S204, step S205, step S206, and step S207.
In step S201, acquiring an image of the outer surface of the corn ear to be detected;
in step S202, gaussian smoothing operation is carried out on the corn ear outer surface image to obtain a Gaussian smooth image;
in step S203, downsampling operation is performed on the gaussian smoothing image to obtain a downsampled image;
in step S204, performing color separation operation on the downsampled image according to color deconvolution algorithm analysis to obtain a cluster contour set, wherein the cluster contour set includes overall ROI contour data of the cluster as a whole and kernel ROI contour data of the cluster kernels;
in step S205, performing a threshold screening operation on the cluster profile set to obtain screening profile data;
in step S206, overall drawing operation is carried out according to the screening contour data, so as to obtain the overall contour of the corn ears;
in step S207, numbering and marking the overall corn ear contour, and drawing the minimum rectangular frame of the overall corn ear contour after marking to obtain rectangular corn ear data, wherein the rectangular corn ear data includes the length of the minimum rectangular frame and the width of the minimum rectangular frame;
in step S208, basic phenotype data of corn ear rectangular data is acquired;
in step S209, the basic phenotype data is transformed according to the grading standard, so as to obtain a target detection result.
In an embodiment of the present application, an image analysis method using an outer surface of a corn ear is provided, including: acquiring an image of the outer surface of a corn ear to be detected; carrying out Gaussian smoothing operation on the corn ear outer surface image to obtain a Gaussian smoothed image; carrying out downsampling operation on the Gaussian smooth image to obtain a downsampled image; performing color separation operation on the downsampled image according to color deconvolution algorithm analysis to obtain a cluster contour set, wherein the cluster contour set comprises overall ROI contour data of the whole cluster and grain ROI contour data of the cluster grains; threshold screening operation is carried out on the cluster profile set, and screening profile data are obtained; carrying out overall drawing operation according to the screening contour data to obtain the overall contour of the corn ears; numbering and marking the overall corn ear outline, and respectively drawing the minimum rectangular frames of the overall corn ear outline after marking to obtain rectangular corn ear data, wherein the rectangular corn ear data comprise the length of the minimum rectangular frames and the width of the minimum rectangular frames; basic phenotype data of corn ear rectangular data are obtained; and carrying out transformation operation on the basic phenotype data according to the grading standard to obtain a target detection result. Compared with the prior art, the application has the advantages that the obtained basic phenotype data is up to 50, and the application can effectively cover the character 30 in the standard of GB/T19557.24-2018 plant variety specificity, consistency and stability test guideline corn: ear length, trait 31: ear diameter, trait 33: ear shape, trait 34: number of color and shape of cluster seeds 35: the yellow degree of the cluster seeds and the characteristics 40: the method can effectively solve the technical problem that all properties cannot be considered in the traditional new plant variety testing method due to the requirements of main color at the top end of the seed, length of bald tip and number of seeds in each row, and can keep the original value error within a range of 5% and the grading accuracy is up to 100%.
In some optional implementations of this embodiment, the base phenotype data includes color analysis data, plant area data, and quarter width data, and step S208 specifically includes: step S301, step S302, and step S303.
In step S301, performing color analysis operation on the corn ear rectangular data to obtain color analysis data;
in step S302, performing an area extraction operation on rectangular data of corn ears to obtain plant area data;
in step S303, a quarter width extraction operation is performed on the corn ear rectangular data, and quarter width data is obtained.
In the embodiment of the application, the quarter width extraction operation refers to drawing the fruit width at positions 1/4, 1/2 and 3/4 of the fruit (the fruit shape can be judged according to the length-width ratio and the like).
In some optional implementations of this embodiment, after step S201, before step S202, the electronic device may further perform: step S401, the step S202 includes: step S402.
In step S401, performing image correction operation on the corn ear outer surface image according to the eight-color scale, to obtain corrected image data;
in step S402, gaussian smoothing operation is performed on the corrected image data to obtain a gaussian-smoothed image.
