CN107110754A - Spray quality detection device, system, method and sampling servicing unit - Google Patents
Spray quality detection device, system, method and sampling servicing unit Download PDFInfo
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- 238000005070 sampling Methods 0.000 title claims abstract description 93
- 238000001514 detection method Methods 0.000 title claims abstract description 84
- 238000000034 method Methods 0.000 title claims abstract description 53
- 239000007921 spray Substances 0.000 title claims description 177
- 238000012545 processing Methods 0.000 claims abstract description 15
- 238000012360 testing method Methods 0.000 claims description 64
- 230000011218 segmentation Effects 0.000 claims description 20
- 230000004807 localization Effects 0.000 claims description 17
- 230000001186 cumulative effect Effects 0.000 claims description 16
- 230000003044 adaptive effect Effects 0.000 claims description 15
- 230000000694 effects Effects 0.000 claims description 14
- 238000003860 storage Methods 0.000 claims description 13
- 238000005259 measurement Methods 0.000 claims description 12
- 238000004891 communication Methods 0.000 claims description 11
- 238000001914 filtration Methods 0.000 claims description 10
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000009826 distribution Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000005315 distribution function Methods 0.000 claims description 8
- 238000003708 edge detection Methods 0.000 claims description 8
- 239000000976 ink Substances 0.000 claims description 7
- 238000012372 quality testing Methods 0.000 claims description 5
- 239000002245 particle Substances 0.000 description 11
- 238000010586 diagram Methods 0.000 description 10
- 239000003814 drug Substances 0.000 description 8
- 239000000575 pesticide Substances 0.000 description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 8
- 239000003595 mist Substances 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 5
- 239000003795 chemical substances by application Substances 0.000 description 4
- 238000005507 spraying Methods 0.000 description 4
- 239000007788 liquid Substances 0.000 description 3
- CPLXHLVBOLITMK-UHFFFAOYSA-N magnesium oxide Inorganic materials [Mg]=O CPLXHLVBOLITMK-UHFFFAOYSA-N 0.000 description 3
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 2
- 239000004305 biphenyl Substances 0.000 description 2
- PMPJQLCPEQFEJW-HPKCLRQXSA-L disodium;2-[(e)-2-[4-[4-[(e)-2-(2-sulfonatophenyl)ethenyl]phenyl]phenyl]ethenyl]benzenesulfonate Chemical group [Na+].[Na+].[O-]S(=O)(=O)C1=CC=CC=C1\C=C\C1=CC=C(C=2C=CC(\C=C\C=3C(=CC=CC=3)S([O-])(=O)=O)=CC=2)C=C1 PMPJQLCPEQFEJW-HPKCLRQXSA-L 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000003912 environmental pollution Methods 0.000 description 2
- 239000000395 magnesium oxide Substances 0.000 description 2
- 239000000843 powder Substances 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 230000007423 decrease Effects 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 239000008187 granular material Substances 0.000 description 1
- AXZKOIWUVFPNLO-UHFFFAOYSA-N magnesium;oxygen(2-) Chemical compound [O-2].[Mg+2] AXZKOIWUVFPNLO-UHFFFAOYSA-N 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/02—Devices for withdrawing samples
- G01N1/10—Devices for withdrawing samples in the liquid or fluent state
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
-
- 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
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- Biochemistry (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Dispersion Chemistry (AREA)
- Hydrology & Water Resources (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
One kind sprinkling quality detecting system (100), method and a kind of sampling servicing unit (20).The sprinkling quality detecting system (100) includes a kind of sprinkling quality detection device (30), and the sprinkling quality detection device (30) includes image collecting device (31) and image processor (32).Described image harvester (31) is used for the sampled images for gathering at least one set of detected sample, and at least one set for the default sample area that at least one set of detected sample is taken respectively from sprinkling region presets sampled point.Described image processor (32) is used to obtain the sampled images of every group of detected sample and carries out image procossing, and using the sprinkling thing parameter of unit area in the described image after pre-set image algorithm calculating processing.
Description
The present invention relates to sprinkling technical field of quality detection, in particular to a kind of sprinkling quality detection device, system, method and sampling auxiliary devices.
Spray, such as the droplets parameter such as distributing homogeneity, coverage rate, density size of droplet is to evaluate the important indicator of agriculture unmanned plane sprinkling quality.The droplet parameter sprayed that is convenient, accurately calculating unmanned plane, is of great significance to the development of agriculture unmanned plane, test, production and use.Droplet parameter is measured in the actual job of field scene, it can be carried out in time, being accurately arranged according to spraying parameter of the data result to agriculture unmanned plane, control agriculture unmanned plane and reach good spray effect, improve the utilization rate of pesticide, it reduces environmental pollution, promotes control efficiency.
The particle size of droplet is an important factor for influencing droplet density, and in a certain range, mist droplet particle size size is inversely proportional with droplet density.Suitable mist droplet particle size can guarantee that droplet uniform deposition and forms preferable Density Distribution on leaf surface, and has the ability that subordinate is seeped preferably into influences of plant crown, this helps to reduce pharmaceutical use, obtains good control efficiency while reducing environmental pollution.Mist droplet particle size is excessive, will lead to that droplet density is too small, and area coverage is few, and penetration of droplets ability is poor, and most of droplet falls to influences of plant crown top surface, it is difficult to arrive inside influences of plant crown, control efficiency is bad.Mist droplet particle size is too small, then is also easy to produce biggish pesticide Driftdiffusion, and the risk of drift increases, while the evaporation loss of infall process is larger, and settled density can reduce, control efficiency decline.
Traditional droplet sizes measurement mainly has two major classes method: based on optics Direct Determination and based on the method for collecting sample.Wherein, it is to measure mist droplet particle size using the optical technologies such as laser hologram, high-speed camera, such as laser particle analyzer based on optics Direct Determination, the principle of use is laser diffraction, particle can make laser generate scattering, and mist droplet particle size size is determined in the case where being not directly contacted with droplet.This method measurement accuracy is high, and range is big, but instrument price is expensive, and bulky, adaptive capacity to environment is poor, is not suitable for field measurement, it is difficult to be widely used.Method based on collecting sample is to collect droplet using sampler to carry out subsequent analysis, common sampler has magnesium oxide plate, water sensitive paper, the quick paper of oil, food tray etc., such as magnesia trace is hit using droplet and is measured, droplet and mgo surface hit and leave trace, then carry out droplet sizes measurement using microscope.Alternatively, collecting droplet by water sensitive paper, directly estimating or acquire water sensitive paper image using microscope and measuring statistics to colour developing point in water sensitive paper using image processing software.However, inconvenience is taken in storage in use since the principle of water sensitive paper is to meet water to develop the color, arrangement and collection water sensitive paper take time and effort, inefficiency, in the situation that ambient humidity is larger, such as in the case that early morning dew is more, water sensitive paper result is easy to be interfered.In addition, measuring based on sampling and using microscope progress droplet sizes, this method great work intensity, the time is long and precision is not high, influences vulnerable to artificial subjective factor.
In view of this, it is necessary to a kind of sprinkling quality detection device, system, method and a kind of sampling auxiliary device are proposed, to solve the above problems.
A kind of sprinkling quality detection device, including image collecting device and image processor.Described image acquisition device is used to acquire the sampled images of at least one set of sample to be tested, and at least one set for the default sampling area that at least one set sample to be tested is taken respectively from sprinkling region presets sampled point.Described image processor is used to obtain the sampled images of every group of sample to be tested and carries out image procossing, and using the spray parameter of unit area in the described image after the calculation processing of pre-set image algorithm.
Further, described image processor is obtaining the sampled images of every group of sample to be tested and when carrying out image procossing, the miscellaneous point of high brightness for including in the sampled images specifically for filtering out acquisition.
Further, at the high brightness that described image processor includes in the sampled images for filtering out acquisition miscellaneous, it is specifically used for:
The sampled images are converted into gray level image；
Calculate the average gray value of gray level image described in whole picture；And
The gray level image is traversed, the gray value of each pixel of the gray level image is compared with the average gray value, gray value is greater than the pixel of the average gray value if it exists, then the gray value of the pixel is assigned a value of the average gray value.
Further, described image processor is when obtaining the sampled images of every group of sample to be tested and carrying out image procossing, the characteristics of when being specifically also used to occur in the sampled images spray adhesion according to each image itself, by the adaptive selection segmentation threshold of P parametric method, and divide the region of spray adhesion.
