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 PDF

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CN112577956A
CN112577956A CN202011409929.1A CN202011409929A CN112577956A CN 112577956 A CN112577956 A CN 112577956A CN 202011409929 A CN202011409929 A CN 202011409929A CN 112577956 A CN112577956 A CN 112577956A
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陈超
梁云东
戴孟初
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China Agricultural University
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China Agricultural University
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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
    • G01N21/84—Systems specially adapted for particular applications
    • 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
    • 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
    • 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
    • G01N21/84—Systems specially adapted for particular applications
    • G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits

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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

Corn seed test system and method based on intelligent device photographing function
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)

1.一种基于图像处理的玉米考种系统，其特征在于：包含穗粗测量模块(1)，图像采集模块(2)，图像处理模块(3)，数据存储模块(4)和数据显示模块(5)，穗粗测量模块(1)测量穗粗实际值并传输至图像处理模块(3)，图像采集模块(2)采集玉米果穗的不同视角图像，进而发送至图像处理模块(3)，图像处理模块(3)对玉米果穗图像进行处理得出玉米果穗的性状特征参数，进一步发送至数据存储模块(4)和数据显示模块(5)进行存储和显示。1. a corn seed testing system based on image processing, is characterized in that: comprise ear thickness measurement module (1), image acquisition module (2), image processing module (3), data storage module (4) and data display module (5), the ear thickness measurement module (1) measures the actual value of ear thickness and transmits it to the image processing module (3), and the image acquisition module (2) collects images of corn ears from different perspectives, and then sends them to the image processing module (3), The image processing module (3) processes the corn ear image to obtain the characteristic characteristic parameters of the corn ear, which are further sent to the data storage module (4) and the data display module (5) for storage and display. 2.根据权利要求1所述的一种基于图像处理的玉米考种系统，其特征在于：还可以包括产量评估模块(6)和数据输入模块(7)，数据输入模块(7)接收输入信息，输入信息至少包括玉米种植面积及玉米种植密度，进而基于图像处理模块(3)输出的玉米果穗的性状特征参数，计算得到产量预估值。2. a kind of corn seed testing system based on image processing according to claim 1, is characterized in that: also can comprise yield evaluation module (6) and data input module (7), data input module (7) receives input information , the input information includes at least corn planting area and corn planting density, and then based on the characteristic parameters of the corn ear output by the image processing module (3), the estimated output value is calculated and obtained. 3.一种基于图像处理的玉米考种方法，其特征在于：3. a kind of corn test method based on image processing, is characterized in that: 包括以下步骤：Include the following steps: 第一步：将玉米果穗平铺于平整地面；Step 1: Lay the corn ear on a flat ground; 第二步：穗粗测量模块测量玉米果穗穗粗实际值；The second step: the ear thickness measurement module measures the actual value of the ear thickness of the corn ear; 第三步：对玉米果穗进行多次不同角度等距离平行拍摄；拍摄不少于三张的不同视角的图像，以包含玉米果穗上的所有籽粒；Step 3: Take multiple parallel shots at different angles and equidistant distances on the corn ear; take no less than three images from different perspectives to include all the grains on the corn ear; 第四步：将采集到图像传送至图像处理模块；Step 4: Send the captured image to the image processing module; 第五步：对玉米果穗图像进行处理分析，提取玉米果穗的性状特征参数。The fifth step: processing and analyzing the corn ear image, and extracting the character characteristic parameters of the corn ear. 4.根据权利要求3所述的一种基于图像处理的玉米考种方法，其特征在于：该方法还包括产量预估步骤：输入玉米种植面积及玉米种植密度，进而基于玉米果穗的性状特征参数，计算得到产量预估值。4. a kind of corn seed testing method based on image processing according to claim 3, is characterized in that: this method also comprises output estimation step: input corn planting area and corn planting density, and then based on the character characteristic parameter of corn ear , and calculate the estimated output value. 5.根据权利要求3所述的一种基于图像处理的玉米考种方法，其特征在于：所述第五步中提取玉米果穗的性状特征参数，具体包括如下步骤：5. a kind of corn seed testing method based on image processing according to claim 3, is characterized in that: in the described 5th step, extract the character characteristic parameter of corn ear, specifically comprises the following steps: 第1步：求取果穗穗长、果穗面积、果穗周长；将原始图像进行二值化处理，并进行去除小区域、填充操作后，利用ROI方法提取果穗轮廓，计算图像穗长，根据已建立数学关系求得实际果穗实际穗长，对果穗图像像素值求和得果穗图像面积根据已建立数学关系求得果穗实际面积，对果穗轮廓边缘像素值求和得果穗图像周长，根据已建立数学关系求得果穗实际面积；Step 1: Obtain the ear length, ear area, and ear perimeter; binarize the original image, remove small areas and fill it, use the ROI method to extract the ear contour, and calculate the image ear length. Establish a mathematical relationship to obtain the actual ear length of the actual ear, and sum the pixel values of the ear image to obtain the area of the ear image. According to the established mathematical relationship, the actual area of the ear is obtained, and the pixel values of the edge of the ear contour are summed to obtain the perimeter of the ear image. Mathematical relationship to obtain the actual area of the ear; 第2步：计算果穗秃尖长度并去除；提取果穗原始图像中间2-3行部分的纹理特征和RGB颜色值，根据果穗RGB颜色值和纹理特征以及穗轮廓宽度、识别果穗秃尖位置，计算秃尖长度并去除；Step 2: Calculate the length of the bald tip of the ear and remove it; extract the texture features and RGB color values of the middle 2-3 lines of the original image of the ear, and calculate Bald tip length and removal; 第3步：对去除秃尖后的果穗原始图像进行图像预处理；包括灰度化、低通滤波、形态学开操作以及图像重建处理，结合颜色特征利用分割图像，填充图像并去除小颗粒；Step 3: Perform image preprocessing on the original image of the ear after removing the bald tip; including grayscale, low-pass filtering, morphological opening, and image reconstruction processing, combining color features to segment the image, filling the image and removing small particles; 第4步：将图像进行图像合并处理，求得玉米籽粒总数；Step 4: Combine the images to obtain the total number of corn kernels; 第5步：计算玉米果穗行数、穗行粒数和粒厚；提取玉米果穗中间三行玉米籽粒，计算玉米果穗行数、穗行粒数和图像粒厚，根据已建立数学关系求得实际粒厚。Step 5: Calculate the number of corn ear rows, the number of grains per row and the thickness of the grains; extract the three rows of corn kernels in the middle of the corn ear, calculate the number of corn ear rows, the number of grains per row and the image grain thickness, and obtain the actual number of corn ears according to the established mathematical relationship. Grain thick. 6.一种基于智能设备拍照功能的玉米考种装置，其特征在于：包括具有拍照功能的智能设备、安装在智能设备中的玉米考种软件，所述的玉米考种软件处理智能设备拍摄的玉米果穗图像，通过图像处理获取玉米果穗的性状特征参数，包括但不限于玉米穗长、穗粗、穗面积、穗周长、穗行数、穗行粒数、粒厚、籽粒总数、秃尖长。6. a corn seed testing device based on the photographing function of intelligent equipment, is characterized in that: comprise the intelligent equipment with photographing function, the corn seed testing software installed in the intelligent equipment, and the corn seed testing software processes the information taken by the intelligent equipment. Corn ear image, the characteristic parameters of corn ear are obtained through image processing, including but not limited to corn ear length, ear thickness, ear area, ear circumference, ear row number, ear row number of grains, grain thickness, total number of grains, bald tip long. 7.根据权利要求6所述的一种基于智能设备拍照功能的玉米考种装置，其特征在于：所述的具有拍照功能的智能设备为智能手机或智能平板。7 . The corn seed testing device based on the photographing function of a smart device according to claim 6 , wherein the smart device with the photographing function is a smart phone or a smart tablet. 8 . 8.根据权利要求6所述的一种基于智能设备拍照功能的玉米考种装置，其特征在于：所述的玉米考种软件设有账号注册和密码登录系统。8 . The corn seed testing device based on the photographing function of an intelligent device according to claim 6 , wherein the corn seed testing software is provided with an account registration and password login system. 9 . 9.根据权利要求6所述的一种基于智能设备拍照功能的玉米考种装置，其特征在于：所述的玉米考种软件设有输入窗口，输入信息包括但不限于玉米品种名，系统自动进行编号；9. a kind of corn seed testing device based on intelligent equipment photographing function according to claim 6, it is characterized in that: described corn seed testing software is provided with input window, input information includes but not limited to corn variety name, the system automatically be numbered; 所述的玉米考种软件对图像处理后的数据进行存储和显示，包括但不限于穗长、穗粗、穗长宽比、穗行数、穗行粒数、穗列数，穗列粒数，籽粒总数，秃尖长；The described corn seed testing software stores and displays the data after image processing, including but not limited to ear length, ear thickness, ear length-width ratio, ear row number, ear row grain number, ear column number, ear column grain number , total number of kernels, length of bald tip; 所述的玉米考种软件具有产量预估模块，通过输入窗口输入玉米种植面积及玉米种植密度，系统计算并显示预估产量。The described corn seed testing software has a yield estimation module, and the corn planting area and the corn planting density are input through the input window, and the system calculates and displays the estimated yield.
CN202011409929.1A 2020-12-04 2020-12-04 Corn seed test system and method based on intelligent device photographing function Pending CN112577956A (en)

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