In some optional implementations of this embodiment, the step S401 specifically includes: step S501, step S502, and step S503.
In step S501, acquiring real-time shooting optical three primary colors of a plant to be detected in real time from an image of the outer surface of a corn ear;
in step S502, color patch information of an eight-color scale is obtained, and target color patch information corresponding to the real photographing optical three primary colors is obtained in the color patch information;
in step S503, nonlinear fitting is performed on the real-time optical three primary colors according to the target patch information, to obtain corrected image data.
In the embodiment of the application, color correction refers to identifying color blocks of different colors in a scale, and acquiring average values (actual values) of r, g and b of standard color blocks in an input image, wherein the average values are used for carrying out nonlinear fitting with standard values to carry out image correction.
In some optional implementations of this embodiment, the step S401 may further include: step S601 and step S602.
In step S601, acquiring the true beat average diameter in the corn ear outer surface image according to an eight-color scale;
in step S602, pixel-scale conversion operation is performed on the real beat average diameter according to the standard diameter, to obtain corrected image data.
In practical application, when corn ears are manually placed for photographing, nonlinear fitting and pixel scale transformation are performed by using eight color circle parameters on a scale, and color and size correction are performed on an original input image respectively. Firstly, gaussian smoothing is carried out on the image, downsampling is carried out, multi-scale description is carried out on the image, and image information is fully utilized. Color separation is carried out on the ROI (Region of Interest) area of the whole ear and the ROI area of the ear seeds in the image according to the absorption specificity of the RGB components in the image through color deconvolution algorithm analysis (Colour Deconvolution), threshold and condition screening are carried out on the ear profile set, the whole corn ear profile is drawn, the label number of the drawn corn ear is given, the unoriented minimum rectangular frame is drawn (the width of each part of four parts is extracted), and meanwhile, the ear length and the ear diameter value are obtained. The middle part of the cluster is selected, and the cluster is subjected to color analysis (color analysis under HSV space). And (3) carrying out normalization (normal) and function conversion (convertScaleAbs) on the preprocessed image to enhance the image contrast, and combining a distance conversion algorithm and a watershed algorithm to complete the segmentation and identification of the cluster seeds, and marking and counting the cluster seeds to obtain row average seed number data. 50 pieces of basic phenotype data were obtained and generated in total, and transformed into trait 30 in the "GB/T19557.24-2018 plant variety specificity, consistency and stability test guide maize" standard according to the grading standard: ear length, trait 31: ear diameter, trait 33: ear shape, trait 34: number of color and shape of cluster seeds 35: the yellow degree of the cluster seeds and the characteristics 40: the main color of the top of the seed, the length of the bald tip and the number of the seeds in each row are adopted, so that the problems of large manual measurement error, low efficiency, high cost and the like in the traditional plant variety testing method are effectively solved. The method can effectively save time by 90% and above, the error of the original value is within 5%, and the grading accuracy is up to 100%.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions. Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
Example two
With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an image analysis apparatus using the external surface of corn ears, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 3, the image analysis apparatus 200 of the present embodiment using the external surface of the corn ear includes: acquisition module 210, gaussian smoothing module 220, downsampling module 230, color separation module 240, threshold screening module 250, contouring module 260, labeling module 270, phenotype data acquisition module 280, and data conversion module 290. Wherein:
an acquisition module 210, configured to acquire an image of an outer surface of a corn ear to be detected;
the Gaussian smoothing module 220 is used for performing Gaussian smoothing operation on the corn ear outer surface image to obtain a Gaussian smoothed image;
the downsampling module 230 is configured to perform a downsampling operation on the gaussian smooth image to obtain a downsampled image;
the color separation module 240 is configured to perform a color separation operation on the downsampled image according to a color deconvolution algorithm analysis to obtain a cluster contour set, where the cluster contour set includes overall ROI contour data of an entire cluster and kernel ROI contour data of a cluster kernel;
the threshold screening module 250 is configured to perform a threshold screening operation on the cluster profile set to obtain screening profile data;
the profile drawing module 260 is configured to perform overall drawing operation according to the screening profile data, so as to obtain an overall profile of the corn ear;
the marking module 270 is configured to number and mark the overall corn ear contour, and draw the minimum rectangular frame of the overall corn ear contour after marking, respectively, to obtain rectangular corn ear data, where the rectangular corn ear data includes the length of the minimum rectangular frame and the width of the minimum rectangular frame;
a phenotype data acquisition module 280 for acquiring basic phenotype data of corn ear rectangular data;
the data conversion module 290 is configured to perform a conversion operation on the basic phenotype data according to the grading standard, so as to obtain a target detection result.
In this embodiment, there is provided an image analysis device 200 using the external surface of corn ears, comprising: an acquisition module 210, configured to acquire an image of an outer surface of a corn ear to be detected; the Gaussian smoothing module 220 is used for performing Gaussian smoothing operation on the corn ear outer surface image to obtain a Gaussian smoothed image; the downsampling module 230 is configured to perform a downsampling operation on the gaussian smooth image to obtain a downsampled image; the color separation module 240 is configured to perform a color separation operation on the downsampled image according to a color deconvolution algorithm analysis to obtain a cluster contour set, where the cluster contour set includes overall ROI contour data of an entire cluster and kernel ROI contour data of a cluster kernel; the threshold screening module 250 is configured to perform a threshold screening operation on the cluster profile set to obtain screening profile data; the profile drawing module 260 is configured to perform overall drawing operation according to the screening profile data, so as to obtain an overall profile of the corn ear; the marking module 270 is configured to number and mark the overall corn ear contour, and draw the minimum rectangular frame of the overall corn ear contour after marking, respectively, to obtain rectangular corn ear data, where the rectangular corn ear data includes the length of the minimum rectangular frame and the width of the minimum rectangular frame; a phenotype data acquisition module 280 for acquiring basic phenotype data of corn ear rectangular data; the data conversion module 290 is configured to perform a conversion operation on the basic phenotype data according to the grading standard, so as to obtain a target detection result. Compared with the prior art, the application has the advantages that the obtained basic phenotype data is up to 50, and the application can effectively cover the character 30 in the standard of GB/T19557.24-2018 plant variety specificity, consistency and stability test guideline corn: ear length, trait 31: ear diameter, trait 33: ear shape, trait 34: number of color and shape of cluster seeds 35: the yellow degree of the cluster seeds and the characteristics 40: the method can effectively solve the technical problem that all properties cannot be considered in the traditional new plant variety testing method due to the requirements of main color at the top end of the seed, length of bald tip and number of seeds in each row, and can keep the original value error within a range of 5% and the grading accuracy is up to 100%.
In some alternative implementations of the present embodiment, the base phenotype data includes color analysis data, plant area data, and quarter width data, and the phenotype data acquisition module 280 includes: color analysis submodule, area extraction submodule and quarter width extraction submodule, wherein:
the color analysis sub-module is used for performing color analysis operation on the corn ear rectangular data to obtain color analysis data;
the area extraction sub-module is used for carrying out area extraction operation on the rectangular data of the corn ears to obtain plant area data;
and the quarter width extraction submodule is used for carrying out quarter width extraction operation on the corn ear rectangular data to obtain quarter width data.
In some optional implementations of this embodiment, the image analysis device 200 that uses the external surface of the corn ear further includes: the image correction module, the gaussian smoothing module 220 includes: gao Siping slide sub-module, wherein:
the image correction module is used for performing image correction operation on the corn ear outer surface image according to the eight-color scale to obtain corrected image data;
gao Siping sliding sub-module is used for carrying out Gaussian smoothing operation on the corrected image data to obtain Gaussian smoothed image.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only computer device 6 having components 61-63 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 61 includes at least one type of readable storage media including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal memory unit of the computer device 6 and an external memory device. In this embodiment, the memory 61 is generally used for storing an operating system and various application software installed on the computer device 6, such as computer readable instructions for applying an image analysis method of the external surface of corn ears. Further, the memory 61 may be used to temporarily store various types of data that have been output or are to be output.
The processor 62 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute computer readable instructions stored in the memory 61 or process data, such as computer readable instructions for executing the image analysis method using the external surface of the corn ear.
The network interface 63 may comprise a wireless network interface or a wired network interface, which network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.
The computer equipment provided by the application can obtain up to 50 basic phenotype data, and can effectively cover the 'trait 30 in the standard of maize of GB/T19557.24-2018 plant variety specificity, consistency and stability test guidelines': ear length, trait 31: ear diameter, trait 33: ear shape, trait 34: number of color and shape of cluster seeds 35: the yellow degree of the cluster seeds and the characteristics 40: the method can effectively solve the technical problem that all properties cannot be considered in the traditional new plant variety testing method due to the requirements of main color at the top end of the seed, length of bald tip and number of seeds in each row, and can keep the original value error within a range of 5% and the grading accuracy is up to 100%.
The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the image analysis method as described above using the external surface of a corn ear.
The computer readable storage medium provided by the application can obtain up to 50 basic phenotype data, and can effectively cover 'trait 30 in GB/T19557.24-2018 plant variety specificity, consistency and stability test guide corn' in the standard: ear length, trait 31: ear diameter, trait 33: ear shape, trait 34: number of color and shape of cluster seeds 35: the yellow degree of the cluster seeds and the characteristics 40: the method can effectively solve the technical problem that all properties cannot be considered in the traditional new plant variety testing method due to the requirements of main color at the top end of the seed, length of bald tip and number of seeds in each row, and can keep the original value error within a range of 5% and the grading accuracy is up to 100%.
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 application 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) 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 application.
It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.
Claims (10)
1. An image analysis method using the outer surface of corn ears is characterized by comprising the following steps:
acquiring an image of the outer surface of a corn ear to be detected;
carrying out Gaussian smoothing operation on the corn ear outer surface image to obtain a Gaussian smoothing image;
performing downsampling operation on the Gaussian smooth image to obtain a downsampled image;
performing color separation operation on the downsampled image according to a color deconvolution algorithm analysis to obtain a cluster contour set, wherein the cluster contour set comprises integral ROI contour data of the whole cluster and grain ROI contour data of the cluster grains;
threshold screening operation is carried out on the cluster profile set, and screening profile data are obtained;
carrying out overall drawing operation according to the screening profile data to obtain the overall profile of the corn ears;
numbering and marking the whole corn ear outline, and respectively drawing a minimum rectangular frame of the marked whole corn ear outline to obtain corn ear rectangular data, wherein the corn ear rectangular data comprises the length of the minimum rectangular frame and the width of the minimum rectangular frame;
basic phenotype data of the corn ear rectangular data are obtained;
and carrying out transformation operation on the basic phenotype data according to the grading standard to obtain a target detection result.
2. The method of claim 1, wherein the basic phenotype data comprises color analysis data, plant area data and quarter width data, and the step of obtaining basic phenotype data of the corn ear rectangular data comprises the steps of:
performing color analysis operation on the corn ear rectangular data to obtain color analysis data;
performing area extraction operation on the corn ear rectangular data to obtain plant area data;
and carrying out quarter width extraction operation on the corn ear rectangular data to obtain quarter width data.
3. The method for analyzing an image using an external surface of a corn ear according to claim 1, wherein after said step of obtaining an external surface image of a corn ear to be detected and before said step of performing a gaussian smoothing operation on said external surface image of a corn ear, further comprising the steps of:
performing image correction operation on the corn ear outer surface image according to the eight-color scale to obtain corrected image data;
the step of performing Gaussian smoothing operation on the corn ear outer surface image to obtain a Gaussian smoothed image specifically comprises the following steps:
and carrying out Gaussian smoothing operation on the corrected image data to obtain the Gaussian smoothed image.