Further, described image processor is specifically used for when through P parametric method adaptive selection segmentation threshold:
Calculate the grey level histogram of image；
Since minimum gray value, the cumulative distribution histogram of image is calculated；And
It chooses with gray value corresponding to the closest cumulative distribution function of target area grayscale image as segmentation threshold.
Further, described image processor is specifically also used to when obtaining the sampled images of every group of sample to be tested and carrying out image procossing:
Canny edge detection is carried out to the gray level image, and according to edge by the Binary Sketch of Grey Scale Image；
Closed operation, object closed onto itself edge are carried out to the binary map；
Connected component analysis is carried out to the binary map, each connected region corresponds to a testing result；
Statistic mixed-state result number, calculates the parameters such as the diameter, area and circularity of each testing result；And
Each point that detected is carried out on the sampled images to retouch side, and each result is outlined with rectangular box, and export picture.
Further, described image processor is also used to as unit of the test data of every group of image, exports the parametric plot of the test data of every group of image, and the spray parameter includes following at least one: density, coverage rate and the partial size of spray.
Further, described image processor is also used to the average that the spray parameter according to every group calculates every group of spray.
Further, described image processor is also used to the standard deviation that the spray parameter according to every group calculates every group of spray, with observation degree of variation size in every group of spray of measurement representation.
Further, described image processor is also used to the coefficient of variation that the spray parameter according to every group calculates every group of spray, to measure the distributing homogeneity of spray.
Further, the sprinkling quality detection device is a portable electronic device.
Further, the portable electronic device is smart phone or tablet computer；And/or
Described image acquisition device is the camera of the portable electronic device；And/or
Described image processor is the processor of the portable electronic device.
Further, the sprinkling quality detection device further includes display screen, and described image processor is also used to generate a test information set interface before sprinkling quality testing starts, and is shown on the display screen.
Further, the sprinkling quality detection device further includes storage device, and the storage device is for storing the acquired image；And/or
The sprinkling quality detection device further includes communication device, for data to be communicated and transmitted with external device (ED), and/or, the communication device is USB interface, WIFI module or bluetooth module.
A kind of sprinkling quality detecting system, including sampling auxiliary device and sprinkling quality detection device.The sampling auxiliary device includes shell, darkroom, headlamp and Image Acquisition window, the darkroom is disposed in the housing portion, and completely cut off extraneous light, sampled point is preset for placing sample to be tested, at least one set for the default sampling area that the sample to be tested is taken respectively from sprinkling region in the darkroom.The headlamp is set in the darkroom, and in the light of offer preset wavelength in the darkroom.The sprinkling quality detection device includes image collecting device and image processor.Described image acquisition device is used to be placed in the sampled images of the dark indoor sample to be tested through the acquisition of described image acquisition window.Described image processor is used to obtain the sampled images of every group of sample to be tested and carries out image procossing, and using the spray parameter of unit area in the described image after the calculation processing of pre-set image algorithm.
Further, described image processor is obtaining the sampled images of every group of sample to be tested and when carrying out image procossing, the miscellaneous point of high brightness for including in the sampled images specifically for filtering out acquisition.
Further, at the high brightness that described image processor includes in the sampled images for filtering out acquisition miscellaneous, it is specifically used for:
The sampled images are converted into gray level image；
Calculate the average gray value of gray level image described in whole picture；And
The gray level image is traversed, the gray value of each pixel of the gray level image is compared with the average gray value, gray value is greater than the pixel of the average gray value if it exists, then the gray value of the pixel is assigned a value of the average gray value.
Further, described image processor is when obtaining the sampled images of every group of sample to be tested and carrying out image procossing, the characteristics of when being specifically also used to occur in the sampled images spray adhesion according to each image itself, by the adaptive selection segmentation threshold of P parametric method, and divide the region of spray adhesion.
Further, described image processor is specifically used for when through P parametric method adaptive selection segmentation threshold:
Calculate the grey level histogram of image；
Since minimum gray value, the cumulative distribution histogram of image is calculated；And
It chooses with gray value corresponding to the closest cumulative distribution function of target area grayscale image as threshold value.
Further, described image processor is specifically also used to when obtaining the sampled images of every group of sample to be tested and carrying out image procossing:
Canny edge detection is carried out to the gray level image, and according to edge by the Binary Sketch of Grey Scale Image；
Closed operation, object closed onto itself edge are carried out to the binary map；
Connected component analysis is carried out to the binary map, each connected region corresponds to a testing result；
Statistic mixed-state result number, calculates the parameters such as the diameter, area and circularity of each testing result；And
Each point that detected is carried out on the sampled images to retouch side, and each result is outlined with rectangular box, and export picture.
Further, described image processor is also used to as unit of the test data of every group of image, exports the parametric plot of the test data of every group of image, and the spray parameter includes following at least one: density, coverage rate and the partial size of spray.
Further, described image processor is also used to the average that the spray parameter according to every group calculates every group of spray.
Further, described image processor is also used to the standard deviation that the spray parameter according to every group calculates every group of spray, with observation degree of variation size in every group of spray of measurement representation.
Further, described image processor is also used to the coefficient of variation that the spray parameter according to every group calculates every group of spray, to measure the distributing homogeneity of spray.
Further, the sprinkling quality detection device is a portable electronic device.
Further, the portable electronic device is smart phone or tablet computer；And/or
Described image acquisition device is the camera of the portable electronic device；And/or
Described image processor is the processor of the portable electronic device.
Further, the sprinkling quality detection device further includes display screen, and described image processor is also used to generate a test information set interface before sprinkling quality testing starts, and is shown on the display screen；And/or
The sprinkling quality detection device further includes storage device, and the storage device is for storing the acquired image；And/or
The sprinkling quality detection device further includes communication device, for data to be communicated and transmitted with external device (ED), and/or, the communication device is USB interface, WIFI module or bluetooth module.
Further, the headlamp includes at least two kinds of light emitting sources that can issue the light of different wave length.
Further, the headlamp includes at least white light and ultraviolet lamp；
Alternatively, the headlamp is LED light or fluorescent lamp.
Further, the sampling auxiliary device further includes battery and light control switch, the light control switch is set to the outside of the shell, and is electrically connected at least two light emitting sources of the headlamp, and being electrically connected between at least two light emitting source and the battery is connected for selecting.
Further, the light control switch is included at least for answering at least two of at least two light emitting source gear and a power-off gear is connected.
Further, the sprinkling quality detecting system further includes battery compartment, portion is disposed in the housing, for accommodating the battery；And/or
It further include power interface and electric power management circuit, the power interface receives the voltage input of the external power supply and be transmitted to the electric power management circuit for connecting with external power supply；The electric power management circuit receives the voltage input of the external power supply and charges to the battery；The battery is used to power to the headlamp.
Further, the sampling auxiliary device further includes localization region, and the localization region is set to the housing upper surface, and is arranged side by side with described image acquisition window, for placing the sprinkling quality detection device.
Further, the sampling auxiliary device further includes fixture, and the fixture is set on the localization region, for clamping and fixing the sprinkling quality detection device, and makes the image collecting device face described image acquisition window of the sprinkling quality detection device.
Further, the fixture includes at least two clamping pieces being oppositely arranged, the positions of described two clamping pieces can activity adjustment, so as to adjust the distance between described two clamping pieces, so as to clamp various sizes of sprinkling quality detection device.
Further, the sampling auxiliary device further includes lens, and the lens are used to improve the clarity of the sampled images and shorten focal length；
And/or the lens parameters of the lens are 10 times of 180 degree wide-angles；
And/or the lens are set in described image acquisition window or are set in the darkroom and are located at below described image acquisition window.
A kind of sprinkling quality determining method, comprising the following steps:
The sampled images of at least one set of sample to be tested are acquired, at least one set for the default sampling area that at least one set sample to be tested is taken respectively from sprinkling region presets sampled point；
It obtains the sampled images of every group of sample to be tested and carries out image procossing；
Using the spray parameter of unit area in the described image after pre-set image algorithm calculation processing.
Further, the step of image of at least one set of sample to be tested of acquisition specifically includes:
One darkroom is provided, and the sample to be tested is placed in the darkroom；
The light of preset wavelength is selected to irradiate the sample to be tested in the darkroom according to the type of sample to be tested；And
An Image Acquisition window and/or lens are provided in the top in the darkroom, and are placed in the sampled images of the dark indoor sample to be tested through described image acquisition window and/or lens acquisition.
Further, the sample to be tested is the plant blade face that field is sprayed with Fluorescent detector, and the specific light is 365nm ultraviolet light；Or
The sample to be tested is the blank sheet of paper for being sprayed with prepared Chinese ink, and the specific light is white light.
Further, include: the step of obtaining the sampled images of every group of sample to be tested and carrying out image procossing
Filter out the miscellaneous point of the high brightness for including in the sampled images of acquisition.
Further, include in the sampled images for filtering out acquisition high brightness miscellaneous the step of includes:
The sampled images are converted into gray level image；
Calculate the average gray value of gray level image described in whole picture；And
The gray level image is traversed, the gray value of each pixel of the gray level image is compared with the average gray value, gray value is greater than the pixel of the average gray value if it exists, then the gray value of the pixel is assigned a value of the average gray value.
Further, the step of obtaining the sampled images of every group of sample to be tested and carrying out image procossing further include:
The characteristics of when occurring spray adhesion in the sampled images according to each image itself, by the adaptive selection segmentation threshold of P parametric method, and divide the region of spray adhesion.
Further, the step of adaptive by P parametric method selection segmentation threshold includes:
Calculate the grey level histogram of image；
Since minimum gray value, the cumulative distribution histogram of image is calculated；And
It chooses with gray value corresponding to the closest cumulative distribution function of target area grayscale image as segmentation threshold.
Further, the step of obtaining the sampled images of every group of sample to be tested and carrying out image procossing further include:
Canny edge detection is carried out to the gray level image, and according to edge by the Binary Sketch of Grey Scale Image；
Closed operation, object closed onto itself edge are carried out to the binary map；
Connected component analysis is carried out to the binary map, each connected region corresponds to a testing result；
Statistic mixed-state result number, calculates the parameters such as the diameter, area and circularity of each testing result；And
Each point that detected is carried out on the sampled images to retouch side, and each result is outlined with rectangular box, and export picture.
Further, the sprinkling quality determining method further comprises the steps of:
As unit of the test data of every group of image, the parametric plot of the test data of every group of image is exported, the spray parameter includes following at least one: density, coverage rate and the partial size of spray.
Further, the sprinkling quality determining method further comprises the steps of:
The spray parameter according to every group calculates the average of every group of spray.
Further, the sprinkling quality determining method further comprises the steps of:
The spray parameter according to every group calculates the standard deviation of every group of spray, with observation degree of variation size in every group of spray of measurement representation.
Further, the sprinkling quality determining method further comprises the steps of:
The spray parameter according to every group calculates the coefficient of variation of every group of spray, to measure the distributing homogeneity of spray.
Further, before the step of acquiring the image of the sample to be tested further include:
At least one described default sampling area is set in sprinkling region, and sets at least one set of default sampled point in each default sampling area.
Further, before the step of acquiring the image of the sample to be tested further include:
It generates and shows a test information set interface.
A kind of sampling auxiliary device, comprising:
Shell；
Darkroom is disposed in the housing portion, and completely cuts off extraneous light, and sampled point is preset for placing sample to be tested, at least one set for the default sampling area that the sample to be tested is taken respectively from sprinkling region in the darkroom；
Headlamp is set in the darkroom, and in the light of offer preset wavelength in the darkroom；And
Image Acquisition window, the image acquisition device for being supplied to a sprinkling quality detection device are placed in the sampled images of the dark indoor sample to be tested.
Further, the headlamp includes at least two kinds of light emitting sources that can issue the light of different wave length.
Further, the headlamp includes at least white light and ultraviolet lamp；
Alternatively, the headlamp is LED light or fluorescent lamp.
Further, the sampling auxiliary device further includes battery and light control switch, the light control switch is set to the outside of the shell, and is electrically connected at least two light emitting sources of the headlamp, and being electrically connected between at least two light emitting source and the battery is connected for selecting.
Further, the light control switch is included at least for answering at least two of at least two light emitting source gear and a power-off gear is connected.
Further, the sampling auxiliary device further includes battery compartment, portion is disposed in the housing, for accommodating the battery；And/or
It further include power interface and electric power management circuit, the power interface receives the voltage input of the external power supply and be transmitted to the electric power management circuit for connecting with external power supply；The electric power management circuit receives the voltage input of the external power supply and charges to the battery；The battery is used to power to the headlamp.
Further, the sampling auxiliary device further includes localization region, and the localization region is set to the housing upper surface, and is arranged side by side with described image acquisition window, for placing the sprinkling quality detection device.
Further, the sampling auxiliary device further includes fixture, and the fixture is set on the localization region, for clamping and fixing the sprinkling quality detection device, and makes the image collecting device face described image acquisition window of the sprinkling quality detection device.
Further, the fixture includes at least two clamping pieces being oppositely arranged, the positions of described two clamping pieces can activity adjustment, so as to adjust the distance between described two clamping pieces, so as to clamp various sizes of sprinkling quality detection device.
Further, the sampling auxiliary device further includes lens, and the lens are used to improve the clarity of the sampled images and shorten focal length；
And/or the lens parameters of the lens are 10 times of 180 degree wide-angles；
And/or the lens are set in described image acquisition window or are set in the darkroom and are located at below described image acquisition window.
A kind of sprinkling quality detecting system provided by the invention rapidly can calculate spray parameter at spray test scene, effectively, it rapidly provides testing result and evaluates sprinkling quality, to keep staff timely according to testing result, accurately adjust the spraying parameter of agriculture unmanned plane, preferably control spray effect, reduce sprinkling medicament waste, reduce pollution of the pesticide to environment, so that agriculture unmanned plane is preferably suitable for different weather condition and crops sprinkling requires, and use the sprinkling quality detecting system, it is easy to operate, it is convenient, measurement is accurate, it is at low cost.
Fig. 1 is a kind of structural schematic diagram of sprinkling quality detecting system of one embodiment of the invention.
Fig. 2 is the spray sampling area schematic diagram of one embodiment of the invention.
Fig. 3 is a kind of perspective view of sampling auxiliary device of one embodiment of the invention.
Fig. 4 is the internal structure chart of the sampling auxiliary device of Fig. 3.
Fig. 5 is the lighting control circuit structure chart of the sampling auxiliary device of Fig. 3.
Fig. 6 is a kind of structural schematic diagram of sprinkling quality detection device of one embodiment of the invention.
Fig. 7 is the test information set interface schematic diagram that shows on the sprinkling quality detection device of one embodiment of the invention.
Fig. 8 is another test information set interface schematic diagram for showing on the sprinkling quality detection device of one embodiment of the invention.
Fig. 9 is the image name interface schematic diagram that shows on the sprinkling quality detection device of one embodiment of the invention.
Figure 10 is the collected sampled images schematic diagram of sprinkling quality detection device of one embodiment of the invention.
Figure 11 be Figure 10 sampled images by Fig. 6 sprinkling quality detection device carry out image procossing after schematic diagram.
Figure 12 is the parametric plot of the test data of the sprinkling quality detection device output of one embodiment of the invention.
Figure 13 is a kind of sprinkling quality determining method flow chart of one embodiment of the invention.
| Spray quality detecting system | 100 |
| Sample auxiliary device | 20 |
| Shell | 21 |
| Darkroom | 22 |
| Image Acquisition window | 23 |
| Lens | 231 |
| Headlamp | 24 |
| White light | 241 |
| Ultraviolet lamp | 242 |
| Battery | 251 |
| Battery compartment | 252 |
| Light driving circuit | 26 |
| Light control switch | 27 |
| Electric power management circuit | 28 |
| Localization region | 291 |
| Fixture | 292 |
| Spray quality detection device | 30 |
| Image collecting device | 31 |
| Image processor | 32 |
| Display screen | 33 |
| Storage device | 34 |
| Communication device | 35 |
| Step | 1301-1310 |
The present invention that the following detailed description will be further explained with reference to the above drawings.
Following will be combined with the drawings in the embodiments of the present invention, and technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, every other embodiment obtained by those of ordinary skill in the art without making creative efforts, shall fall within the protection scope of the present invention.
Referring to Fig. 1, a kind of structural schematic diagram of sprinkling quality detecting system 100 for one embodiment of the invention.The sprinkling quality detecting system 100 includes sampling auxiliary device 20 and sprinkling quality detection device 30.The sprinkling quality detection device 30 is detected for acquiring the image of sample to be tested in agriculture unmanned plane (not shown) spray test, and according to sprinkling quality of the image of acquisition to the agriculture unmanned plane.The sampled images that the sampling auxiliary device 20 is used to assist the sprinkling quality detection device 30 accurately and rapidly to acquire the sample to be tested in the agriculture unmanned plane spray test.
In agriculture unmanned plane spray test, the agricultural unmanned plane can fly automatically according to preconfigured course data and spray medicament to the ground.In the present embodiment, the plant of the agriculture unmanned plane to the ground sprays medicament.In the present embodiment, the medicament is fluorescent brightener CBS-X, such as double talan-biphenyl type optical fluorescence agent, the optical fluorescence agent can issue the fluorescent effect of brilliant white under ultraviolet light.In other embodiments, blank sheet of paper, the agricultural unmanned plane analog actual job mode, the blank sheet of paper sprinkling prepared Chinese ink being laid with to the ground can be laid on ground.
After the agricultural unmanned plane sprinkling, staff can set at least one spray sampling area in sprinkling region, and at least one set of sampled point can be set in each spray sampling area.Such as, as shown in Figure 2, staff can be on the direction perpendicular to the agriculture unmanned plane course line, spray sampling area will be set at described agriculture unmanned plane departure location 10m, 20m, the length of each spray sampling area is 6m, a sampled point is set at interval of 0.5m, each spray sampling area sets 13 sampled points altogether.It is understood that the spray sampling area, the length of spray sampling area, sampling interval can be according to practical field field changes, however it is not limited to the present embodiment.
It should be noted that spray can be drop, solid state powder, solid granulates etc., for example, spray is liquid pesticide is atomized and forms droplet, or the raindrop being discretely formed when sprinkling liquid pesticide；When spraying the pesticide of pulverulence, spray is powder particle；Granular seed is sprayed, spray is particle etc..When in the following embodiments, to spray liquid pesticide, spray be droplet for be illustrated.
Fig. 3-4 is please referred to, is a kind of perspective view of sampling auxiliary device 20 of one embodiment of the invention.The sampling auxiliary device 20 includes shell 21, darkroom 22 and Image Acquisition window 23.The darkroom 22 is set to inside the shell 21, and completely cuts off extraneous light, and photophobism is preferable.Described image acquisition window 23 is set to the top in the darkroom 22.In the present embodiment, the sampling auxiliary device 20 further includes lens 231, the lens 231 are set in described image acquisition window 23 or are set in the darkroom 22 and are located at 23 lower section of described image acquisition window, and the lens 231 are used to improve the clarity of the sampled images and shorten focal length.In the present embodiment, the lens parameters of the lens 231 are 10 times of 180 degree wide-angles.
In the present embodiment, the sampling auxiliary device 20 further includes localization region 291, and the localization region 291 is set to 21 upper surface of shell, and is arranged side by side with described image acquisition window 23, for placing the sprinkling quality detection device 30.
In the present embodiment, the sampling auxiliary device 20 further includes fixture 292, and the fixture 292 is set on the localization region 291, for clamping and fixing the sprinkling quality detection device 30.In the present embodiment, the fixture 292 includes at least two clamping pieces being oppositely arranged, the position of described two clamping pieces can activity adjustment, so as to adjust the distance between described two clamping pieces, so as to clamp various sizes of sprinkling quality detection device 30.For example, described two clamping pieces are translatable in the localization region 291, to adjust the distance between described two clamping pieces.Alternatively, described two clamping pieces are contracting mechanism, pass through the distance between described two clamping pieces of the telescopic adjustment of described two clamping pieces.It is understood that the fixture 292 or other structures, as long as various sizes of sprinkling quality detection device 30 can be clamped, however it is not limited to the present embodiment.
Referring to Fig. 5, in the present embodiment, the sampling auxiliary device 20 further includes headlamp 24, the headlamp 24 is set in the darkroom 22, and in the light of offer preset wavelength in the darkroom 22.In the present embodiment, the headlamp 24 includes at least two kinds of light emitting sources that can issue the light of different wave length.In the present embodiment, the headlamp 24, which includes at least, can issue the white light 241 of white light and can issue the ultraviolet lamp 242 of 365nm ultraviolet light.The headlamp 24 can be LED light or fluorescent lamp.
In the present embodiment, the sampling auxiliary device 20 further includes battery 251, light driving circuit 26 and light control switch 27.The light control switch 27 can be set to the outside of the shell 21, and be electrically connected at least two light emitting sources of the headlamp 24, and being electrically connected between at least two light emitting source and the battery 251 is connected for selecting.
In the present embodiment, the light control switch 27 is included at least for answering at least two of at least two light emitting source gear and a power-off gear is connected.For example, the light control switch 27 may include the gear of left, center, right three, wherein left gear is the gear that the white light 241 is connected, and middle gear is power-off gear, and right gear is the gear that the ultraviolet lamp 242 is connected.
In the present embodiment, as shown in figure 4, the sampling auxiliary device 20 further includes the battery compartment 252 inside the shell 21, for accommodating the battery 251.In other embodiments, the sampling auxiliary device 20 further includes power interface (not shown) and electric power management circuit 28(as shown in Figure 5), the power interface receives the voltage input of the external power supply and is transmitted to the electric power management circuit 28 for connecting with external power supply (not shown).The electric power management circuit 28 receives the voltage input of the external power supply and charges to the battery 251.The battery 251 is powered by the light driving circuit 26 and the light control switch 27 to the headlamp 24.
Referring to Fig. 6, a kind of structural schematic diagram of sprinkling quality detection device 30 for one embodiment of the invention.In the present embodiment, the sprinkling quality detection device 30 includes image collecting device 31 and image processor 32.Described image acquisition device 31 is used to acquire the sampled images of at least one set of sample to be tested, and at least one set for the default sampling area that at least one set sample to be tested is taken respectively from sprinkling region presets sampled point.
In the present embodiment, the sprinkling quality detection device 30 is a portable electronic device, such as smart phone or tablet computer etc..Described image acquisition device 31 is the camera of the portable electronic device.Described image processor 32 is the processor of the portable electronic device.Due to using portable electronic device as image sampling and processing platform, requirement easy to operate, convenient, at low cost is reached, and valuableness, the image sampling of bulky, processing equipment are not needed, environmental suitability is strong, can directly use at spray test scene.
When sampling, first the sprinkling quality detection device 30 is placed on the fixture 292 of the sampling auxiliary device 20, and it is fixedly clamped the sprinkling quality detection device 30 after making the Image Acquisition window 23 of sampling auxiliary device 20 described in 31 face of image collecting device of the sprinkling quality detection device 30, then the respective sources built in the sampling auxiliary device 20 are opened according to the type of the sample to be tested, the sample to be tested for being sprayed with spray of every group of default sampled point is placed in again in the darkroom 22 of the sampling auxiliary device 20, then operation described image acquisition device 31 acquires the sampled images of the sample to be tested.
Such as, in the present embodiment, in the darkroom 22 that corresponding influences of plant crown blade face is positioned at the sampling auxiliary device 20 by the position of each sampled point of spray pickup area, and using influences of plant crown blade face described in 365nm ultraviolet light, the fluorescent image on the influences of plant crown blade face is obtained through the Image Acquisition window 23 of the sampling auxiliary device 20 with the camera of the portable electronic device.
In the present embodiment, the sprinkling quality detection device 30 further includes display screen 33 and storage device 34.Described image processor 32 is used to before sprinkling quality testing starts generate a test information set interface, and it is shown on the display screen 33, so that dependence test information is arranged in test job personnel, such as, as Figure 7-8, the title of settable test, time, sampling length, sampling interval etc..Image Acquisition interface can be entered after setting, and after collected sampled images names (as shown in Figure 9) in order, the default file that the sampled images are stored in the storage device 34 is pressed from both sides in.
Described image processor 32 is also used to obtain the sampled images (as shown in Figure 10) of every group of sample to be tested and carries out image procossing.
Specifically, described image processor 32 filters out the miscellaneous point of high brightness for including in the sampled images of acquisition first.
The sampled images of plant leaf blade or the surface color of sampled images of test paper that are acquired due to described image acquisition device 31 are simultaneously uneven, and the miscellaneous point of some high brightness present in sampled images will affect the accuracy of detection algorithm.Subsequent processing for convenience, usually will first filter out these miscellaneous points.Traditional method is that Fuzzy Processing, such as Gaussian Blur are filtered to described image, and intermediate value is fuzzy etc., but the filter effect of miscellaneous point biggish for some individuals is poor.
In the present embodiment, the sampled images of the sample to be tested are color image, the sampled images are converted to gray level image first by described image processor 32, then the average gray value of gray level image described in whole picture is calculated, the gray level image is traversed again, the gray value of each pixel of the gray level image is compared with the average gray value, it is greater than the pixel of the average gray value if there is gray value, then the gray value of the pixel is assigned a value of the average gray value by described image processor 32；Otherwise, keep the gray value of the pixel constant.
The characteristics of when spray adhesion also occurs in the sampled images in described image processor 32 according to each image itself, by the adaptive selection segmentation threshold of P parametric method, and divide the region of spray adhesion, in order to the statistics of spray parameter.Specifically, described image processor 32 first calculates the grey level histogram of image, again since minimum gray value, the cumulative distribution histogram of image is calculated, and is chosen with gray value corresponding to the closest cumulative distribution function of target area grayscale image as segmentation threshold.
Described image processor 32 also carries out canny edge detection to the gray level image, and according to edge by the Binary Sketch of Grey Scale Image；Closed operation, object closed onto itself edge are carried out to the binary map；Connected component analysis is carried out to the binary map, each connected region corresponds to a testing result, such as ink droplet or phosphor dot；Statistic mixed-state result number, calculates the parameters such as the diameter, area and circularity of each testing result；And each point that detected is carried out on the sampled images to retouch side, and outline each result with rectangular box, and export picture (as shown in figure 11), so as to intuitive judgment algorithm accuracy.
In the present embodiment, described image processor 32 is also using the spray parameter of unit area in the described image after the calculation processing of pre-set image algorithm, and as unit of the test data of every group of image, export the parametric plot of the test data of every group of image, wherein, as shown in figure 12, the spray parameter includes following at least one: density, coverage rate and the partial size of spray.As unit of every group of data, the spray parameter of every group of image is showed in graph form, thus can objectively see very much the variation of data.
Described image processor 32 is also used to the average that the spray parameter according to every group calculates every group of spray, uses average as the representative of spray data, and representative power is influenced by observation degree of variation each in spray.By taking spray coverage rate as an example, the average can be calculated by following formula and be obtained:
,
In formula: i=1,2 ..., n；Indicate unit area (1 × 1cm of each image in entire set of image2) image district；
Unit area (1 × 1cm of every image of-expression2) spray area coverage (unit: pixel) in image district；
The average value (unit: pixel) of the spray area coverage of-expression entire set of image.
If the variation of each observation is small, average is strong to the representativeness of spray, if the variation of each observation is big, average representativeness is weak, thus only with the description that takes statistics of feature of the average to a data is incomplete.
In the present embodiment, described image processor 32 is also used to the spray parameter according to every group and calculates the statistic for indicating observation degree of variation size in every group of spray, i.e. standard deviation.It is obtained for example, the standard deviation can be calculated by following formula:
,
In formula:Unit area (1 × 1cm of every image of-expression2) spray area coverage (unit: pixel) in image district；
The standard deviation (unit: pixel) of the spray area coverage of-expression entire set of image.
In the present embodiment, described image processor 32 calculates the coefficient of variation of every group of spray also according to spray parameter described in every group, to measure the distributing homogeneity of spray.The coefficient of variation is to measure another statistic of each observation degree of variation in spray.When carry out two or more sets spray spray effects compare when, the coefficient of variation can be used to compare.The coefficient of variation is the ratio of standard deviation and average, is denoted as C V.The coefficient of variation can eliminate the influence that unit and (or) average difference compare two or more data variance degree.It is obtained for example, the coefficient of variation can be calculated by following formula:
,
In formula:The standard deviation (unit: pixel) of the spray area coverage of-expression entire set of image；
The average value (unit: pixel) of the spray area coverage of-expression entire set of image；
The coefficient of variation of C V-expression entire set of image spray area coverage.
Referring to Fig. 6, in the present embodiment, the sprinkling quality detection device 30 further includes communication device 35, for being communicated with external device (ED) (not shown) and transmitting data.The communication device 35 can be USB interface, WIFI module or bluetooth module.
Sprinkling quality detecting system 100 provided by the invention rapidly can calculate spray parameter at spray test scene, effectively, it rapidly provides testing result and evaluates sprinkling quality, to keep staff timely according to testing result, accurately adjust the spraying parameter of agriculture unmanned plane, preferably control spray effect, reduce sprinkling medicament waste, reduce pollution of the pesticide to environment, so that agriculture unmanned plane is preferably suitable for different weather condition and crops sprinkling requires, and use the sprinkling quality detecting system 100, it is easy to operate, it is convenient, measurement is accurate, it is at low cost.
Figure 13 is a kind of sprinkling quality determining method flow chart of one embodiment of the invention, the sprinkling quality determining method can acquire the sampled images of sample to be tested in agriculture unmanned plane spray test, and be detected according to sprinkling quality of the sampled images to the agriculture unmanned plane.
In the agriculture unmanned plane spray test, the agricultural unmanned plane can fly automatically according to preconfigured course data and spray medicament to the ground.In the present embodiment, the plant of the agriculture unmanned plane to the ground sprays medicament.In the present embodiment, the medicament is fluorescent brightener CBS-X, such as double talan-biphenyl type optical fluorescence agent, the optical fluorescence agent can issue the fluorescent effect of brilliant white under ultraviolet light.In other embodiments, blank sheet of paper, the agricultural unmanned plane analog actual job mode, the blank sheet of paper sprinkling prepared Chinese ink being laid with to the ground can be laid on ground.
In the present embodiment, the sprinkling quality determining method includes the following steps:
Step 1301, at least one default sampling area is set in sprinkling region, and at least one set of default sampled point is set in each default sampling area.
Such as, staff can be on the direction perpendicular to the agriculture unmanned plane course line, spray sampling area will be set at described agriculture unmanned plane departure location 10m, 20m, the length of each spray sampling area is 6m, a sampled point is set at interval of 0.5m, each spray sampling area sets 13 sampled points altogether.It is understood that the spray sampling area, the length of spray sampling area, sampling interval can be according to practical field field changes, however it is not limited to the present embodiment.
Step 1302, described image processor 32 generates and shows a test information set interface, so that dependence test information is arranged in test job personnel, for example, the title of settable test, time, sampling length, sampling interval etc..
Step 1303, a darkroom 22 is provided, and sample to be tested is placed in the darkroom 22, at least one set for the default sampling area that the sample to be tested is taken respectively from sprinkling region presets sampled point.
Step 1304, the light of preset wavelength is selected to irradiate the sample to be tested in the darkroom 22 according to the type of sample to be tested.
In the present embodiment, the sample to be tested is the plant blade face that field is sprayed with Fluorescent detector, and the specific light is 365nm ultraviolet light.In other embodiments, the sample to be tested can also be the blank sheet of paper for being sprayed with prepared Chinese ink, and the specific light is white light.
Step 1305, an Image Acquisition window 23 and/or lens 231 are provided in the top in the darkroom 22, described image acquisition device 31 acquires the sampled images for the sample to be tested being placed in the darkroom 22 through described image acquisition window 23 and/or the lens 231.
Step 1306, described image processor 32 obtains the sampled images of every group of sample to be tested and carries out image procossing.
Specifically, described image processor 32 filters out the miscellaneous point of high brightness for including in the sampled images of acquisition first.
The sampled images of plant leaf blade or the surface color of sampled images of test paper that are acquired due to described image acquisition device 31 are simultaneously uneven, and the miscellaneous point of some high brightness present in sampled images will affect the accuracy of detection algorithm.Subsequent processing for convenience, usually will first filter out these miscellaneous points.Traditional method is that Fuzzy Processing, such as Gaussian Blur are filtered to described image, and intermediate value is fuzzy etc., but the filter effect of miscellaneous point biggish for some individuals is poor.
In the present embodiment, the sampled images of the sample to be tested are color image, the sampled images are converted to gray level image first by described image processor 32, then the average gray value of gray level image described in whole picture is calculated, the gray level image is traversed again, the gray value of each pixel of the gray level image is compared with the average gray value, it is greater than the pixel of the average gray value if there is gray value, then the gray value of the pixel is assigned a value of the average gray value by described image processor 32；Otherwise, keep the gray value of the pixel constant.
The characteristics of when spray adhesion also occurs in the sampled images in described image processor 32 according to each image itself, by the adaptive selection segmentation threshold of P parametric method, and divide the region of spray adhesion, in order to the statistics of spray parameter.Specifically, described image processor 32 first calculates the grey level histogram of image, again since minimum gray value, the cumulative distribution histogram of image is calculated, and is chosen with gray value corresponding to the closest cumulative distribution function of target area grayscale image as segmentation threshold.
Described image processor 32 also carries out canny edge detection to the gray level image, and according to edge by the Binary Sketch of Grey Scale Image；Closed operation, object closed onto itself edge are carried out to the binary map；Connected component analysis is carried out to the binary map, each connected region corresponds to a testing result, such as ink droplet or phosphor dot；Statistic mixed-state result number, calculates the parameters such as the diameter, area and circularity of each testing result；And each point that detected is carried out on the sampled images to retouch side, and outline each result with rectangular box, and export picture, so as to intuitive judgment algorithm accuracy.
Step 1307, described image processor 32 uses the spray parameter of unit area in the described image after the calculation processing of pre-set image algorithm, and as unit of the test data of every group of image, exports the parametric plot of the test data of every group of image.Wherein, the spray parameter includes following at least one: density, coverage rate and the partial size of spray.As unit of every group of data, the spray parameter of every group of image is showed in graph form, thus can objectively see very much the variation of data.
Step 1308, the spray parameter according to every group of described image processor 32 calculates the average of every group of spray, uses average as the representative of spray data, and representative power is influenced by observation degree of variation each in spray.By taking spray coverage rate as an example, the average can be calculated by following formula and be obtained:
,
In formula: i=1,2 ..., n；Indicate unit area (1 × 1cm of each image in entire set of image2) image district；
Unit area (1 × 1cm of every image of-expression2) spray area coverage (unit: pixel) in image district；
The average value (unit: pixel) of the spray area coverage of-expression entire set of image.
If the variation of each observation is small, average is strong to the representativeness of spray, if the variation of each observation is big, average representativeness is weak, thus only with the description that takes statistics of feature of the average to a data is incomplete.
Step 1309, the spray parameter according to every group of described image processor 32 calculates the statistic for indicating observation degree of variation size in every group of spray, i.e. standard deviation.It is obtained for example, the standard deviation can be calculated by following formula:
,
In formula:Unit area (1 × 1cm of every image of-expression2) spray area coverage (unit: pixel) in image district；
The standard deviation (unit: pixel) of the spray area coverage of-expression entire set of image.
Step 1310, the spray parameter according to every group of described image processor 32 calculates the coefficient of variation of every group of spray, to measure the distributing homogeneity of spray.
The coefficient of variation is to measure another statistic of each observation degree of variation in spray.When carry out two or more sets spray spray effects compare when, the coefficient of variation can be used to compare.The coefficient of variation is the ratio of standard deviation and average, is denoted as C V.The coefficient of variation can eliminate the influence that unit and (or) average difference compare two or more data variance degree.It is obtained for example, the coefficient of variation can be calculated by following formula:
,
In formula:The standard deviation (unit: pixel) of the spray area coverage of-expression entire set of image；
The average value (unit: pixel) of the spray area coverage of-expression entire set of image；
The coefficient of variation of C V-expression entire set of image spray area coverage.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment method can be realized by means of software and necessary general hardware platform, it can certainly the former be more preferably embodiment by hardware, but in many cases.Based on this understanding, substantially the part that contributes to existing technology can be embodied in the form of software products technical solution of the present invention in other words, the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, CD), it uses including some instructions so that a terminal device (can be mobile phone, computer, server or the network equipment etc.) execute method described in each embodiment of the present invention.
Finally it should be noted that, the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although being described the invention in detail referring to preferred embodiment, those skilled in the art should understand that, it can modify to technical solution of the present invention or equivalent replacement, without departing from the spirit and scope of the technical solution of the present invention.
Claims (60)
- A kind of sprinkling quality detection device characterized by comprisingImage collecting device, for acquiring the sampled images of at least one set of sample to be tested, at least one set for the default sampling area that at least one set sample to be tested is taken respectively from sprinkling region presets sampled point；AndImage processor, for obtaining the sampled images of every group of sample to be tested and carry out image procossing, and using the spray parameter of unit area in the described image after the calculation processing of pre-set image algorithm.
- Sprinkling quality detection device as described in claim 1, which is characterized in that described image processor is when obtaining the sampled images of every group of sample to be tested and carrying out image procossing, the miscellaneous point of high brightness that includes in the sampled images specifically for filtering out acquisition.
- Sprinkling quality detection device as claimed in claim 2, which is characterized in that at the high brightness that described image processor includes in the sampled images for filtering out acquisition miscellaneous, be specifically used for:The sampled images are converted into gray level image；Calculate the average gray value of gray level image described in whole picture；AndThe gray level image is traversed, the gray value of each pixel of the gray level image is compared with the average gray value, gray value is greater than the pixel of the average gray value if it exists, then the gray value of the pixel is assigned a value of the average gray value.
- Sprinkling quality detection device as claimed in claim 3, it is characterized in that, described image processor is when obtaining the sampled images of every group of sample to be tested and carrying out image procossing, the characteristics of when being specifically also used to occur in the sampled images spray adhesion according to each image itself, by the adaptive selection segmentation threshold of P parametric method, and divide the region of spray adhesion.
- Sprinkling quality detection device as claimed in claim 4, which is characterized in that described image processor is specifically used for when through P parametric method adaptive selection segmentation threshold:Calculate the grey level histogram of image；Since minimum gray value, the cumulative distribution histogram of image is calculated；AndIt chooses with gray value corresponding to the closest cumulative distribution function of target area grayscale image as segmentation threshold.
- Sprinkling quality detection device as claimed in claim 5, which is characterized in that described image processor is specifically also used to when obtaining the sampled images of every group of sample to be tested and carrying out image procossing:Canny edge detection is carried out to the gray level image, and according to edge by the Binary Sketch of Grey Scale Image；Closed operation, object closed onto itself edge are carried out to the binary map；Connected component analysis is carried out to the binary map, each connected region corresponds to a testing result；Statistic mixed-state result number, calculates the parameters such as the diameter, area and circularity of each testing result；AndEach point that detected is carried out on the sampled images to retouch side, and each result is outlined with rectangular box, and export picture.
- Sprinkling quality detection device as described in claim 1, it is characterized in that, described image processor is also used to as unit of the test data of every group of image, the parametric plot of the test data of every group of image is exported, the spray parameter includes following at least one: density, coverage rate and the partial size of spray.
- Sprinkling quality detection device as claimed in claim 7, which is characterized in that described image processor is also used to the average that the spray parameter according to every group calculates every group of spray.
- Sprinkling quality detection device as claimed in claim 8, which is characterized in that described image processor is also used to the standard deviation that the spray parameter according to every group calculates every group of spray, with observation degree of variation size in every group of spray of measurement representation.
- Sprinkling quality detection device as claimed in claim 9, which is characterized in that described image processor is also used to the coefficient of variation that the spray parameter according to every group calculates every group of spray, to measure the distributing homogeneity of spray.
- Sprinkling quality detection device as described in claim 1, which is characterized in that the sprinkling quality detection device is a portable electronic device.
- Sprinkling quality detection device as claimed in claim 11, which is characterized in that the portable electronic device is smart phone or tablet computer；And/orDescribed image acquisition device is the camera of the portable electronic device；And/orDescribed image processor is the processor of the portable electronic device.
- Sprinkling quality detection device as described in claim 11 or 12, it is characterized in that, the sprinkling quality detection device further includes display screen, and described image processor is also used to generate a test information set interface before sprinkling quality testing starts, and is shown on the display screen.
- Sprinkling quality detection device as claimed in claim 11, which is characterized in that the sprinkling quality detection device further includes storage device, and the storage device is for storing the acquired image；And/orThe sprinkling quality detection device further includes communication device, for data to be communicated and transmitted with external device (ED), and/or, the communication device is USB interface, WIFI module or bluetooth module.
- A kind of sprinkling quality detecting system characterized by comprisingSample auxiliary device, including shell, darkroom, headlamp and Image Acquisition window, the darkroom is disposed in the housing portion, and completely cut off extraneous light, sampled point is preset for placing sample to be tested, at least one set for the default sampling area that the sample to be tested is taken respectively from sprinkling region in the darkroom；The headlamp is set in the darkroom, and in the light of offer preset wavelength in the darkroom；AndQuality detection device, including image collecting device and image processor are sprayed, described image acquisition device is used to be placed in the sampled images of the dark indoor sample to be tested through the acquisition of described image acquisition window；Described image processor is used to obtain the sampled images of every group of sample to be tested and carries out image procossing, and using the spray parameter of unit area in the described image after the calculation processing of pre-set image algorithm.
- Sprinkling quality detecting system as claimed in claim 15, which is characterized in that described image processor is when obtaining the sampled images of every group of sample to be tested and carrying out image procossing, the miscellaneous point of high brightness that includes in the sampled images specifically for filtering out acquisition.
- Sprinkling quality detecting system as claimed in claim 16, which is characterized in that at the high brightness that described image processor includes in the sampled images for filtering out acquisition miscellaneous, be specifically used for:The sampled images are converted into gray level image；Calculate the average gray value of gray level image described in whole picture；AndThe gray level image is traversed, the gray value of each pixel of the gray level image is compared with the average gray value, gray value is greater than the pixel of the average gray value if it exists, then the gray value of the pixel is assigned a value of the average gray value.
- Sprinkling quality detecting system as claimed in claim 17, it is characterized in that, described image processor is when obtaining the sampled images of every group of sample to be tested and carrying out image procossing, the characteristics of when being specifically also used to occur in the sampled images spray adhesion according to each image itself, by the adaptive selection segmentation threshold of P parametric method, and divide the region of spray adhesion.
- Sprinkling quality detecting system as claimed in claim 18, which is characterized in that described image processor is specifically used for when through P parametric method adaptive selection segmentation threshold:Calculate the grey level histogram of image；Since minimum gray value, the cumulative distribution histogram of image is calculated；AndIt chooses with gray value corresponding to the closest cumulative distribution function of target area grayscale image as threshold value.
- Sprinkling quality detecting system as claimed in claim 19, which is characterized in that described image processor is specifically also used to when obtaining the sampled images of every group of sample to be tested and carrying out image procossing:Canny edge detection is carried out to the gray level image, and according to edge by the Binary Sketch of Grey Scale Image；Closed operation, object closed onto itself edge are carried out to the binary map；Connected component analysis is carried out to the binary map, each connected region corresponds to a testing result；Statistic mixed-state result number, calculates the parameters such as the diameter, area and circularity of each testing result；AndEach point that detected is carried out on the sampled images to retouch side, and each result is outlined with rectangular box, and export picture.
- Sprinkling quality detecting system as claimed in claim 15, it is characterized in that, described image processor is also used to as unit of the test data of every group of image, the parametric plot of the test data of every group of image is exported, the spray parameter includes following at least one: density, coverage rate and the partial size of spray.
- Sprinkling quality detecting system as claimed in claim 21, which is characterized in that described image processor is also used to the average that the spray parameter according to every group calculates every group of spray.
- Sprinkling quality detecting system as claimed in claim 22, which is characterized in that described image processor is also used to the standard deviation that the spray parameter according to every group calculates every group of spray, with observation degree of variation size in every group of spray of measurement representation.
- Sprinkling quality detecting system as claimed in claim 23, which is characterized in that described image processor is also used to the coefficient of variation that the spray parameter according to every group calculates every group of spray, to measure the distributing homogeneity of spray.
- Sprinkling quality detecting system as claimed in claim 15, which is characterized in that the sprinkling quality detection device is a portable electronic device.
- Sprinkling quality detecting system as claimed in claim 25, which is characterized in that the portable electronic device is smart phone or tablet computer；And/orDescribed image acquisition device is the camera of the portable electronic device；And/orDescribed image processor is the processor of the portable electronic device.
- Sprinkling quality detecting system as described in claim 25 or 26, it is characterized in that, the sprinkling quality detection device further includes display screen, and described image processor is also used to generate a test information set interface before sprinkling quality testing starts, and is shown on the display screen；And/orThe sprinkling quality detection device further includes storage device, and the storage device is for storing the acquired image；And/orThe sprinkling quality detection device further includes communication device, for data to be communicated and transmitted with external device (ED), and/or, the communication device is USB interface, WIFI module or bluetooth module.
- Sprinkling quality detecting system as claimed in claim 15, which is characterized in that the headlamp includes at least two kinds of light emitting sources that can issue the light of different wave length.
- Sprinkling quality detecting system as claimed in claim 28, which is characterized in that the headlamp includes at least white light and ultraviolet lamp；Alternatively, the headlamp is LED light or fluorescent lamp.
- Sprinkling quality detecting system as claimed in claim 29, it is characterized in that, the sampling auxiliary device further includes battery and light control switch, the light control switch is set to the outside of the shell, and be electrically connected at least two light emitting sources of the headlamp, being electrically connected between at least two light emitting source and the battery is connected for selecting.
- Sprinkling quality detecting system as claimed in claim 30, which is characterized in that the light control switch is included at least for answering at least two of at least two light emitting source gear and a power-off gear is connected.
- Sprinkling quality detecting system as claimed in claim 30, which is characterized in that further include battery compartment, be disposed in the housing portion, for accommodating the battery；And/orIt further include power interface and electric power management circuit, the power interface receives the voltage input of the external power supply and be transmitted to the electric power management circuit for connecting with external power supply；The electric power management circuit receives the voltage input of the external power supply and charges to the battery；The battery is used to power to the headlamp.
- Sprinkling quality detecting system as claimed in claim 15, it is characterized in that, the sampling auxiliary device further includes localization region, the localization region is set to the housing upper surface, and be arranged side by side with described image acquisition window, for placing the sprinkling quality detection device.
- Sprinkling quality detecting system as claimed in claim 33, it is characterized in that, the sampling auxiliary device further includes fixture, the fixture is set on the localization region, for clamping and fixing the sprinkling quality detection device, and make the image collecting device face described image acquisition window of the sprinkling quality detection device.
- Sprinkling quality detecting system as claimed in claim 34, it is characterized in that, the fixture includes at least two clamping pieces being oppositely arranged, the position of described two clamping pieces being capable of activity adjustment, so as to adjust the distance between described two clamping pieces, so as to clamp various sizes of sprinkling quality detection device.
- Sprinkling quality detecting system as claimed in claim 15, which is characterized in that the sampling auxiliary device further includes lens, and the lens are used to improve the clarity of the sampled images and shorten focal length；And/or the lens parameters of the lens are 10 times of 180 degree wide-angles；And/or the lens are set in described image acquisition window or are set in the darkroom and are located at below described image acquisition window.
- A kind of sprinkling quality determining method, which comprises the following steps:The sampled images of at least one set of sample to be tested are acquired, at least one set for the default sampling area that at least one set sample to be tested is taken respectively from sprinkling region presets sampled point；It obtains the sampled images of every group of sample to be tested and carries out image procossing；Using the spray parameter of unit area in the described image after pre-set image algorithm calculation processing.
- Sprinkling quality determining method as claimed in claim 37, which is characterized in that the step of acquiring the image of at least one set of sample to be tested specifically includes:One darkroom is provided, and the sample to be tested is placed in the darkroom；The light of preset wavelength is selected to irradiate the sample to be tested in the darkroom according to the type of sample to be tested；AndAn Image Acquisition window and/or lens are provided in the top in the darkroom, and are placed in the sampled images of the dark indoor sample to be tested through described image acquisition window and/or lens acquisition.
- Sprinkling quality determining method as claimed in claim 38, which is characterized in that the sample to be tested is the plant blade face that field is sprayed with Fluorescent detector, and the specific light is 365nm ultraviolet light；OrThe sample to be tested is the blank sheet of paper for being sprayed with prepared Chinese ink, and the specific light is white light.
- Sprinkling quality determining method as described in claim 37 or 39, which is characterized in that include: in the step of obtaining the sampled images of every group of sample to be tested and carrying out image procossingFilter out the miscellaneous point of the high brightness for including in the sampled images of acquisition.
- Sprinkling quality determining method as claimed in claim 40, which is characterized in that the high brightness for including in the sampled images for filtering out acquisition miscellaneous the step of includes:The sampled images are converted into gray level image；Calculate the average gray value of gray level image described in whole picture；AndThe gray level image is traversed, the gray value of each pixel of the gray level image is compared with the average gray value, gray value is greater than the pixel of the average gray value if it exists, then the gray value of the pixel is assigned a value of the average gray value.
- Sprinkling quality determining method as claimed in claim 41, which is characterized in that in the step of obtaining the sampled images of every group of sample to be tested and carrying out image procossing further include:The characteristics of when occurring spray adhesion in the sampled images according to each image itself, by the adaptive selection segmentation threshold of P parametric method, and divide the region of spray adhesion.
- Sprinkling quality determining method as claimed in claim 42, which is characterized in that the step of adaptive by P parametric method selection segmentation threshold includes:Calculate the grey level histogram of image；Since minimum gray value, the cumulative distribution histogram of image is calculated；AndIt chooses with gray value corresponding to the closest cumulative distribution function of target area grayscale image as segmentation threshold.
- Sprinkling quality determining method as claimed in claim 43, which is characterized in that in the step of obtaining the sampled images of every group of sample to be tested and carrying out image procossing further include:Canny edge detection is carried out to the gray level image, and according to edge by the Binary Sketch of Grey Scale Image；Closed operation, object closed onto itself edge are carried out to the binary map；Connected component analysis is carried out to the binary map, each connected region corresponds to a testing result；Statistic mixed-state result number, calculates the parameters such as the diameter, area and circularity of each testing result；AndEach point that detected is carried out on the sampled images to retouch side, and each result is outlined with rectangular box, and export picture.
- Sprinkling quality determining method as claimed in claim 37, which is characterized in that further comprise the steps of:As unit of the test data of every group of image, the parametric plot of the test data of every group of image is exported, the spray parameter includes following at least one: density, coverage rate and the partial size of spray.
- Sprinkling quality determining method as claimed in claim 45, which is characterized in that further comprise the steps of:The spray parameter according to every group calculates the average of every group of spray.
- Sprinkling quality determining method as claimed in claim 46, which is characterized in that further comprise the steps of:The spray parameter according to every group calculates the standard deviation of every group of spray, with observation degree of variation size in every group of spray of measurement representation.
- Sprinkling quality determining method as claimed in claim 47, which is characterized in that further comprise the steps of:The spray parameter according to every group calculates the coefficient of variation of every group of spray, to measure the distributing homogeneity of spray.
- Sprinkling quality determining method as claimed in claim 37, which is characterized in that before the step of acquiring the image of the sample to be tested further include:At least one described default sampling area is set in sprinkling region, and sets at least one set of default sampled point in each default sampling area.
- Sprinkling quality determining method as claimed in claim 37, which is characterized in that before the step of acquiring the image of the sample to be tested further include:It generates and shows a test information set interface.
- A kind of sampling auxiliary device characterized by comprisingShell；Darkroom is disposed in the housing portion, and completely cuts off extraneous light, and sampled point is preset for placing sample to be tested, at least one set for the default sampling area that the sample to be tested is taken respectively from sprinkling region in the darkroom；Headlamp is set in the darkroom, and in the light of offer preset wavelength in the darkroom；AndImage Acquisition window, the image acquisition device for being supplied to a sprinkling quality detection device are placed in the sampled images of the dark indoor sample to be tested.
- Sampling auxiliary device as claimed in claim 51, which is characterized in that the headlamp includes at least two kinds of light emitting sources that can issue the light of different wave length.
- Sampling auxiliary device as claimed in claim 52, which is characterized in that the headlamp includes at least white light and ultraviolet lamp；Alternatively, the headlamp is LED light or fluorescent lamp.
- Sampling auxiliary device as claimed in claim 53, it is characterized in that, the sampling auxiliary device further includes battery and light control switch, the light control switch is set to the outside of the shell, and be electrically connected at least two light emitting sources of the headlamp, being electrically connected between at least two light emitting source and the battery is connected for selecting.
- Sampling auxiliary device as claimed in claim 54, which is characterized in that the light control switch is included at least for answering at least two of at least two light emitting source gear and a power-off gear is connected.
- Sampling auxiliary device as claimed in claim 54, which is characterized in that further include battery compartment, be disposed in the housing portion, for accommodating the battery；And/orIt further include power interface and electric power management circuit, the power interface receives the voltage input of the external power supply and be transmitted to the electric power management circuit for connecting with external power supply；The electric power management circuit receives the voltage input of the external power supply and charges to the battery；The battery is used to power to the headlamp.
- Sampling auxiliary device as claimed in claim 51, it is characterized in that, the sampling auxiliary device further includes localization region, the localization region is set to the housing upper surface, and be arranged side by side with described image acquisition window, for placing the sprinkling quality detection device.
- Sampling auxiliary device as claimed in claim 57, it is characterized in that, the sampling auxiliary device further includes fixture, the fixture is set on the localization region, for clamping and fixing the sprinkling quality detection device, and make the image collecting device face described image acquisition window of the sprinkling quality detection device.
- Sampling auxiliary device as claimed in claim 58, it is characterized in that, the fixture includes at least two clamping pieces being oppositely arranged, the position of described two clamping pieces being capable of activity adjustment, so as to adjust the distance between described two clamping pieces, so as to clamp various sizes of sprinkling quality detection device.
- Sampling auxiliary device as claimed in claim 51, which is characterized in that further include lens, the lens are used to improve the clarity of the sampled images and shorten focal length；And/or the lens parameters of the lens are 10 times of 180 degree wide-angles；And/or the lens are set in described image acquisition window or are set in the darkroom and are located at below described image acquisition window.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2015/091776 WO2017063128A1 (en) | 2015-10-12 | 2015-10-12 | Ejection quality testing system, method and sampling auxiliary device |
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| Publication Number | Publication Date |
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| CN107110754A true CN107110754A (en) | 2017-08-29 |
| CN107110754B CN107110754B (en) | 2019-05-03 |
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| CN (1) | CN107110754B (en) |
| WO (1) | WO2017063128A1 (en) |
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| CN109410288A (en) * | 2018-10-18 | 2019-03-01 | 广州极飞科技有限公司 | Methods of exhibiting, the apparatus and system of spraying operation |
| CN109507070A (en) * | 2018-10-22 | 2019-03-22 | 中国农业大学 | Detect the method and device of Droplet deposition |
| CN109709971A (en) * | 2018-12-10 | 2019-05-03 | 北京云无忧大数据科技有限公司 | For the method and apparatus of plant protection, storage medium and electronic equipment |
| CN112547353A (en) * | 2020-11-24 | 2021-03-26 | 张金凤 | Method and system for spraying material |
| WO2021217313A1 (en) * | 2020-04-26 | 2021-11-04 | 深圳市大疆创新科技有限公司 | Spraying evaluation method, device, and storage medium |
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| CN109410288A (en) * | 2018-10-18 | 2019-03-01 | 广州极飞科技有限公司 | Methods of exhibiting, the apparatus and system of spraying operation |
| US12032360B2 (en) | 2018-10-18 | 2024-07-09 | Guangzhou Xaircraft Technology Co., Ltd. | Method, apparatus and system for presenting spraying operation |
| CN109507070A (en) * | 2018-10-22 | 2019-03-22 | 中国农业大学 | Detect the method and device of Droplet deposition |
| CN109709971A (en) * | 2018-12-10 | 2019-05-03 | 北京云无忧大数据科技有限公司 | For the method and apparatus of plant protection, storage medium and electronic equipment |
| WO2021217313A1 (en) * | 2020-04-26 | 2021-11-04 | 深圳市大疆创新科技有限公司 | Spraying evaluation method, device, and storage medium |
| CN112547353A (en) * | 2020-11-24 | 2021-03-26 | 张金凤 | Method and system for spraying material |
| CN114332629A (en) * | 2022-01-06 | 2022-04-12 | 安徽农业大学 | Method for measuring delay of multi-pesticide fog drops impacting leaf surface based on high-speed visual coupling contour feature extraction |
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| CN117689672A (en) * | 2024-01-03 | 2024-03-12 | 广州悦瑞化妆品有限公司 | Facial mask nutrient solution spraying method based on image processing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107110754B (en) | 2019-05-03 |
| WO2017063128A1 (en) | 2017-04-20 |
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