CN109557003A - Pesticide deposition amount detection method and device and data acquisition combination device - Google Patents
Pesticide deposition amount detection method and device and data acquisition combination device Download PDFInfo
- Publication number
- CN109557003A CN109557003A CN201910060614.1A CN201910060614A CN109557003A CN 109557003 A CN109557003 A CN 109557003A CN 201910060614 A CN201910060614 A CN 201910060614A CN 109557003 A CN109557003 A CN 109557003A
- Authority
- CN
- China
- Prior art keywords
- pesticide
- characteristic wave
- blade
- deposition
- characteristic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 239000000575 pesticide Substances 0.000 title claims abstract description 125
- 230000008021 deposition Effects 0.000 title claims abstract description 96
- 238000001514 detection method Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 claims abstract description 27
- 230000008685 targeting Effects 0.000 claims description 13
- 230000005540 biological transmission Effects 0.000 claims description 10
- 238000013527 convolutional neural network Methods 0.000 claims description 8
- 239000007921 spray Substances 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 7
- 239000002131 composite material Substances 0.000 claims description 4
- 238000013135 deep learning Methods 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 3
- 238000009313 farming Methods 0.000 claims description 3
- 239000004973 liquid crystal related substance Substances 0.000 claims description 3
- 239000002362 mulch Substances 0.000 claims description 3
- 238000000513 principal component analysis Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000001678 irradiating effect Effects 0.000 claims description 2
- 238000000151 deposition Methods 0.000 description 72
- 230000008901 benefit Effects 0.000 description 5
- 230000003595 spectral effect Effects 0.000 description 5
- 238000005259 measurement Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 102000004190 Enzymes Human genes 0.000 description 2
- 108090000790 Enzymes Proteins 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 2
- 239000003814 drug Substances 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 238000000746 purification Methods 0.000 description 2
- 238000011897 real-time detection Methods 0.000 description 2
- 238000002310 reflectometry Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000007811 spectroscopic assay Methods 0.000 description 2
- 238000004611 spectroscopical analysis Methods 0.000 description 2
- 230000001256 tonic effect Effects 0.000 description 2
- 229920000742 Cotton Polymers 0.000 description 1
- 241000196324 Embryophyta Species 0.000 description 1
- 240000008042 Zea mays Species 0.000 description 1
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 description 1
- 235000002017 Zea mays subsp mays Nutrition 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000000889 atomisation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004737 colorimetric analysis Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 235000005822 corn Nutrition 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000000857 drug effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 244000037666 field crops Species 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000004817 gas chromatography Methods 0.000 description 1
- 229910052736 halogen Inorganic materials 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000013011 mating Effects 0.000 description 1
- 238000009659 non-destructive testing Methods 0.000 description 1
- 230000005622 photoelectricity Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 238000005507 spraying Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 239000002344 surface layer Substances 0.000 description 1
- 229910052721 tungsten Inorganic materials 0.000 description 1
- 239000010937 tungsten Substances 0.000 description 1
- -1 tungsten halogen Chemical class 0.000 description 1
Classifications
-
- 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
- G01N15/04—Investigating sedimentation of particle suspensions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Dispersion Chemistry (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a pesticide deposit amount detection method and device and a data acquisition combination device, and belongs to the technical field of pesticide deposit amount detection. A pesticide deposition amount detection method for targeted pesticide application comprises the following steps: a. determining a characteristic wave band; b. customizing a characteristic wave light source and a characteristic wave band pass filter; c. collecting data corresponding to the characteristic wave-pesticide concentration; d. obtaining a characteristic wave-pesticide deposition amount relation model; e. and (4) carrying out field detection on the pesticide deposition amount of the crops. A characteristic wave data acquisition combination device comprises four camera devices with the same structure. A pesticide deposition amount detection device for targeted pesticide application comprises the four camera devices and a control circuit, wherein the control circuit comprises a single chip microcomputer MCU and a starting switch. The method has the characteristics of convenience, rapidness, high detection efficiency and the like.
Description
Technical field
The present invention relates to pesticide deposition detection technique fields.