4. The method for analyzing images of external surfaces of corn ears according to claim 3, wherein the step of performing image correction operation on the images of external surfaces of corn ears according to an eight-color scale to obtain corrected image data comprises the steps of:
acquiring real-time shooting optical three primary colors of a plant to be detected in real time from the corn ear outer surface image;
acquiring color block information of the eight-color scale, and acquiring target color block information corresponding to the real photographing optical three primary colors in the color block information;
and carrying out nonlinear fitting on the three primary colors of the real shooting optics according to the target color block information to obtain the corrected image data.
5. The method for analyzing images using the outer surface of corn ears according to claim 3, wherein the step of performing image correction operation on the real shot image data according to the eight-color scale to obtain corrected image data comprises the steps of:
acquiring the real beat average diameter in the corn ear outer surface image according to the eight color scale;
and carrying out pixel-scale conversion operation on the real beat average diameter according to the standard diameter to obtain the corrected image data.
6. An image analysis device using the outer surface of corn ears, comprising:
the acquisition module is used for acquiring an image of the outer surface of the corn ear to be detected;
the Gaussian smoothing module is used for carrying out Gaussian smoothing operation on the corn ear outer surface image to obtain a Gaussian smoothing image;
the downsampling module is used for performing downsampling operation on the Gaussian smooth image to obtain a downsampled image;
the color separation module is used for performing color separation operation on the downsampled image according to color deconvolution algorithm analysis to obtain a cluster contour set, wherein the cluster contour set comprises overall RO I contour data of the whole cluster and grain ROI contour data of the cluster grains;
the threshold screening module is used for carrying out threshold screening operation on the cluster profile set to obtain screening profile data;
the profile drawing module is used for carrying out overall drawing operation according to the screening profile data to obtain the overall profile of the corn ears;
the marking module is used for numbering and marking the whole corn ear outline and respectively drawing the minimum rectangular frame of the whole corn ear outline after marking to obtain corn ear rectangular data, wherein the corn ear rectangular data comprise the length of the minimum rectangular frame and the width of the minimum rectangular frame;
the phenotype data acquisition module is used for acquiring basic phenotype data of the corn ear rectangular data;
and the data conversion module is used for carrying out conversion operation on the basic phenotype data according to the grading standard to obtain a target detection result.
7. The image analysis device for applying the outer surface of a corn ear according to claim 6, wherein the basic phenotype data includes color analysis data, plant area data, and quarter width data, and the phenotype data acquisition module includes:
the color analysis sub-module is used for performing color analysis operation on the corn ear rectangular data to obtain color analysis data;
the area extraction sub-module is used for carrying out area extraction operation on the corn ear rectangular data to obtain plant area data;
and the quarter width extraction submodule is used for carrying out quarter width extraction operation on the corn ear rectangular data to obtain quarter width data.
8. The image analysis device for applying the outer surface of a corn ear according to claim 6, further comprising: an image correction module, the gaussian smoothing module comprising: gao Siping slide sub-module, wherein:
the image correction module is used for performing image correction operation on the corn ear outer surface image according to the eight-color scale to obtain corrected image data;
and the Gao Siping sliding sub-module is used for carrying out Gaussian smoothing operation on the corrected image data to obtain the Gaussian smoothed image.
9. A computer device comprising a memory having stored therein computer readable instructions which when executed by the processor perform the steps of the image analysis method of any one of claims 1 to 5 using the outer surface of a corn ear.
10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor perform the steps of the image analysis method of any one of claims 1 to 5 using the outer surface of corn ears.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117911294A (en) * | 2024-03-18 | 2024-04-19 | 浙江托普云农科技股份有限公司 | Corn ear surface image correction method, system and device based on vision |
| CN117911294B (en) * | 2024-03-18 | 2024-05-31 | 浙江托普云农科技股份有限公司 | Corn ear surface image correction method, system and device based on vision |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117911294A (en) * | 2024-03-18 | 2024-04-19 | 浙江托普云农科技股份有限公司 | Corn ear surface image correction method, system and device based on vision |
| CN117911294B (en) * | 2024-03-18 | 2024-05-31 | 浙江托普云农科技股份有限公司 | Corn ear surface image correction method, system and device based on vision |
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