CN113063704B - Particle fullness analysis platform and method - Google Patents
Particle fullness analysis platform and method Download PDFInfo
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Abstract
The invention relates to a particle plumpness analysis platform and a method, wherein the platform comprises: a random screening mechanism for randomly selecting individual corn cobs on a conveying belt for conveying the corn cobs and pushing the selected individual corn cobs into an inspection container below the instant insertion equipment; the inspection container is in a long-strip-shaped container structure and is used for keeping the single corn cob body in a vertical placement state; instant insertion equipment comprises a first driving motor and an insertion cantilever, wherein the top end of the insertion cantilever is connected with the first driving motor and used for being controlled by the first driving motor to move up and down, and the tail end of the insertion cantilever is a sharp plug-in. The particle fullness analyzing platform and the method are compact in design and intelligent in operation. A customized testing platform comprising a random screening mechanism, a test receptacle, an instant insertion device, and a data capture mechanism can be used to perform a targeted analysis of the grain fullness of individual corn cobs that are randomly screened.
Description
Technical Field
The invention relates to the field of crop inspection, in particular to a grain fullness analyzing platform and a grain fullness analyzing method.
Background
Crops are various plants cultivated in agriculture. Including grain crops and economic crops (oil crops, vegetable crops, flowers, grasses and trees). The food is taken by people as days, the relationship between people and food is expressed, and the reasonable meal collocation can bring health to people. The growth of crops can not be separated from scientific technological production technology and novel industrially manufactured mechanical equipment capable of assisting agricultural production.
For example, the soil for corn planting needs to be turned over deeply in autumn, and the low temperature in winter is used for killing parasites in the soil. The survival rate of the seedlings can be ensured only when the seedlings enter the sowing period. The simple seeding is to dig a proper pit with a pick on a high slope, pour a proper amount of water, dibble seeds after the water permeates into the ground surface, cultivate soil, slightly step with feet to finish the primary planting process, reasonably fertilize the edge of one side of the corn seedling according to different growth stages at the later stage, and finally pour a proper amount of water on the corn seedling. The large-scale large-area seeding needs scientific and reasonable preparation work before seeding, the corns belong to high-slope crops, furrows are needed during planting, and the corns are planted in the plane soil on the furrows and ridges. And (3) dibbling on ridges in a precise manner, generally, sowing and fertilizing operations are completed at one time, and the distance between seeds and fertilizer is kept at 5cm on the side and 3cm in depth.
In the prior art, as one of important crop products, the grain fullness degree of corn is a key factor for judging the quality of the corn. Generally, the individual grains of each corn cob are visually observed and manually classified in a manual mode, obviously, the inspection mode not only consumes a great deal of labor cost, but also takes a great deal of time, and more importantly, the inspection result is difficult to guarantee.
Disclosure of Invention
In order to solve the technical problems in the related field, the invention provides a grain fullness analyzing platform which can be used for carrying out targeted analysis on the grain fullness of a single corn cob body which is randomly screened by adopting a customized detection platform which comprises a random screening mechanism, a detection container, an instant insertion device and a data capturing mechanism.
Therefore, the invention needs to have the following three important points:
(1) a customized detection platform comprising a random screening mechanism, an inspection container, an instant insertion device and a data capturing mechanism is introduced to realize the random screening of a single corn cob body and the on-site capturing of the whole-body visual signals;
(2) analyzing the plump degree of a single corn cob by adopting a random screening mode so as to judge the quality degree of the corns in the batch in which the single corn cob is positioned;
(3) a targeted visual detection mechanism is introduced to detect the entity distribution area of each particle of the whole body of a single corn cob in real time, and key data are provided for judging the fullness degree of the single corn cob.
According to an aspect of the present invention, there is provided a particle turgor resolution platform, the platform comprising:
a random screening mechanism for randomly selecting individual corn cobs on a conveying belt for conveying the corn cobs and pushing the selected individual corn cobs into an inspection container below the instant insertion equipment;
the inspection container is in a long-strip-shaped container structure and is used for receiving the pushed single corn cobs so as to keep the single corn cobs in a vertical placement state;
the instant insertion equipment comprises a first driving motor and an insertion cantilever, wherein the top end of the insertion cantilever is connected with the first driving motor and used for being controlled by the first driving motor to move up and down, the tail end of the insertion cantilever is provided with a sharp plug-in unit which is used for inserting the single corn cob body when the insertion cantilever moves to the low position and driving the inserted single corn cob body to keep the single corn cob body in a suspended state when the insertion cantilever returns to the high position;
the data capturing mechanism is connected with the instant insertion equipment, is arranged on the side surface of the high position, and is used for performing circular motion on the horizontal plane around the single corn cob body when the single corn cob body is in a suspended state, and always keeping the lens area of the single corn cob body during the circular motion so as to capture a panoramic image corresponding to the single corn cob body and output the panoramic image as a whole corn cob body image;
the first filtering device is arranged near the inspection container, is wirelessly connected with the data capturing mechanism through a Bluetooth communication link, and is used for performing smooth spatial filtering processing on the received whole body image of the rod body so as to obtain and output a corresponding first filtering image;
the second filtering device is connected with the first filtering device and used for executing edge-preserving smooth filtering processing on the received first filtering image so as to obtain and output a corresponding second filtering image;
the content processing mechanism is connected with the second filtering equipment and used for executing artifact removing processing on the received second filtering image so as to obtain a corresponding content processing image;
a grain identifying device connected to the content processing mechanism, for identifying each corn grain pixel in the content processing image based on a distribution range of a yellow channel value of pixels constituting corn grains, and acquiring each corn grain object in the content processing image based on each corn grain pixel of the content processing image;
an information analysis mechanism, connected to the particle identification device, for estimating a physical distribution area of each corn particle object based on a number of pixels occupied by the corn particle object in the content-processed image and a depth value of the corn particle object in the content-processed image;
and the parameter judgment equipment is connected with the information analysis mechanism and is used for determining the full degree of the corn particles of the single corn cob body based on the distribution area of each entity of each corn particle object.
According to another aspect of the present invention, there is also provided a method of grain fullness analysis comprising targeted analysis of grain fullness of randomly screened individual corn cobs using a grain fullness analysis platform as described above to incorporate a customized detection platform comprising a random screening mechanism, a test receptacle, an instant insertion device, and a data capture mechanism.
The particle fullness analyzing platform and the method are compact in design and intelligent in operation. A customized testing platform comprising a random screening mechanism, a test receptacle, an instant insertion device, and a data capture mechanism can be used to perform a targeted analysis of the grain fullness of individual corn cobs that are randomly screened.
Detailed Description
Embodiments of the particle turgor resolution platform and method of the present invention are described in detail below.
The physical properties of the corn comprise the indexes of grain color, grain shape, seed coat luster, grain length, grain width, hundred grain weight, grain diameter, grain uniformity, hard rate and the like. Corn kernel color includes three parts, the seed coat, the aleurone layer (protein rich, also known as the protein layer), and the endosperm. In most cases, the endosperm of the mature corn kernel is yellow or white in color, and the seed coat and aleurone layer are colorless and transparent. The corn is divided into yellow corn, white corn and mixed corn according to the color of corn grains. According to the shape, hardness and different purposes of corn grains, the corn is divided into two types, namely common corn (hard grain type, intermediate type, dent type, hard horse type and hard horse type) and special corn (high-lysine corn, high-oil corn, sweet corn, cracked corn and waxy corn). The shape and size of the corn are different according to varieties, generally the corn is 8-12mm long, 7-10mm wide and 3-7mm thick, and if the difference between corn particles is too large, the corn is difficult to clean and break in the processing process.
In the prior art, as one of important crop products, the grain fullness degree of corn is a key factor for judging the quality of the corn. Generally, the individual grains of each corn cob are visually observed and manually classified in a manual mode, obviously, the inspection mode not only consumes a great deal of labor cost, but also takes a great deal of time, and more importantly, the inspection result is difficult to guarantee.
In order to overcome the defects, the invention builds a platform and a method for analyzing the full degree of the particles, and can effectively solve the corresponding technical problems.
A particle turgor resolution platform is shown according to an embodiment of the invention comprising:
a random screening mechanism for randomly selecting individual corn cobs on a conveying belt for conveying the corn cobs and pushing the selected individual corn cobs into an inspection container below the instant insertion equipment;
the inspection container is in a long-strip-shaped container structure and is used for receiving the pushed single corn cobs so as to keep the single corn cobs in a vertical placement state;
the instant insertion equipment comprises a first driving motor and an insertion cantilever, wherein the top end of the insertion cantilever is connected with the first driving motor and used for being controlled by the first driving motor to move up and down, the tail end of the insertion cantilever is provided with a sharp plug-in unit which is used for inserting the single corn cob body when the insertion cantilever moves to the low position and driving the inserted single corn cob body to keep the single corn cob body in a suspended state when the insertion cantilever returns to the high position;
the data capturing mechanism is connected with the instant insertion equipment, is arranged on the side surface of the high position, and is used for performing circular motion on the horizontal plane around the single corn cob body when the single corn cob body is in a suspended state, and always keeping the lens area of the single corn cob body during the circular motion so as to capture a panoramic image corresponding to the single corn cob body and output the panoramic image as a whole corn cob body image;
the first filtering device is arranged near the inspection container, is wirelessly connected with the data capturing mechanism through a Bluetooth communication link, and is used for performing smooth spatial filtering processing on the received whole body image of the rod body so as to obtain and output a corresponding first filtering image;
the second filtering device is connected with the first filtering device and used for executing edge-preserving smooth filtering processing on the received first filtering image so as to obtain and output a corresponding second filtering image;
the content processing mechanism is connected with the second filtering equipment and used for executing artifact removing processing on the received second filtering image so as to obtain a corresponding content processing image;
a grain identifying device connected to the content processing mechanism, for identifying each corn grain pixel in the content processing image based on a distribution range of a yellow channel value of pixels constituting corn grains, and acquiring each corn grain object in the content processing image based on each corn grain pixel of the content processing image;
an information analysis mechanism, connected to the particle identification device, for estimating a physical distribution area of each corn particle object based on a number of pixels occupied by the corn particle object in the content-processed image and a depth value of the corn particle object in the content-processed image;
and the parameter judgment equipment is connected with the information analysis mechanism and is used for determining the full degree of the corn particles of the single corn cob body based on the distribution area of each entity of each corn particle object.
Next, the specific structure of the particle turgor analysis platform of the present invention will be further described.
In the granule turgor resolution platform:
determining the fullness of corn kernels of said individual corn cob based on the individual entity distribution area of each corn kernel object comprises: the greater the mean value of the individual entity distribution areas for each corn grain object, the greater the turgor of corn grains determined for an individual corn cob.
In the granule turgor resolution platform:
estimating a physical distribution area of each corn grain object based on the number of pixels occupied by the corn grain object in the content-processed image and the depth of field value of each corn grain object in the content-processed image comprises: the greater the number of pixels each corn grain object occupies in the content-processed image, the greater the estimated physical distribution area of the corresponding corn grain object.
In the granule turgor resolution platform:
estimating a physical distribution area of each corn grain object based on the number of pixels occupied by the corn grain object in the content-processed image and the depth of field value of each corn grain object in the content-processed image comprises: the deeper the depth of field value of each corn grain object in the content-processed image, the greater the estimated entity distribution area of the corresponding corn grain object.
In the granule turgor resolution platform:
the distribution range of the yellow channel values of the pixels constituting the corn grain is the distribution range of the Y channel values, i.e., the yellow channel values, of the pixels constituting the corn grain in the CMYK color space.
In the granule turgor resolution platform:
the color channels of each pixel in the CMYK color space are respectively a C channel, i.e., cyan channel, an M channel, i.e., magenta channel, a Y channel, i.e., yellow channel, and a K channel, i.e., black channel.
The particle turgor resolution platform can further comprise:
and the second driving motor is connected with the data capturing mechanism and used for driving the data capturing mechanism to perform circular motion on a horizontal plane.
The particle turgor resolution platform can further comprise:
and the voltage conversion mechanism is respectively connected with the parameter judgment device, the information analysis mechanism and the particle identification device.
In the granule turgor resolution platform:
the voltage conversion mechanism is used for respectively providing power supply voltages required by respective work for the parameter judgment device, the information analysis mechanism and the particle identification device.
Meanwhile, in order to overcome the defects, the invention also establishes a grain fullness analyzing method, which comprises the step of using the grain fullness analyzing platform to introduce a customized detecting platform comprising a random screening mechanism, a detecting container, an instant inserting device and a data capturing mechanism to carry out targeted analysis on the grain fullness of the randomly screened single corn stick.
In addition, in the grain fullness analyzing platform, for the CMYK color space, the color is different feelings of human eyes to different frequencies of light, and the color is objectively present (light of different frequencies) and subjectively perceived, and has different recognition. The human perception of color has undergone a very long process, which has not been perfected until recently, but to date, human still cannot say that color is completely understood and accurately expressed, and many concepts are not so easy to understand. The term "color space" is originated from western "ColorSpace", also called "color gamut", in colorimetry, people establish a plurality of color models, and express a certain color by one-dimensional, two-dimensional, three-dimensional or even four-dimensional space coordinates, and the color range defined by the coordinate system is the color space. The color spaces that we often use are mainly RGB, CMYK, Lab, etc. Subtractive color mixing is used in CMYK printing processes because it describes what inks need to be used to show color through reflection of light. It uses ink to represent the image on a white medium (drawing board, page, etc.). CMYK describes the values of the four inks cyan, magenta, yellow and black. There are a variety of CMYK color spaces depending on the different inks, media, and print characteristics.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: Read-Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A particle turgor resolution platform, comprising:
a random screening mechanism for randomly selecting individual corn cobs on a conveying belt for conveying the corn cobs and pushing the selected individual corn cobs into an inspection container below the instant insertion equipment;
the inspection container is in a long-strip-shaped container structure and is used for receiving the pushed single corn cobs so as to keep the single corn cobs in a vertical placement state;
the instant insertion equipment comprises a first driving motor and an insertion cantilever, wherein the top end of the insertion cantilever is connected with the first driving motor and used for being controlled by the first driving motor to move up and down, the tail end of the insertion cantilever is provided with a sharp plug-in unit which is used for inserting the single corn cob body when the insertion cantilever moves to the low position and driving the inserted single corn cob body to keep the single corn cob body in a suspended state when the insertion cantilever returns to the high position;
the data capturing mechanism is connected with the instant insertion equipment, is arranged on the side surface of the high position, and is used for performing circular motion on the horizontal plane around the single corn cob body when the single corn cob body is in a suspended state, and always keeping the lens area of the single corn cob body during the circular motion so as to capture a panoramic image corresponding to the single corn cob body and output the panoramic image as a whole corn cob body image;
the first filtering device is arranged near the inspection container, is wirelessly connected with the data capturing mechanism through a Bluetooth communication link, and is used for performing smooth spatial filtering processing on the received whole body image of the rod body so as to obtain and output a corresponding first filtering image;
the second filtering device is connected with the first filtering device and used for executing edge-preserving smooth filtering processing on the received first filtering image so as to obtain and output a corresponding second filtering image;
the content processing mechanism is connected with the second filtering equipment and used for executing artifact removing processing on the received second filtering image so as to obtain a corresponding content processing image;
a grain identifying device connected to the content processing mechanism, for identifying each corn grain pixel in the content processing image based on a distribution range of a yellow channel value of pixels constituting corn grains, and acquiring each corn grain object in the content processing image based on each corn grain pixel of the content processing image;
an information analysis mechanism, connected to the particle identification device, for estimating a physical distribution area of each corn particle object based on a number of pixels occupied by the corn particle object in the content-processed image and a depth value of the corn particle object in the content-processed image;
the parameter judgment device is connected with the information analysis mechanism and is used for determining the full degree of the corn particles of the single corn cob body based on the distribution area of each entity of each corn particle object;
wherein determining the fullness of corn kernels for a single corn cob based on the respective entity distribution areas for the respective corn kernel objects comprises: the larger the mean value of the distribution area of each entity of each corn grain object, the higher the fullness of corn grains of a single corn cob is determined;
wherein estimating the entity distribution area of each corn grain object based on the number of pixels occupied by the corresponding corn grain object in the content-processed image and the depth of field value of each corn grain object in the content-processed image comprises: the greater the number of pixels each corn grain object occupies in the content-processed image, the greater the estimated physical distribution area of the corresponding corn grain object.
2. The particle turgor resolution platform of claim 1, wherein:
estimating a physical distribution area of each corn grain object based on the number of pixels occupied by the corn grain object in the content-processed image and the depth of field value of each corn grain object in the content-processed image comprises: the deeper the depth of field value of each corn grain object in the content-processed image, the greater the estimated entity distribution area of the corresponding corn grain object.
3. The particle turgor resolution platform of claim 2, wherein:
the distribution range of the yellow channel values of the pixels constituting the corn grain is the distribution range of the Y channel values, i.e., the yellow channel values, of the pixels constituting the corn grain in the CMYK color space.
4. The particle turgor resolution platform of claim 3, wherein:
the color channels of each pixel in the CMYK color space are respectively a C channel, i.e., cyan channel, an M channel, i.e., magenta channel, a Y channel, i.e., yellow channel, and a K channel, i.e., black channel.
5. The particle turgor resolution platform of claim 4, further comprising:
and the second driving motor is connected with the data capturing mechanism and used for driving the data capturing mechanism to perform circular motion on a horizontal plane.
6. The particle turgor resolution platform of claim 5, further comprising:
and the voltage conversion mechanism is respectively connected with the parameter judgment device, the information analysis mechanism and the particle identification device.
7. The particle turgor resolution platform of claim 6, wherein:
the voltage conversion mechanism is used for respectively providing power supply voltages required by respective work for the parameter judgment device, the information analysis mechanism and the particle identification device.
8. A method of grain fullness profiling comprising using the grain fullness profiling platform of any one of claims 1-7 to introduce a customized testing platform comprising a random screening mechanism, a test receptacle, an immediate insertion device, and a data capture mechanism to perform a targeted analysis of the grain fullness of individual corn cobs randomly screened.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101957313A (en) * | 2010-09-21 | 2011-01-26 | 吉林大学 | Method and device for computer visual inspection classification of quality of fresh corn ears |
| CN104197866A (en) * | 2014-07-28 | 2014-12-10 | 山东农业大学 | Method for quantitative determination of cutin to starch percentage of corn kernel cross section |
| CN104598908A (en) * | 2014-09-26 | 2015-05-06 | 浙江理工大学 | Method for recognizing diseases of crop leaves |
| CN105806401A (en) * | 2016-03-03 | 2016-07-27 | 中国农业大学 | Three-dimensional image detection system used for indoor quick seed test of maize ears |
| CN106127692A (en) * | 2016-02-25 | 2016-11-16 | 哈尔滨理工大学 | Can be used for the bilateral regression filtering method of gray scale and coloured image |
| CN106296644A (en) * | 2015-06-10 | 2017-01-04 | 浙江托普云农科技股份有限公司 | Method is analyzed in a kind of corn kernel species test based on image procossing |
| CN108257136A (en) * | 2018-02-09 | 2018-07-06 | 天津海达奥普光电技术股份有限公司 | A kind of image partition method of corn seed Shape Feature Extraction |
| CN108445007A (en) * | 2018-01-09 | 2018-08-24 | 深圳市华汉伟业科技有限公司 | A kind of detection method and its detection device based on image co-registration |
| CN110490868A (en) * | 2019-08-23 | 2019-11-22 | 陈�峰 | A kind of lossless method of counting based on computer vision corn grain number per spike |
| CN110782394A (en) * | 2019-10-21 | 2020-02-11 | 中国人民解放军63861部队 | Panoramic video rapid splicing method and system |
| CN110967339A (en) * | 2018-09-29 | 2020-04-07 | 北京瑞智稷数科技有限公司 | Method and device for analyzing corn ear characters and corn character analysis equipment |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BR112013029288A2 (en) * | 2011-05-17 | 2017-12-19 | Basf Plant Science Co Gmbh | method for enhancing one or more traits related to plant production and for the production of a transgenic plant, plant expression construct, transgenic plant, collectable parts of a plant, product and use of a nucleic acid and a construct |
| CN103065352B (en) * | 2012-12-20 | 2015-04-15 | 北京农业信息技术研究中心 | Plant three-dimensional reconstruction method based on image and scanning data |
| US10186029B2 (en) * | 2014-09-26 | 2019-01-22 | Wisconsin Alumni Research Foundation | Object characterization |
| WO2016123525A1 (en) * | 2015-01-30 | 2016-08-04 | Raytheon Company | Apparatus and methods for classifying and counting corn kernels |
| CN104732564B (en) * | 2015-04-03 | 2018-06-19 | 中国农业科学院农业信息研究所 | A kind of Estimating Leaf Area In Maize dynamic nondestructive monitoring device and method |
| CN110357380A (en) * | 2018-04-02 | 2019-10-22 | 江苏凯亚环保科技有限公司 | A kind of filter gap self-cleaning type equipment for separating liquid from solid |
-
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Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101957313A (en) * | 2010-09-21 | 2011-01-26 | 吉林大学 | Method and device for computer visual inspection classification of quality of fresh corn ears |
| CN104197866A (en) * | 2014-07-28 | 2014-12-10 | 山东农业大学 | Method for quantitative determination of cutin to starch percentage of corn kernel cross section |
| CN104598908A (en) * | 2014-09-26 | 2015-05-06 | 浙江理工大学 | Method for recognizing diseases of crop leaves |
| CN106296644A (en) * | 2015-06-10 | 2017-01-04 | 浙江托普云农科技股份有限公司 | Method is analyzed in a kind of corn kernel species test based on image procossing |
| CN106127692A (en) * | 2016-02-25 | 2016-11-16 | 哈尔滨理工大学 | Can be used for the bilateral regression filtering method of gray scale and coloured image |
| CN105806401A (en) * | 2016-03-03 | 2016-07-27 | 中国农业大学 | Three-dimensional image detection system used for indoor quick seed test of maize ears |
| CN108445007A (en) * | 2018-01-09 | 2018-08-24 | 深圳市华汉伟业科技有限公司 | A kind of detection method and its detection device based on image co-registration |
| CN108257136A (en) * | 2018-02-09 | 2018-07-06 | 天津海达奥普光电技术股份有限公司 | A kind of image partition method of corn seed Shape Feature Extraction |
| CN110967339A (en) * | 2018-09-29 | 2020-04-07 | 北京瑞智稷数科技有限公司 | Method and device for analyzing corn ear characters and corn character analysis equipment |
| CN110490868A (en) * | 2019-08-23 | 2019-11-22 | 陈�峰 | A kind of lossless method of counting based on computer vision corn grain number per spike |
| CN110782394A (en) * | 2019-10-21 | 2020-02-11 | 中国人民解放军63861部队 | Panoramic video rapid splicing method and system |
Non-Patent Citations (2)
| Title |
|---|
| "Predicting kernel processing score of harvested and processed corn silage via image processing techniques";Jessica L. Drewry 等;《Computers and Electronics in Agriculture》;20190531;第160卷;第144-152页 * |
| "鲜玉米果穗自动分级方法研究";王慧慧;《中国优秀博硕士学位论文全文数据库(博士) 工程科技Ⅰ辑》;20110915(第9期);B024-4 * |
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