Background technique
, may be smaller in non-course line dose using unmanned plane spray during field pesticides spraying operation, or by day
Gas (e.g., wind direction, wind speed, rainfall etc.) reason is affected, and pesticide is sprayed from spray tank to the entire of target plant blade transmitting
In the process, medical fluid will be by a series of processes such as atomization, flight, shock, rebounds.Inevitably it will appear in this process
The pesticide loss of pesticide droplet drift, droplet evaporation, droplet loss etc.;Therefore, most pesticide droplet is difficult to reach predetermined
Target leaf on, to limit the performance of drug effect.It needs to detect pesticide deposition, and then targetedly carries out
Tonic adjustment, under existing applications of pesticide technical conditions, often using the canopy of crop as study pesticide deposition target, because
And carrying out pesticide tonic according to deposition size is the important means for carrying out targeting application.
Current pesticide deposition detection inhibits the methods of principle and photoelectric colorimetry frequently with enzyme, is suppressed to principle with enzyme
Detection method need to will crops picking blade processing after measure pesticide deposition, be off-line checking method, can not accomplish in real time
Fast nondestructive evaluation makes troubles to the detection of field crops pesticide deposition.Spectral method of detection usually has laboratory testing
Two kinds are detected with field, laboratory testing need to build darkroom, and visible light interference is excluded, crop leaf to be measured is picked and is detected,
Although being not necessarily to damaged blade, can not accomplish in field real-time detection.And portable spectrometer is used to carry out field detection, it is extraneous
Visible light has certain interference, and spectrometer involves great expense, and is unsuitable for agricultural production practice.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of pesticide deposition quantity measuring method, device and data acquisition combinations
Device can at the scene realize pesticide deposition and directly detect, have the features such as convenient and efficient, detection efficiency is high.
In order to solve the above technical problems, the technical solution used in the present invention is:
A kind of pesticide deposition quantity measuring method for targeting application, comprising the following steps:
A. characteristic wave bands determine, configure the pesticide solution of various concentration, choose one group of crops blade, each blade is sprayed
The pesticide solution of various concentration, to form the pesticide deposition of various concentration on each blade, to form characteristic wave bands sampling
Sample irradiates each blade using composite light source in dark room conditions, is deposited by spectrometer collection by the pesticide of carrier of blade
Reflectivity data and wavelength data are measured, using Principal Component Analysis, determination can most react different pesticide deposition reflectance signature waves
Four characteristic wave bands;
B. custom features wave source and characteristic wave bandpass filter, for the wave of each characteristic wave bands of four characteristic wave bands
It is long, customize corresponding feature wave source and characteristic wave bandpass filter respectively, four customized feature wave sources and
Four characteristic wave bandpass filters are respectively only capable of the light of sending or the wavelength by characteristic wave bands corresponding thereto;
C. acquisition characteristics wave~pesticide concentration corresponding data is matched according to the detection range of crops blade pesticide deposition
The pesticide solution of various concentration is set, one group of crops blade is chosen, is sprayed respectively on each blade in this group of blade different dense
The pesticide solution of degree makes the pesticide of each crops blade in the group to form the pesticide deposition of various concentration on each blade
The detection range of deposition discrete mulch farming object blade pesticide deposition on the whole, to form characteristic wave~pesticide deposition
Concentration corresponding data collecting sample is measured, the light reflection intensity data of four characteristic wave bands in the sample per a piece of blade is carried out
Acquisition, the light reflection intensity data acquisition method of characteristic wave bands are as follows: respectively by four feature wave sources to being loaded with different pesticides
Each blade of deposition is irradiated, and using camera through characteristic wave bandpass filter corresponding to every kind of feature wave source into
Row shooting, to collect in image information form using blade as four spies corresponding to the pesticide deposition of each concentration of carrier
Levy the light reflection intensity data of wave band;
D. obtain characteristic wave-pesticide deposition relational model, by the pesticide deposition of each concentration obtained in step c and its
The light reflection intensity data of four kinds of corresponding characteristic wave bands is directed respectively into CNN convolutional neural networks and carries out deep learning training,
The light reflection intensity data corresponding relationship of system automatically generated pesticide deposition and four kinds of characteristic wave bands, to obtain with four spies
The light reflection intensity data for levying wave band is input, and pesticide deposition is characteristic wave-pesticide deposition relational model of output;
E. on-site test is carried out to the pesticide deposition of crops, according to the light reflected intensity of the characteristic wave bands in step c
Collecting method acquires crops blade for the light reflection intensity data of four characteristic wave bands, and by four spies collected
Light reflection intensity data input feature vector wave-pesticide deposition relational model of wave band is levied, to obtain to acquire four features
The crops blade of the light reflection intensity data of wave band is the crops pesticide deposition of sample.
A kind of characteristic waves are combined the unit according to acquisition, and combination unit includes the identical photographic device of four structures, respectively
Photographic device I, photographic device II, photographic device III and photographic device IV, they include CCD camera, above-mentioned characteristic wave
Light source and above-mentioned characteristic wave bandpass filter, feature wave source is arranged on CCD camera, for irradiating CCD camera
The camera lens front end of CCD camera is arranged in viewfinder range, characteristic wave bandpass filter, so that CCD camera acquisition characteristics wavestrip
The feature wave source of the light for the characteristic wave bands wavelength that pass filter is penetrated, each photographic device is opposite with characteristic wave bandpass filter
It answers, so that the light reflection intensity data of four characteristic wave bands is acquired by each photographic device respectively.
It is a kind of for targeting the pesticide deposition amount detection device of application, including above-mentioned four photographic devices and control electricity
Road, control circuit include single-chip microprocessor MCU and start switch, start switch issued on-off model and conveyed by the port I/O
To single-chip microprocessor MCU, single-chip microprocessor MCU issues timing control signal by four ports I/O, successively controls four photographic devices
The open and close of CCD camera, four CCD cameras acquisition picture signal respectively by respective I/O port transmission extremely
Single-chip microprocessor MCU, the operation output signal of single-chip microprocessor MCU is through I/O port transmission to display device.
The present invention further improvement lies in that:
Display device is YM12232B type liquid crystal display.
Single-chip microprocessor MCU also passes through the port I/O and connect with zigbee wireless communication module, to realize the operation of single-chip microprocessor MCU
Output signal wireless transmission.
The beneficial effects of adopting the technical scheme are that
Advantage 1: crop pesticide application deposition detects at present, needs to pick blade or even crushing mostly, in the lab
It is measured, efficiency is lower.The present invention can detect crop pesticide deposition in field, can be realized lossless, real-time
Detection, fast speed, timeliness is higher, can real-time transmission data, for farmland spray machine device people or spray operator into
Row fast variable spray, promptly and accurately supplements pesticide to scarce medicine region, not exposed not excess, does not repeat to spray to medicine region is not lacked
It applies.And without picking blade, crop is not destroyed, realizes non-destructive testing effect.
Advantage 2: traditional chemical detection method, chromatographic detection method etc., step is more, sample extraction, purification and etc.
Expend certain time, and this patent use spectral method of detection without sample extraction, purification and etc., directly measurement, accurately
Efficiently.
Advantage 3: previous spectral method of detection mostly uses spectrometer directly to detect, spectrometer purchase and maintenance cost compared with
For valuableness, the equipment of measurement requirement can satisfy mostly more than hundreds of thousands member, higher cost is unfavorable for agricultural production practice.And
The equipment built according to method proposed by the present invention, it is only necessary to feature wave source, characteristic wave bandpass filter, CCD camera, control
The devices such as circuit processed carry out building combination, and cost is greatly reduced on the basis of meeting measurement demand in thousands of members or so
This, a possibility that having spectroscopic assay pesticide deposition in farmland production operation to peasant household's Promotion practice.
Advantage 4: previous spectroscopic assay pesticide deposition method acquires crop leaf spectroscopic data information by spectroscopy equipment,
It is handled again after export data information to determine deposition.Data are acquired and are divided by method and apparatus proposed by the invention
Analysis is integrated in same control chip, control chip is incorporated into after founding mathematical models, the inputting mathematical after equipment collects data
Model automatically derives deposition output data.
Advantage 5: spectral image information and pesticide deposition corresponding relationship model foundation are highly efficient compared with conventional method, quasi-
Really.The present invention uses CNN (convolutional neural networks) deep learning method, automatically by spectral image information and pesticide deposition information
It is trained modeling, compared with machine learning methods such as traditional neural network, support vector machines, dimensionality reduction efficiency is higher, accurately
Degree is higher, substantially increases mathematical model accuracy, corresponding relationship modeling difficulty and time is reduced, to make measurement result more
It is accurate.
Pesticide deposition amount detection device for targeting application can be realized to be automatically performed by taking pictures to deposition to calculate to export.
With the features such as convenient and efficient, detection efficiency is high.
Detailed description of the invention
Fig. 1 is the flow chart of pesticide deposition detection method in the application;
Fig. 2 is the structural schematic diagram that characteristic waves combine the unit each photographic device according to acquisition in the application;
Fig. 3 is in the application for targeting the structural schematic diagram of the pesticide deposition amount detection device of application;
Fig. 4 is in the application for targeting the structural schematic diagram of the pesticide deposition amount detection device control circuit of application.
In the accompanying drawings: 1.CCD camera;The camera lens of 1-1.CCD camera;2. feature wave source;3. characteristic wave band logical is filtered
Mating plate;
Specific embodiment
Below in conjunction with the drawings and specific embodiments, the present invention will be described in further detail.
Referring to Fig. 1, a kind of pesticide deposition quantity measuring method for targeting application, comprising the following steps:
A. characteristic wave bands determine, configure the pesticide solution of various concentration, choose one group of crops blade, each blade is sprayed
The pesticide solution of various concentration, to form the pesticide deposition of various concentration on each blade, to form characteristic wave bands sampling
Sample irradiates each blade using composite light source in dark room conditions, is deposited by spectrometer collection by the pesticide of carrier of blade
Reflectivity data and wavelength data are measured, using Principal Component Analysis, determination can most react different pesticide deposition reflectance signature waves
Four characteristic wave bands;
B. custom features wave source and characteristic wave bandpass filter, for the wave of each characteristic wave bands of four characteristic wave bands
It is long, customize corresponding feature wave source and characteristic wave bandpass filter respectively, four customized feature wave sources and
Four characteristic wave bandpass filters are respectively only capable of the light of sending or the wavelength by characteristic wave bands corresponding thereto;
C. acquisition characteristics wave~pesticide concentration corresponding data is matched according to the detection range of crops blade pesticide deposition
The pesticide solution of various concentration is set, one group of crops blade is chosen, is sprayed respectively on each blade in this group of blade different dense
The pesticide solution of degree makes the pesticide of each crops blade in the group to form the pesticide deposition of various concentration on each blade
The detection range of deposition discrete mulch farming object blade pesticide deposition on the whole, to form characteristic wave~pesticide deposition
Concentration corresponding data collecting sample is measured, the light reflection intensity data of four characteristic wave bands in the sample per a piece of blade is carried out
Acquisition, the light reflection intensity data acquisition method of characteristic wave bands are as follows: respectively by four feature wave sources to being loaded with different pesticides
Each blade of deposition is irradiated, and using camera through characteristic wave bandpass filter corresponding to every kind of feature wave source into
Row shooting, to collect in image information form using blade as four spies corresponding to the pesticide deposition of each concentration of carrier
Levy the light reflection intensity data of wave band;
D. obtain characteristic wave-pesticide deposition relational model, by the pesticide deposition of each concentration obtained in step c and its
The light reflection intensity data of four kinds of corresponding characteristic wave bands is directed respectively into CNN convolutional neural networks and carries out deep learning training,
The light reflection intensity data corresponding relationship of system automatically generated pesticide deposition and four kinds of characteristic wave bands, to obtain with four spies
The light reflection intensity data for levying wave band is input, and pesticide deposition is characteristic wave-pesticide deposition relational model of output;
E. on-site test is carried out to the pesticide deposition of crops, according to the light reflected intensity of the characteristic wave bands in step c
Collecting method acquires crops blade for the light reflection intensity data of four characteristic wave bands, and by four spies collected
Light reflection intensity data input feature vector wave-pesticide deposition relational model of wave band is levied, to obtain to acquire four features
The crops blade of the light reflection intensity data of wave band is the crops pesticide deposition of sample.
Referring to fig. 2, a kind of characteristic waves are combined the unit according to acquisition, and combination unit includes the identical camera shooting dress of four structures
Set, respectively photographic device I, photographic device II, photographic device III and photographic device IV, they include CCD camera 1, on
(and above-mentioned characteristic wave bandpass filter 3, feature wave source 2 are arranged on CCD camera 1, are used for the feature wave source 2 stated
The viewfinder range of CCD camera 1 is irradiated, the front end camera lens 1-1 of CCD camera 1 is arranged in characteristic wave bandpass filter 3, so that
The light for the characteristic wave bands wavelength that 1 acquisition characteristics wavestrip pass filter 3 of CCD camera is penetrated, the feature glistening light of waves of each photographic device
Source 2 is corresponding with characteristic wave bandpass filter 3, so that the light reflection intensity data of four characteristic wave bands is respectively by each photographic device
It is acquired.
Each blade for being loaded with different pesticide depositions is shot respectively by the present apparatus, thus in the form of image information
It collects using blade as the light reflection intensity data of four characteristic wave bands corresponding to the pesticide deposition of each concentration of carrier, with
It is the characteristic wave-pesticide deposition relational model exported that generation, which is ready for use on, with pesticide deposition.
It is a kind of for targeting the pesticide deposition amount detection device of application referring to Fig. 3~Fig. 4, including four above-mentioned camera shootings
Device and control circuit, control circuit include single-chip microprocessor MCU and start switch, start switch issued on-off model and pass through
The port I/O is delivered to single-chip microprocessor MCU, and single-chip microprocessor MCU issues timing control signal by four ports I/O, successively controls four
The open and close of the CCD camera of photographic device, four characteristic wave bands that four CCD cameras are acquired in the form of image information
Light reflection intensity data pass through respective I/O port transmission to single-chip microprocessor MCU, the operation output signal of single-chip microprocessor MCU respectively
Through I/O port transmission to display device.
Display device is YM12232B type liquid crystal display.
Single-chip microprocessor MCU also passes through the port I/O and connect with zigbee wireless communication module, to realize the operation of single-chip microprocessor MCU
Output signal wireless transmission.
The operational formula of single-chip microprocessor MCU is based on characteristic wave-pesticide deposition relational model, feature in present apparatus control circuit
Wave-pesticide deposition relational model in the application for targeting the pesticide deposition quantity measuring method of application by obtaining, herein no longer
It repeats.
Using the common pesticide fenifrothion deposition in corn surface layer as test object, according to equipment disclosed by the invention and
The application method of equipment operates, and composite light source (marine optics Vivo tungsten halogen lamp) and spectrometer are used in dark room conditions
(model of CAMLIN company production: VNIR-SWIR spectrometer) determination can most react different pesticide deposition reflectance signature waves
The wavelength of four characteristic wave bands is respectively respectively 650nm, 830nm, 1150nm, 1581nm, is passed through (Sen Quan photoelectricity manufacturer)
Four feature wave sources and corresponding four characteristic wave bandpass filters are customized, are combined the unit by characteristic waves according to acquisition
The light reflection intensity data of four characteristic wave bands of (wherein 1 model of CCD camera: PCO1600) acquisition, is examined by pesticide deposition
Survey device to cotton crops carry out pesticide deposition detection, testing result and gas-chromatography detection method detection result into
Row comparison, obtains predictablity rate, comparing result see the table below:
Claims (5)
1. a kind of pesticide for targeting application deposits quantity measuring method, it is characterised in that: the described method comprises the following steps:
A. characteristic wave bands determine, configure the pesticide solution of various concentration, choose one group of crops blade, each blade are sprayed different
The pesticide solution of concentration, to form the pesticide deposition of various concentration on each blade, so that characteristic wave bands sample is formed,
Each blade is irradiated using composite light source in dark room conditions, is reflected by spectrometer collection by the pesticide deposition of carrier of blade
Rate data and wavelength data determine four for capable of most reacting different pesticide deposition reflectance signature waves using Principal Component Analysis
Characteristic wave bands;
B. custom features wave source and characteristic wave bandpass filter, for the wave of each characteristic wave bands of four characteristic wave bands
It is long, corresponding feature wave source and characteristic wave bandpass filter, the feature glistening light of waves of customized four are customized respectively
Source and four characteristic wave bandpass filters are respectively only capable of the light of sending or the wavelength by characteristic wave bands corresponding thereto;
C. acquisition characteristics wave~pesticide concentration corresponding data, according to the detection range of crops blade pesticide deposition, configuration is not
With the pesticide solution of concentration, one group of crops blade is chosen, sprays various concentration respectively on each blade in this group of blade
The pesticide solution deposits the pesticide of each crops blade in the group to form the pesticide deposition of various concentration on each blade
The detection range for measuring mulch farming object blade pesticide deposition discrete on the whole, so that it is dense to form characteristic wave~pesticide deposition
Corresponding data collecting sample is spent, the light reflection intensity data of four characteristic wave bands in the sample per a piece of blade is carried out
Acquisition, the light reflection intensity data acquisition method of characteristic wave bands are as follows: respectively by four feature wave sources to being loaded with difference
Each blade of pesticide deposition is irradiated, and using camera through the characteristic wave corresponding to every kind of feature wave source
Bandpass filter is shot, to collect the pesticide deposition institute using blade as each concentration of carrier in image information form
The light reflection intensity data of corresponding four characteristic wave bands;
D. characteristic wave-pesticide deposition relational model is obtained, the pesticide deposition of each concentration obtained in step c and its institute is right
The light reflection intensity data for the four kinds of characteristic wave bands answered is directed respectively into CNN convolutional neural networks and carries out deep learning training,
The light reflection intensity data corresponding relationship of system automatically generated pesticide deposition and four kinds of characteristic wave bands, to obtain with four spies
The light reflection intensity data for levying wave band is input, and pesticide deposition is characteristic wave-pesticide deposition relational model of output;
E. on-site test is carried out to the pesticide deposition of crops, according to the light reflected intensity of characteristic wave bands described in step c
Collecting method acquires crops blade for the light reflection intensity data of four characteristic wave bands, and by collected four
Light reflection intensity data input feature vector wave-pesticide deposition relational model of a characteristic wave bands, to obtain described to acquire
The crops blade of the light reflection intensity data of four characteristic wave bands is the crops pesticide deposition of sample.
2. a kind of characteristic waves are combined the unit according to acquisition, which is characterized in that the combination unit, which includes that four structures are identical, to be taken the photograph
As device, respectively photographic device I, photographic device II, photographic device III and photographic device IV, they include CCD camera
(1), feature wave source (2) as described in claim 1 and characteristic wave bandpass filter as described in claim 1 (3), it is described
Feature wave source (2) is arranged on the CCD camera (1), described for irradiating the viewfinder range of the CCD camera (1)
Characteristic wave bandpass filter (3) is arranged in front end camera lens (1-1) of the CCD camera (1), so that the CCD camera (1)
Acquire the light for the characteristic wave bands wavelength that the characteristic wave bandpass filter (3) is penetrated, the feature glistening light of waves of each photographic device
Source (2) is corresponding with characteristic wave bandpass filter (3), so that the light reflection intensity data of four characteristic wave bands is each respectively
The photographic device is acquired.
3. a kind of for targeting the pesticide deposition amount detection device of application, which is characterized in that including as claimed in claim 2 four
A photographic device and control circuit, the control circuit include that single-chip microprocessor MCU is issued with described start switch is started switch
On-off model is delivered to the single-chip microprocessor MCU by the port I/O, and the single-chip microprocessor MCU issues timing by four ports I/O
Signal is controlled, the open and close of the CCD camera of four photographic devices are successively controlled, four CCD cameras are adopted
Respectively by respective I/O port transmission to the single-chip microprocessor MCU, the operation of the single-chip microprocessor MCU exports the picture signal of collection
Signal is through I/O port transmission to display device.
4. according to claim 3 a kind of for targeting the pesticide deposition amount detection device of application, it is characterised in that: described
Display device is YM12232B type liquid crystal display.
5. according to claim 3 or 4 a kind of for targeting the pesticide deposition amount detection device of application, it is characterised in that:
The single-chip microprocessor MCU also passes through the port I/O and connect with zigbee wireless communication module, to realize the operation of the single-chip microprocessor MCU
Output signal wireless transmission.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910060614.1A CN109557003B (en) | 2019-01-23 | 2019-01-23 | Pesticide deposition amount detection method and device and data acquisition combination device |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910060614.1A CN109557003B (en) | 2019-01-23 | 2019-01-23 | Pesticide deposition amount detection method and device and data acquisition combination device |
Publications (2)
Publication Number | Publication Date |
---|---|
CN109557003A true CN109557003A (en) | 2019-04-02 |
CN109557003B CN109557003B (en) | 2024-08-02 |
Family
ID=65873815
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201910060614.1A Active CN109557003B (en) | 2019-01-23 | 2019-01-23 | Pesticide deposition amount detection method and device and data acquisition combination device |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109557003B (en) |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110057764A (en) * | 2019-04-25 | 2019-07-26 | 浙江省农业科学院 | A kind of pesticide application safety management alarming device and method |
CN110100596A (en) * | 2019-06-03 | 2019-08-09 | 河北农业大学 | Light supplementing and sterilizing method and device for crops and data acquisition device |
CN112730275A (en) * | 2021-02-04 | 2021-04-30 | 华东理工大学 | Micro-spectral imaging system, pesticide detection system and method |
CN113008742A (en) * | 2021-02-23 | 2021-06-22 | 中国农业大学 | Method and system for detecting deposition amount of fog drops |
CN113252522A (en) * | 2021-05-12 | 2021-08-13 | 中国农业大学 | Hyperspectral scanning-based device for measuring deposition amount of fog drops on plant leaves |
CN113589846A (en) * | 2021-08-27 | 2021-11-02 | 河北农业大学 | System and method for droplet control under wind field monitoring based on unmanned aerial vehicle pesticide spraying |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1180839A (en) * | 1996-08-01 | 1998-05-06 | 株式会社佐竹制作所 | Content measuring apparatus for plant leaf |
CN1677105A (en) * | 2004-04-01 | 2005-10-05 | 安捷伦科技有限公司 | Optoelectronic rapid diagnostic test system |
CN101592659A (en) * | 2009-02-09 | 2009-12-02 | 马义才 | A kind of based on the test strip quantitative detection system and the method thereof that continue fluorescent-substance markers |
CN204405523U (en) * | 2015-03-11 | 2015-06-17 | 中国科学院地理科学与资源研究所 | A kind of crop nitrogen nutrition diagnostic equipment |
US20160069743A1 (en) * | 2014-06-18 | 2016-03-10 | Innopix, Inc. | Spectral imaging system for remote and noninvasive detection of target substances using spectral filter arrays and image capture arrays |
CN209525221U (en) * | 2019-01-23 | 2019-10-22 | 河北农业大学 | Pesticide deposit amount characteristic wave data acquisition and pesticide deposit amount detection device |
-
2019
- 2019-01-23 CN CN201910060614.1A patent/CN109557003B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1180839A (en) * | 1996-08-01 | 1998-05-06 | 株式会社佐竹制作所 | Content measuring apparatus for plant leaf |
CN1677105A (en) * | 2004-04-01 | 2005-10-05 | 安捷伦科技有限公司 | Optoelectronic rapid diagnostic test system |
CN101592659A (en) * | 2009-02-09 | 2009-12-02 | 马义才 | A kind of based on the test strip quantitative detection system and the method thereof that continue fluorescent-substance markers |
US20160069743A1 (en) * | 2014-06-18 | 2016-03-10 | Innopix, Inc. | Spectral imaging system for remote and noninvasive detection of target substances using spectral filter arrays and image capture arrays |
CN204405523U (en) * | 2015-03-11 | 2015-06-17 | 中国科学院地理科学与资源研究所 | A kind of crop nitrogen nutrition diagnostic equipment |
CN209525221U (en) * | 2019-01-23 | 2019-10-22 | 河北农业大学 | Pesticide deposit amount characteristic wave data acquisition and pesticide deposit amount detection device |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110057764A (en) * | 2019-04-25 | 2019-07-26 | 浙江省农业科学院 | A kind of pesticide application safety management alarming device and method |
CN110057764B (en) * | 2019-04-25 | 2021-09-14 | 浙江省农业科学院 | Pesticide application safety management warning device and method |
CN110100596A (en) * | 2019-06-03 | 2019-08-09 | 河北农业大学 | Light supplementing and sterilizing method and device for crops and data acquisition device |
CN110100596B (en) * | 2019-06-03 | 2023-08-29 | 河北农业大学 | Crop light supplementing and sterilizing method and device and data acquisition device |
CN112730275A (en) * | 2021-02-04 | 2021-04-30 | 华东理工大学 | Micro-spectral imaging system, pesticide detection system and method |
CN113008742A (en) * | 2021-02-23 | 2021-06-22 | 中国农业大学 | Method and system for detecting deposition amount of fog drops |
CN113252522A (en) * | 2021-05-12 | 2021-08-13 | 中国农业大学 | Hyperspectral scanning-based device for measuring deposition amount of fog drops on plant leaves |
CN113252522B (en) * | 2021-05-12 | 2022-03-15 | 中国农业大学 | Hyperspectral scanning-based device for measuring deposition amount of fog drops on plant leaves |
CN113589846A (en) * | 2021-08-27 | 2021-11-02 | 河北农业大学 | System and method for droplet control under wind field monitoring based on unmanned aerial vehicle pesticide spraying |
Also Published As
Publication number | Publication date |
---|---|
CN109557003B (en) | 2024-08-02 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109557003A (en) | Pesticide deposition amount detection method and device and data acquisition combination device | |
CN209525221U (en) | Pesticide deposit amount characteristic wave data acquisition and pesticide deposit amount detection device | |
Liu et al. | Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing | |
CN111582055B (en) | Unmanned aerial vehicle aviation pesticide application route generation method and system | |
CN104931470B (en) | A kind of pesticide residue detection device and detection method based on fluorescent high spectral technology | |
CN101382488B (en) | Method for detecting nitrogen content in fresh tea by visible light-near infrared diffuse reflection spectrum technology | |
CN107110754B (en) | Spray quality detection device, system, method and sampling auxiliary device | |
CN107084790A (en) | Portable spectrometer and its spectral method of detection based on smart mobile phone | |
TWI708546B (en) | Liquid spraying method of drone system and artificial intelligence image processing technology | |
CN110122456A (en) | Intelligent mosquito dispelling detection system and detection method | |
CN208905370U (en) | A kind of device that the blade face medicine based on plant space prescription map sprays | |
CA2896035A1 (en) | Methods and systems for automated micro farming | |
US20180018537A1 (en) | Non-spectroscopic imaging of plants | |
CN107044959A (en) | Micro- multi-modal fusion spectral detection system | |
US20180373937A1 (en) | Methods and systems for automated micro farming | |
CN109984105A (en) | A kind of intelligent monitoring system of Landscape Construction | |
CN109827957A (en) | A kind of rice leaf SPAD value estimating and measuring method based on computer vision and system | |
CN108935413B (en) | Foliage medicine spraying device and method based on plant space prescription diagram | |
CN107727542A (en) | A kind of device for fast detecting and detection method that droplet is sprayed suitable for unmanned plane | |
CN105753509A (en) | Humidity sensing ceramic, and preparation method and application thereof | |
CN103731440A (en) | Near-infrared crop growth information real-time monitoring and crop disaster prediction wireless system | |
CN109634225A (en) | A kind of industrialized agriculture intelligent management system | |
West et al. | Detection of fungal diseases optically and pathogen inoculum by air sampling | |
Yao et al. | Design and testing of an active light source apparatus for crop growth monitoring and diagnosis | |
CN107678372A (en) | Automatic medicine sprayer based on Internet of Things agricultural pest intelligent monitoring |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |