CN112577956A - Corn seed test system and method based on intelligent device photographing function - Google Patents
Corn seed test system and method based on intelligent device photographing function Download PDFInfo
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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Abstract
The invention discloses a corn test system based on image processing, which comprises a rough corn measurement module, an image acquisition module, an image processing module, a data storage module and a data display module, wherein the image acquisition module acquires images of different visual angles of corn ears and then sends the images to the image processing module, the image processing module processes the images of the corn ears to obtain characteristic parameters of the corn ears, including but not limited to the length of the corn ears, the rough corn ears, the area of the corn ears, the perimeter of the corn ears, the number of the corn ears, the grain thickness, the total number of grains and the length of bald tips, and the characteristic parameters are further sent to the data storage module and the data display module for storage and display. The invention is based on the photographing function of portable intelligent equipment (such as a smart phone or a smart tablet), is convenient to carry and simple to operate, does not need special photographing or scanning equipment, corn clamping equipment and the like, can carry out real-time on-site measurement, does not need to bring a large amount of corn ears back to a laboratory for measurement, and greatly shortens the measurement period.
Description
Technical Field
The invention relates to the technical field of corn test, in particular to a corn test system and method based on a photographing function of intelligent equipment.
Background
Corn is three main grain products, can also be used as feed and industrial raw materials, is a very important agricultural crop in life and production of people, the physical properties of corn kernels and corn ears are closely related to the yield, and the yield of the corn is determined by selecting a proper corn variety according to the physical properties, so the corn seed test work is very important for the excellent seed selection of the corn. The corn seed test comprises the measurement of the properties of corn ears, such as ear thickness, ear length-width ratio, ear row number, ear row grain number, ear type, bald tip length, grain thickness, ear weight and the like, and the detection of the physical properties of the corn at present is mainly completed by manpower. But the workload of data acquisition is large, and the manual seed test speed is low, the efficiency is low, and the accuracy is poor. The seed examination mode using mechanical automatic detection can reduce the labor amount of breeding workers, improve the working efficiency and ensure the accuracy of measured data.
At present, the detection of important quality characteristics such as surface defects and damages of agricultural products, size, surface color and the like mostly adopts a machine vision mode, researchers detect property parameters of corn ears by utilizing the machine vision, place a single corn ear on a rotating platform to drive the corn ear to rotate to form assembly line operation, shoot each side surface of the corn ear by a camera and perform image splicing, the method can detect each side surface of the corn ear in a nondestructive mode, but equipment is complex, cost is high, online rapid detection cannot be performed, a scanner is adopted to collect images, speed is low, and corn grains are damaged. In addition, all existing corn test systems and methods need to work indoors, cannot perform on-site measurement, have too long measurement period from the corn field to the laboratory, cannot rapidly grasp detailed information of corn ears in real time, and have complicated and high-cost test system equipment.
Disclosure of Invention
The invention provides a corn test system and a method based on an intelligent device photographing function, and solves the problems that in the prior art, devices are complex and heavy, the cost is high, and the character characteristic parameters of corn ears cannot be measured in real time on site.
A kind of corn test system based on image processing, include ear of grain thick measuring module 1, image acquisition module 2, image processing module 3, data storage module 4 and data display module 5, ear of grain thick measuring module 1 utilizes the gauge comparison corn ear to get the ear of grain thick value directly, set up the mathematical relation between actual corn ear of grain and the image of corn ear; the image acquisition module 2 acquires images of different viewing angles of the corn ears and then sends the images to the image processing module 3, the image processing module 3 processes the images of the corn ears to obtain characteristic parameters of the corn ears, and the characteristic parameters are further sent to the data storage module 4 and the data display module 5 to be stored and displayed.
The ear thickness measuring module 1 has a measuring scale function, and compares the ear thickness value with the ear of corn directly by using the measuring scale to conveniently establish a mathematical relationship between an actual value and an image processing value, and further calculates characteristic parameters of the ear of corn by establishing the mathematical relationship and the data value obtained by image processing, including but not limited to actual ear length, bald tip length, ear area, ear circumference and grain thickness.
Optionally, the system can further comprise a yield evaluation module 6 and a data input module 7, wherein the data input module 7 receives input information at least comprising corn planting area and corn planting density, and further calculates a yield prediction value based on the characteristic parameters of the corn ears output by the image processing module 3, so that a user can conveniently make a cognitive judgment on the yield of the corn variety.
A corn test method based on image processing comprises the following steps:
the first step is as follows: flatly paving corn ears on a flat ground;
the second step is that: the ear thickness measuring module 1 measures the actual value of the ear thickness of the corn ear;
the third step: carrying out parallel shooting on corn ears at different angles for many times at equal intervals; at least three images with different visual angles are shot so as to contain all kernels on the corn ears;
the fourth step: transmitting the collected image to an image processing module 3;
the fifth step: and (4) processing and analyzing the corn ear image, and extracting the characteristic parameters of the corn ear.
Optionally, the method further comprises a yield estimation step: and inputting the corn planting area and the corn planting density, and calculating to obtain a yield predicted value based on the character characteristic parameters of the corn ears.
Further, the method for extracting the characteristic parameters of the corn ears in the fourth step comprises the following steps:
step 1: calculating the length of the cluster, the area of the cluster and the perimeter of the cluster; carrying out binarization processing on an original image, carrying out small Region removal and filling operation, then extracting a cluster outline by using a Region Of Interest (ROI) method, calculating the length Of the image cluster, obtaining the actual cluster length according to the established mathematical relationship, summing pixel values Of the cluster image to obtain the actual area Of the cluster according to the established mathematical relationship, summing the edge pixel values Of the cluster outline to obtain the perimeter Of the cluster image, and obtaining the actual area Of the cluster according to the established mathematical relationship;
step 2: calculating and removing the bald tip length of the fruit cluster; extracting texture features and RGB color values of the middle 2-3 lines of the original ear image, identifying the bald tip position of the ear according to the RGB color values and the texture features of the ear and the ear contour width, calculating the bald tip length and removing the bald tip;
and 3, step 3: carrying out image preprocessing on the cluster original image without the bald tip; the method comprises graying, low-pass filtering, morphological opening operation and image reconstruction processing, wherein an image is segmented by combining color characteristics, and the image is filled and small particles are removed;
and 4, step 4: carrying out image combination processing on the images to obtain the total number of the corn kernels;
and 5, step 5: calculating the row number, the grain number and the grain thickness of the corn ear rows; and extracting three lines of corn kernels in the middle of the corn ear, calculating the line number of the corn ear, the kernel number in the ear line and the kernel thickness of the image, and solving the actual kernel thickness according to the established mathematical relationship.
The utility model provides a kind of device is examined to maize based on smart machine function of shooing, includes the smart machine that has the function of shooing, installs the kind of software is examined to maize in the smart machine, kind of software is examined to maize handle the maize ear of grain image that the smart machine was shot, acquires the trait characteristic parameter of maize ear of grain through image processing, including but not limited to maize ear of grain length, ear of grain is thick, ear of grain area, ear of grain girth, ear of grain line number of grain, grain thickness, seed grain total number, bald tip length.
Optionally, the smart device with the photographing function is a smart phone or a smart tablet.
Optionally, the corn test software is provided with an account registration and password login system.
Optionally, the corn seed test software is provided with an input window, the input information includes, but is not limited to, the corn seed name, and the system automatically performs numbering.
Corn test software can call the function of shooing of smart machine, gather maize ear of grain image, corn test software save and show the data after image processing, like ear length, ear thickness, ear length-width ratio, ear row number of grains, ear column number of grains, seed grain total number, bald tip length.
The corn seed testing software is provided with a yield estimation module, the corn planting area and the corn planting density are input through an input window, and the estimated yield is calculated and displayed by a system.
The invention has the beneficial effects that: the invention is based on the photographing function of portable intelligent equipment (such as a smart phone or a smart tablet), is convenient to carry and simple to operate, does not need to carry a computer or other large-scale equipment, does not need special photographing or scanning equipment, corn clamping equipment and the like, and has low cost. In addition, the method can carry out real-time on-site measurement, a large amount of corn ears do not need to be brought back to a laboratory for measurement, the measurement period is greatly shortened, a user can establish a new functional module according to actual needs at any time, and the method is better in feasibility.
Drawings
FIG. 1 is a general block diagram of an embodiment of the present invention
FIG. 2 is a general block diagram of another embodiment of the present invention
FIG. 3 is a flow chart of a corn test method of the present invention
FIG. 4 is a detailed flow chart of the present invention for extracting characteristic parameters of corn ears
In the figure:
1. a coarse ear measuring module; 2. an image acquisition module; 3. an image processing module;
4. a data storage module; 5. a data display module; 6. a yield evaluation module;
7. and a data input module.
Detailed Description
The invention will be described in detail below with reference to the drawings,
as shown in fig. 1, the corn test system based on image processing provided by the invention comprises a rough ear measurement module 1, an image acquisition module 2, an image processing module 3, a data storage module 4 and a data display module 5, wherein the rough ear measurement module 1 measures a rough ear actual value and transmits the rough ear actual value to the image processing module 3, the image acquisition module 2 acquires images of different viewing angles of corn ears and further transmits the images to the image processing module 3, and the image processing module 3 processes the images of the corn ears to obtain characteristic parameters of the corn ears and further transmits the characteristic parameters to the data storage module 4 and the data display module 5 for storage and display.
As shown in fig. 2, the present invention may further include a yield evaluation module 6 and a data input module 7, where the data input module 7 receives input information, at least including corn planting area and corn planting density, and further calculates a yield prediction value based on the characteristic parameters of the corn ears output by the image processing module 3, so as to facilitate a user to make a cognitive judgment on the yield of the corn variety.
As shown in FIG. 3, the corn test method based on image processing of the invention comprises the following steps:
the first step is as follows: flatly paving corn ears on a flat ground;
the second step is that: the ear thickness measuring module measures the actual value of the ear thickness of the corn ear;
the third step: carrying out parallel shooting on corn ears at different angles for many times at equal intervals; at least three images with different visual angles are shot so as to contain all kernels on the corn ears;
the fourth step: transmitting the collected image to an image processing module 3;
the fifth step: and (4) processing and analyzing the corn ear image, and extracting the characteristic parameters of the corn ear.
Optionally, the method further comprises a yield estimation step: and inputting the corn planting area and the corn planting density, and calculating to obtain a yield predicted value based on the character characteristic parameters of the corn ears.
Further, as shown in fig. 4, the extracting of the trait characteristic parameters of the corn ears in the fourth step includes the following steps:
step 1: calculating the length of the cluster, the area of the cluster and the perimeter of the cluster; carrying out binarization processing on an original image, carrying out small region removal and filling operation, extracting a cluster outline by using an ROI (region of interest) method, calculating the length of the image cluster, obtaining the actual cluster length according to the established mathematical relationship, summing pixel values of the cluster image to obtain the area of the cluster image, obtaining the actual area of the cluster according to the established mathematical relationship, summing edge pixel values of the cluster outline to obtain the perimeter of the cluster image, and obtaining the actual area of the cluster according to the established mathematical relationship;
step 1: calculating the length of the cluster, the thickness of the cluster, the area of the cluster and the perimeter of the cluster; carrying out binarization processing on an original image, removing small regions and filling, extracting a cluster outline by using an ROI (region of interest) method, calculating the length of the cluster image, obtaining the actual length of the cluster according to an established mathematical relationship, calculating the thickness of the cluster by taking the average value of the width of the outline of the middle part, summing the pixel values of the cluster image to obtain the area of the cluster image, obtaining the actual area of the cluster according to the established mathematical relationship, summing the pixel values of the edge of the cluster outline to obtain the perimeter of the cluster image, and obtaining the actual perimeter of the cluster according to the established mathematical relationship;
step 2: calculating and removing the bald tip length of the fruit cluster; extracting texture features and RGB color values of the middle 2-3 lines of the original ear image, identifying the bald tip position of the ear according to the RGB color values and the texture features of the ear and the ear contour width, calculating the bald tip length and removing the bald tip;
and 3, step 3: carrying out image preprocessing on the cluster original image without the bald tip; the method comprises graying, low-pass filtering, morphological opening operation and image reconstruction processing, wherein the method comprises the steps of utilizing a segmented image in combination with color characteristics, filling the image and removing small particles;
and 4, step 4: carrying out image combination processing on the images to obtain the total number of the corn kernels;
and 5, step 5: calculating the row number, the grain number and the grain thickness of the corn ear rows; and extracting three lines of corn kernels in the middle of the corn ear, calculating the line number of the corn ear, the kernel number in the ear line and the kernel thickness of the image, and solving the actual kernel thickness according to the established mathematical relationship.
The utility model provides a kind of device is examined to maize based on smart machine function of shooing, includes the smart machine that has the function of shooing, installs the kind of software is examined to maize in the smart machine, kind of software is examined to maize handle the maize ear of grain image that the smart machine was shot, acquires the trait characteristic parameter of maize ear of grain through image processing, including but not limited to maize ear of grain length, ear of grain is thick, ear of grain area, ear of grain girth, ear of grain line number of grain, grain thickness, seed grain total number, bald tip length.
Optionally, the smart device with the photographing function is a smart phone or a smart tablet.
Optionally, the corn test software is provided with an account registration and password login system.
Optionally, the corn seed test software is provided with an input window, the input information includes, but is not limited to, the corn seed name, and the system automatically performs numbering.
The corn seed testing software can call the photographing function of the intelligent equipment to collect the corn ear images;
the corn test software stores and displays data after image processing, such as ear length, ear thickness, ear length-width ratio, ear row number, ear column number, total number of seeds and bald tip length.
The corn seed testing software is provided with a yield estimation module, the corn planting area and the corn planting density are input through an input window, and the estimated yield is calculated and displayed by a system.
The above embodiments are only used for illustrating the present invention, and the structure, connection mode, etc. of the components may be changed, and all equivalent changes and modifications based on the technical solution of the present invention should not be excluded from the protection scope of the present invention.
Claims (9)
1. The utility model provides a kind of system of maize examination based on image processing which characterized in that: contain thick measurement module of ear of grain (1), image acquisition module (2), image processing module (3), data storage module (4) and data display module (5), thick actual value of ear of grain is measured and is transmitted to image processing module (3) in thick measurement module of ear of grain (1), the different visual angle images of corn ear are gathered in image acquisition module (2), and then send to image processing module (3), image processing module (3) are handled the character characteristic parameter that reachs the corn ear to the corn ear image, further send to data storage module (4) and data display module (5) and save and show.
2. The image processing-based corn test system of claim 1, wherein: the corn ear yield prediction method based on the image processing module can further comprise a yield evaluation module (6) and a data input module (7), wherein the data input module (7) receives input information, the input information at least comprises corn planting area and corn planting density, and further a yield prediction value is calculated based on the characteristic parameters of the corn ears output by the image processing module (3).
3. A corn test method based on image processing is characterized in that:
the method comprises the following steps:
the first step is as follows: flatly paving corn ears on a flat ground;
the second step is that: the ear thickness measuring module measures the actual value of the ear thickness of the corn ear;
the third step: carrying out parallel shooting on corn ears at different angles for many times at equal intervals; at least three images with different visual angles are shot so as to contain all kernels on the corn ears;
the fourth step: transmitting the collected image to an image processing module;
the fifth step: and (4) processing and analyzing the corn ear image, and extracting the characteristic parameters of the corn ear.
4. The corn test method based on image processing as claimed in claim 3, wherein: the method also comprises a yield estimation step: and inputting the corn planting area and the corn planting density, and calculating to obtain a yield predicted value based on the character characteristic parameters of the corn ears.
5. The corn test method based on image processing as claimed in claim 3, wherein: the fifth step of extracting the characteristic parameters of the corn ears specifically comprises the following steps:
step 1: calculating the length of the cluster, the area of the cluster and the perimeter of the cluster; carrying out binarization processing on an original image, carrying out small region removal and filling operation, extracting a cluster outline by using an ROI (region of interest) method, calculating the length of the image cluster, obtaining the actual cluster length according to the established mathematical relationship, summing pixel values of the cluster image to obtain the area of the cluster image, obtaining the actual area of the cluster according to the established mathematical relationship, summing edge pixel values of the cluster outline to obtain the perimeter of the cluster image, and obtaining the actual area of the cluster according to the established mathematical relationship;
step 2: calculating and removing the bald tip length of the fruit cluster; extracting texture features and RGB color values of the middle 2-3 lines of the original ear image, identifying the bald tip position of the ear according to the RGB color values and the texture features of the ear and the ear contour width, calculating the bald tip length and removing the bald tip;
and 3, step 3: carrying out image preprocessing on the cluster original image without the bald tip; the method comprises graying, low-pass filtering, morphological opening operation and image reconstruction processing, wherein the method comprises the steps of utilizing a segmented image in combination with color characteristics, filling the image and removing small particles;
and 4, step 4: carrying out image combination processing on the images to obtain the total number of the corn kernels;
and 5, step 5: calculating the row number, the grain number and the grain thickness of the corn ear rows; and extracting three lines of corn kernels in the middle of the corn ear, calculating the line number of the corn ear, the kernel number in the ear line and the kernel thickness of the image, and solving the actual kernel thickness according to the established mathematical relationship.
6. The utility model provides a kind of device is examined to maize based on smart machine function of shooing which characterized in that: including the smart machine that has the function of shooing, install the maize software of examining in the smart machine, maize examine kind of software and handle the maize ear of grain image that the smart machine shot, acquire the trait characteristic parameter of maize ear through image processing, including but not limited to maize ear of grain length, ear of grain thick, ear area, ear of grain girth, ear of grain line number, grain thickness, seed grain total number, bald tip length.
7. The corn seed test device based on the intelligent device photographing function as claimed in claim 6, wherein: the intelligent device with the photographing function is an intelligent mobile phone or an intelligent tablet.
8. The corn seed test device based on the intelligent device photographing function as claimed in claim 6, wherein: the corn seed testing software is provided with an account registration and password login system.
9. The corn seed test device based on the intelligent device photographing function as claimed in claim 6, wherein: the corn seed test software is provided with an input window, input information includes but is not limited to corn seed names, and the system automatically numbers;
the corn test software stores and displays data after image processing, including but not limited to ear length, ear thickness, ear length-width ratio, ear row number, ear column number, total number of grains and bald tip length;
the corn seed testing software is provided with a yield estimation module, the corn planting area and the corn planting density are input through an input window, and the estimated yield is calculated and displayed by a system.
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Cited By (2)
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| CN115345880A (en) * | 2022-10-18 | 2022-11-15 | 浙江托普云农科技股份有限公司 | Corn ear character estimation method and device based on corn ear unilateral scanning map |
| CN118196639A (en) * | 2024-05-16 | 2024-06-14 | 吉林省中农阳光数据有限公司 | Corn yield estimation method and device |
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Application publication date: 20210330 |