CN108982521A - Visualize the horizontal detection device of soil health - Google Patents
Visualize the horizontal detection device of soil health Download PDFInfo
- Publication number
- CN108982521A CN108982521A CN201810883433.4A CN201810883433A CN108982521A CN 108982521 A CN108982521 A CN 108982521A CN 201810883433 A CN201810883433 A CN 201810883433A CN 108982521 A CN108982521 A CN 108982521A
- Authority
- CN
- China
- Prior art keywords
- video information
- moment
- submodule
- soil
- high frequency
- 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.)
- Withdrawn
Links
Classifications
-
- 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
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Image Processing (AREA)
Abstract
In order to avoid the characteristic of sensor oneself requirement fixed form installation monitors the blind area that may cause to blowdown, the present invention provides a kind of horizontal detection devices of visualization soil health, for to forest farm or pasture by being monitored in the recovery process after chemical contamination to soil health level, remote monitoring server including detecting soil health level indirectly in turn for visually monitoring vegetation, further includes: acquiring video information unit, ARM unit and mark unit.The present invention can be taken photo by plane by equipment such as unmanned planes and in the way of machine learning, obtain the growth characteristics such as the color of vegetation and state, and then carry out simple, quickly judgement to recovery situation of the soil after being polluted.
Description
Technical field
The present invention relates to environment monitoring techniques fields, set more particularly, to a kind of detection of visualization soil health level
It is standby.
Background technique
Soil pollution is the environmental problem being on the rise, and directly threatens the safety and soil ecology function of human food's health
The sustainable development of energy.It has attracted wide public concern about the toxicity of contaminated soil and with risk assessment, but has only combined
The interaction between soil pollutant and organism can be just effectively detected in the method for chemistry and biology.
Currently, Soil K+adsorption instrument point includes following several: 1) for the soil sample collector of soil pre-treatment, soil vibration sieve
Instrument, cutting ring, soil sieve, soil liquid sampler etc.；2) by soil nutrient detection soil EC based on, soil nutrient tacheometer,
Desk-top near-infrared soil nutrient tacheometer, hand-held soil nutrient tacheometer, nylon mesh, azotometer, ionometer, PH meter, atom
Absorption spectrophotometer, inscription hollow cathode lamp, second block steel cylinder, Atomic Absorption Spectrometer, atomic fluorescence spectrophotometer, mercury vapourmeter, drop
Determine instrument, gas chromatograph, spectrophotometer, conductivity meter, soil salt analyzer etc.；3) for the portable of soil moisture detection
Formula soil moisture content quick analyser, soil moisture content quick analyser, portable soil soil moisture content analyzer, soil moisture temperature tacheometer, drying
Method infrared moisture tester, soil moisture temperature tacheometer etc.；4) it is used for the digital soil hardometer of soil hardness detection, refers to
Pin type stratameter, soil density analyzer etc.；5) based on the P in soil H of soil acidity or alkalinity detection, pointer soil acid
Spend meter, digital soil acidometer etc..
In the prior art, application No. is the Chinese invention patent applications of CN201410220674.2 to disclose one kind based on object
Enterprise's rainwater discharge outlet monitoring system of networking, including field data acquisition device, Internet of Things monitoring center and emergency processing dress
It sets, the field data acquisition device includes monitor, water quality testing meter, integrated water pump；The Internet of Things monitoring center packet
Include central processing unit, controller, display, emergency alarm device, 3G warning module；The emergency treatment device includes recirculation water
Pump, several electrically operated valves, emergency lagoon, the field data acquisition device, display, controller and communication system are and centre
Device connection is managed, each electrically operated valve is connect with controller, and the emergency alarm device and 3G warning module pass through wired or nothing
Line mode is connected with communication system, and the monitor and water quality testing meter are mounted on the water inlet of integrated pump station.However, including
The prior art including this mode requires to be arranged many Soil K+adsorption equipment to scene, and the purchase cost of these equipment compared with
Height, installation cost are higher, and maintenance cost is higher.
In addition, the moment monitors the data that these equipment or regular monitoring these equipment obtain, it is difficult to which accurately reflecting it is
It is no the problem of soil secondary pollution occur.For current soil, pollution and repair process be typically all compared with
It is slow.Above-mentioned Soil K+adsorption equipment is unsuitable for being fixedly secured to these scenes, and otherwise efficiency is extremely low.And artificial scene inspection
The cost of survey is also higher, and detection activity is restricted by factors such as environment, geographical locations, thus what artificial on-site test obtained
Data accuracy leaves a question open.
Summary of the invention
In order to improve the efficiency of soil remediation detection and contamination monitoring, monitoring cost is reduced, the present invention provides one kind can
Depending on changing the horizontal detection device of soil health, for forest farm or pasture by the recovery process after chemical contamination to soil health
Level is monitored, the remote monitoring service including detecting soil health level indirectly in turn for visually monitoring vegetation
Device, further includes:
Acquiring video information unit, the image letter of the vegetation for shooting soil region to be detected using unmanned plane mode
Breath, obtains video information；
Video Transmission Unit, the video information transmission for obtaining the acquiring video information unit take to remote monitoring
Business device unit；
ARM unit, the video information for being obtained according to the acquiring video information unit carry out the reparation state of soil
Detection；
Unit is identified, the remote monitoring server unit is located at, the mark unit is used for institute according to the pre-stored data
The feature that vegetation restores after by the pollution of different organic pollutants is stated, is compared with the result of the ARM unit, so that it is determined that having
The possibility type of machine pollutant.
Further, the acquiring video information unit includes:
Latitude and longitude information obtains subelement, for obtaining the latitude and longitude information at each moment；
Soil remediation state determines subelement, for determining soil remediation state.
Further, the latitude and longitude information acquisition subelement includes:
It corrects video information image-latitude and longitude information packet and obtains module, for obtaining the vegetation of soil region to be detected
Video information is corrected, and the corresponding latitude and longitude information of video information after being corrected, according to the video information after correction
Image-latitude and longitude information packet.
Further, the soil remediation state determines that subelement is believed according to the video information image after correction-longitude and latitude
Breath packet carries out the determination of soil remediation state.
Further, the correction video information image-latitude and longitude information packet acquisition module includes:
Assuming that the T0 moment, the T1 moment, the T2 moment ..., the Tn moment be corresponding n+1 consecutive hours in the video information
It carves, wherein n is the natural number greater than 4；
The video information at T0 and T1 moment is converted to image respectively for carrying out framing to video information by framing module
Information Img0 and Img1, and obtain the correction coefficient of low frequency sub-band signal and the correction coefficient of high frequency subband signals；
Low frequency sub-band signal correction module, for according to the correction coefficient of low frequency sub-band signal and the school of high frequency subband signals
Positive coefficient, to the T2 moment ..., the video information at Tn moment is corrected；
Sorting module, for the corrected T0 moment, the T1 moment, the T2 moment ..., the video information at Tn moment into
Row sequence；
Video information image-latitude and longitude information packet generation module, for generating video information image-latitude and longitude information packet.
Further, the framing module includes:
First subband signal obtains submodule and is corresponding in turn to for carrying out wavelet transformation respectively to Img0 and Img1
In the low frequency sub-band signal L at T0 moment and T1 moment0, high frequency subband signals L1And high frequency subband signals H0, high frequency subband signals
H1；
Low frequency sub-band signal correction coefficient computational submodule, for calculating the correction coefficient C (x, y) of low frequency sub-band signalL:
Wherein, the x and y respectively indicates the abscissa and ordinate of some pixel in the frame image at T0 moment, βmIt indicates
The mean value of correction matrix, ηmIndicate correction matrix variance, the correction matrix be withFor variance,For mean value
2 rank diagonal matrix B；
Submodule is filtered, for, by Gaussian filter, obtaining H ' to high frequency subband signals0And H '1:
High frequency subband signals correction coefficient computational submodule calculates for the frame image for the T0 moment and is located at the position (x, y)
The correction coefficient C (x, y) of the high frequency subband signals for the pixel setH:
Wherein SδIndicate centered on (x, y),For the area in the circle domain of radius, the modulus value of D representing matrix A
Upper integer, A indicate following matrix:
Wherein i is the lower integer of the modulus value of matrix A.
Further, the low frequency sub-band signal correction module includes:
Second subband signal obtains submodule, for the T2 moment ..., the video information at Tn moment carry out small echo change
It changes, respectively obtains and the one-to-one high frequency subband signals of these video informations and low frequency sub-band signal；
First high frequency correction submodule, for these high frequency subband signals for its correspondence at the time of video information in
Each point, with C (x, y)HSubtract each other；
Second high frequency correction submodule, for these low frequency sub-band signals for its correspondence at the time of video information in
Each point, with C (x, y)LSubtract each other；
It corrects video information and obtains submodule, for passing through the high frequency subband signals subtracted each other and low frequency sub-band signal for above-mentioned
At the time of according to its correspondence, carry out wavelet inverse transformation respectively, obtain with the corrected T2 moment ..., the video at Tn moment
Information.
Further, above-mentioned sorting module includes:
Record sub module, for recording in above-mentioned correction course, corrected each high frequency subband signals；
Convolution submodule, for each high frequency subband signals according to chronological order, to be carried out convolution two-by-two；
Median computational submodule, for calculating the median of convolution value；
Minimum convoluted value determines submodule, for the determining the smallest convolution value of absolute value of the difference with the median；
Reference video information determines submodule, for determine it is corresponding with the smallest convolution value of the absolute value, according to when
Between sequentially come subsequent video information, as the T0 moment, the T1 moment, the T2 moment ..., in this n+1 moment at Tn moment
Reference video information.
Further, the video information image-latitude and longitude information packet generation module includes:
Submodule is encapsulated, for corresponding latitude and longitude information in video information to be sealed with it by the reference video information
Dress；
Sending submodule, for transmitting the information after the encapsulation.
Further, the soil remediation state determines that subelement includes:
Receiving submodule obtains reference video information and matched for receiving the information after encapsulation and unlocking
Latitude and longitude information；
Vegetation identifies submodule, for carrying out vegetation identification to reference video information in the way of machine learning；
Growth characteristics identify submodule, for carrying out growth characteristics identification to the vegetation identified；
Detection sub-module, for being compared according to the growth characteristics identified with reference to growth characteristics, when lower than threshold value
Or when being higher than threshold value, it is determined as soil restoration exception.
Further, the growth characteristics include: leaf color, plant trunk, trunk diameter.
The beneficial effect comprise that can be taken photo by plane by equipment such as unmanned planes and in the way of machine learning,
Growth characteristics and the states such as the color of vegetation are obtained, and then recovery situation of the soil after being polluted is carried out simple, quick
Judgement.Since this recovery process is very slow, it is being located at the domestic many places forest land progress in the Inner Mongol on a small scale through applicant
The monitoring frequency of test, unmanned plane can be primary for primary even two months one month, not only significantly reduces from energy consumption
Monitoring requirements, and monitoring cost is also greatly reduced from equipment purchase and maintenance cost.
Detailed description of the invention
Fig. 1 shows the composition block diagram of equipment of the invention.
Specific embodiment
As shown in Figure 1, preferred embodiment in accordance with the present invention, the present invention provides a kind of inspections of visualization soil health level
Measurement equipment, for forest farm or pasture by being monitored in the recovery process after chemical contamination to soil health level, including
Detect the remote monitoring server of soil health level indirectly in turn for visually monitoring vegetation, further includes:
Acquiring video information unit, the image letter of the vegetation for shooting soil region to be detected using unmanned plane mode
Breath, obtains video information；
Video Transmission Unit, the video information transmission for obtaining the acquiring video information unit take to remote monitoring
Business device unit；
ARM unit, the video information for being obtained according to the acquiring video information unit carry out the reparation state of soil
Detection；
Unit is identified, the remote monitoring server unit is located at, the mark unit is used for institute according to the pre-stored data
The feature that vegetation restores after by the pollution of different organic pollutants is stated, is compared with the result of the ARM unit, so that it is determined that having
The possibility type of machine pollutant.
Preferably, the acquiring video information unit includes:
Latitude and longitude information obtains subelement, for obtaining the latitude and longitude information at each moment；
Soil remediation state determines subelement, for determining soil remediation state.
Preferably, the latitude and longitude information acquisition subelement includes:
It corrects video information image-latitude and longitude information packet and obtains module, for obtaining the vegetation of soil region to be detected
Video information is corrected, and the corresponding latitude and longitude information of video information after being corrected, according to the video information after correction
Image-latitude and longitude information packet.
Preferably, the soil remediation state determines subelement according to video information image-latitude and longitude information after correction
Packet carries out the determination of soil remediation state.
Preferably, the correction video information image-latitude and longitude information packet acquisition module includes:
Assuming that the T0 moment, the T1 moment, the T2 moment ..., the Tn moment be corresponding n+1 consecutive hours in the video information
It carves, wherein n is the natural number greater than 4；
The video information at T0 and T1 moment is converted to image respectively for carrying out framing to video information by framing module
Information Img0 and Img1, and obtain the correction coefficient of low frequency sub-band signal and the correction coefficient of high frequency subband signals；
Low frequency sub-band signal correction module, for according to the correction coefficient of low frequency sub-band signal and the school of high frequency subband signals
Positive coefficient, to the T2 moment ..., the video information at Tn moment is corrected；
Sorting module, for the corrected T0 moment, the T1 moment, the T2 moment ..., the video information at Tn moment into
Row sequence；
Video information image-latitude and longitude information packet generation module, for generating video information image-latitude and longitude information packet.
Preferably, the framing module includes:
First subband signal obtains submodule and is corresponding in turn to for carrying out wavelet transformation respectively to Img0 and Img1
In the low frequency sub-band signal L at T0 moment and T1 moment0, high frequency subband signals L1And high frequency subband signals H0, high frequency subband signals
H1；
Low frequency sub-band signal correction coefficient computational submodule, for calculating the correction coefficient C (x, y) of low frequency sub-band signalL:
Wherein, the x and y respectively indicates the abscissa and ordinate of some pixel in the frame image at T0 moment, βmIt indicates
The mean value of correction matrix, ηmIndicate correction matrix variance, the correction matrix be withFor variance,For mean value
2 rank diagonal matrix B；
Submodule is filtered, for, by Gaussian filter, obtaining H ' to high frequency subband signals0And H '1:
High frequency subband signals correction coefficient computational submodule calculates for the frame image for the T0 moment and is located at the position (x, y)
The correction coefficient C (x, y) of the high frequency subband signals for the pixel setH:
Wherein SδIndicate centered on (x, y),For the area in the circle domain of radius, the modulus value of D representing matrix A
Upper integer, A indicate following matrix:
Wherein i is the lower integer of the modulus value of matrix A.
Preferably, the low frequency sub-band signal correction module includes:
Second subband signal obtains submodule, for the T2 moment ..., the video information at Tn moment carry out small echo change
It changes, respectively obtains and the one-to-one high frequency subband signals of these video informations and low frequency sub-band signal；
First high frequency correction submodule, for these high frequency subband signals for its correspondence at the time of video information in
Each point, with C (x, y)HSubtract each other；
Second high frequency correction submodule, for these low frequency sub-band signals for its correspondence at the time of video information in
Each point, with C (x, y)LSubtract each other；
It corrects video information and obtains submodule, for passing through the high frequency subband signals subtracted each other and low frequency sub-band signal for above-mentioned
At the time of according to its correspondence, carry out wavelet inverse transformation respectively, obtain with the corrected T2 moment ..., the video at Tn moment
Information.
Preferably, above-mentioned sorting module includes:
Record sub module, for recording in above-mentioned correction course, corrected each high frequency subband signals；
Convolution submodule, for each high frequency subband signals according to chronological order, to be carried out convolution two-by-two；
Median computational submodule, for calculating the median of convolution value；
Minimum convoluted value determines submodule, for the determining the smallest convolution value of absolute value of the difference with the median；
Reference video information determines submodule, for determine it is corresponding with the smallest convolution value of the absolute value, according to when
Between sequentially come subsequent video information, as the T0 moment, the T1 moment, the T2 moment ..., in this n+1 moment at Tn moment
Reference video information.
Preferably, the video information image-latitude and longitude information packet generation module includes:
Submodule is encapsulated, for corresponding latitude and longitude information in video information to be sealed with it by the reference video information
Dress；
Sending submodule, for transmitting the information after the encapsulation.
Preferably, the soil remediation state determines that subelement includes:
Receiving submodule obtains reference video information and matched for receiving the information after encapsulation and unlocking
Latitude and longitude information；
Vegetation identifies submodule, for carrying out vegetation identification to reference video information in the way of machine learning；
Growth characteristics identify submodule, for carrying out growth characteristics identification to the vegetation identified；
Detection sub-module, for being compared according to the growth characteristics identified with reference to growth characteristics, when lower than threshold value
Or when being higher than threshold value, it is determined as soil restoration exception.
Preferably, the growth characteristics include: leaf color, plant trunk, trunk diameter.
The above-described embodiments merely illustrate the principles and effects of the present invention, and is not intended to limit the present invention.It is any ripe
The personage for knowing this technology all without departing from the spirit and scope of the present invention, carries out modifications and changes to above-described embodiment.Cause
This, institute is complete without departing from the spirit and technical ideas disclosed in the present invention by those of ordinary skill in the art such as
At all equivalent modifications or change, should be covered by the claims of the present invention.
Claims (10)
1. a kind of horizontal detection device of visualization soil health, for forest farm or pasture by the recovery process after chemical contamination
In soil health level is monitored, including for visually monitoring vegetation and then detecting horizontal remote of soil health indirectly
Hold monitoring server, further includes:
Acquiring video information unit, the image information of the vegetation for being shot soil region to be detected using unmanned plane mode, is obtained
To video information；
Video Transmission Unit, video information transmission for obtaining the acquiring video information unit to remote monitoring server
Unit；
ARM unit, the video information for being obtained according to the acquiring video information unit carry out the inspection of the reparation state of soil
It surveys；
Unit is identified, the remote monitoring server unit is located at, the mark unit is used for the plant according to the pre-stored data
The feature restored after by the pollution of different organic pollutants, is compared, so that it is determined that organic dirt with the result of the ARM unit
Contaminate the possibility type of object.
2. equipment according to claim 1, which is characterized in that the acquiring video information unit includes:
Latitude and longitude information obtains subelement, for obtaining the latitude and longitude information at each moment；
Soil remediation state determines subelement, for determining soil remediation state.
3. equipment according to claim 2, which is characterized in that the latitude and longitude information obtains subelement and includes:
It corrects video information image-latitude and longitude information packet and obtains module, the video of the vegetation for obtaining soil region to be detected
Information is corrected, and the corresponding latitude and longitude information of video information after being corrected, according to the video information image-after correction
Latitude and longitude information packet.
4. equipment according to claim 3, which is characterized in that after the soil remediation state determines subelement according to correction
Video information image-latitude and longitude information packet carry out soil remediation state determination.
5. equipment according to claim 4, which is characterized in that the correction video information image-latitude and longitude information packet obtains
Obtaining module includes:
Assuming that the T0 moment, the T1 moment, the T2 moment ..., the Tn moment be corresponding n+1 continuous moment in the video information,
Wherein n is the natural number greater than 4；
The video information at T0 and T1 moment is converted to image information respectively for carrying out framing to video information by framing module
Img0 and Img1, and obtain the correction coefficient of low frequency sub-band signal and the correction coefficient of high frequency subband signals；
Low frequency sub-band signal correction module, for according to the correction coefficient of low frequency sub-band signal and the correction system of high frequency subband signals
Number, to the T2 moment ..., the video information at Tn moment is corrected；
Sorting module, for the corrected T0 moment, the T1 moment, the T2 moment ..., the video information at Tn moment arranges
Sequence；
Video information image-latitude and longitude information packet generation module, for generating video information image-latitude and longitude information packet.
6. equipment according to claim 5, which is characterized in that the framing module includes:
First subband signal obtains submodule and obtains being corresponding in turn in T0 for carrying out wavelet transformation respectively to Img0 and Img1
The low frequency sub-band signal L at moment and T1 moment0, high frequency subband signals L1And high frequency subband signals H0, high frequency subband signals H1；
Low frequency sub-band signal correction coefficient computational submodule, for calculating the correction coefficient C (x, y) of low frequency sub-band signalL:
Wherein, the x and y respectively indicates the abscissa and ordinate of some pixel in the frame image at T0 moment, βmIndicate amendment square
The mean value of battle array, ηmIndicate correction matrix variance, the correction matrix be withFor variance,For 2 ranks of mean value
Diagonal matrix B；
Submodule is filtered, for, by Gaussian filter, obtaining H ' to high frequency subband signals0And H '1:
High frequency subband signals correction coefficient computational submodule calculates for the frame image for the T0 moment and is located at the position (x, y)
The correction coefficient C (x, y) of the high frequency subband signals of pixelH:
Wherein SδIndicate centered on (x, y),For the area in the circle domain of radius, the modulus value of D representing matrix A it is upper whole
Number, A indicate following matrix:
Wherein i is the lower integer of the modulus value of matrix A.
7. equipment according to claim 6, which is characterized in that the low frequency sub-band signal correction module includes:
Second subband signal obtains submodule, for the T2 moment ..., the video information at Tn moment carry out wavelet transformation, point
It does not obtain and the one-to-one high frequency subband signals of these video informations and low frequency sub-band signal；
First high frequency correction submodule, for these high frequency subband signals for its correspondence at the time of video information in it is each
It is a, with C (x, y)HSubtract each other；
Second high frequency correction submodule, for these low frequency sub-band signals for its correspondence at the time of video information in it is each
It is a, with C (x, y)LSubtract each other；
Correct video information and obtain submodule, for by it is above-mentioned through the high frequency subband signals that subtracting each other and low frequency sub-band signal according to
At the time of it is corresponded to, carry out wavelet inverse transformation respectively, obtain with the corrected T2 moment ..., the video information at Tn moment.
8. equipment according to claim 7, which is characterized in that above-mentioned sorting module includes:
Record sub module, for recording in above-mentioned correction course, corrected each high frequency subband signals；
Convolution submodule, for each high frequency subband signals according to chronological order, to be carried out convolution two-by-two；
Median computational submodule, for calculating the median of convolution value；
Minimum convoluted value determines submodule, for the determining the smallest convolution value of absolute value of the difference with the median；
Reference video information determines submodule, corresponding with the smallest convolution value of the absolute value, suitable according to the time for determination
Sequence comes subsequent video information, as the T0 moment, the T1 moment, the T2 moment ..., the reference in this n+1 moment at Tn moment
Video information.
9. equipment according to claim 8, which is characterized in that the video information image-latitude and longitude information packet generates mould
Block includes:
Submodule is encapsulated, for corresponding latitude and longitude information in video information to be packaged with it by the reference video information；
Sending submodule, for transmitting the information after the encapsulation.
10. equipment according to claim 9, which is characterized in that the soil remediation state determines that subelement includes:
Receiving submodule obtains reference video information and matched warp for receiving the information after encapsulating and unlocking
Latitude information；
Vegetation identifies submodule, for carrying out vegetation identification to reference video information in the way of machine learning；
Growth characteristics identify submodule, for carrying out growth characteristics identification to the vegetation identified；
Detection sub-module, for being compared according to the growth characteristics that identify with reference to growth characteristics, when be lower than threshold value or
When higher than threshold value, it is determined as soil restoration exception.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810883433.4A CN108982521A (en) | 2018-08-04 | 2018-08-04 | Visualize the horizontal detection device of soil health |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810883433.4A CN108982521A (en) | 2018-08-04 | 2018-08-04 | Visualize the horizontal detection device of soil health |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108982521A true CN108982521A (en) | 2018-12-11 |
Family
ID=64555592
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810883433.4A Withdrawn CN108982521A (en) | 2018-08-04 | 2018-08-04 | Visualize the horizontal detection device of soil health |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108982521A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI799850B (en) * | 2021-05-05 | 2023-04-21 | 嶺東科技大學 | