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 PDF

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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
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pesticide
characteristic wave
blade
deposition
characteristic
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CN109557003B (en
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孙磊
郝建军
李建昌
金俊宝
赵建国
索雪松
马志凯
马跃进
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Hebei Qianpeng Agricultural Machinery Technology Co ltd
Heibei Agricultural University
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Heibei Agricultural University
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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

A kind of pesticide deposition quantity measuring method, device and data acquisition combination unit
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.
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Cited By (6)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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

Patent Citations (6)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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

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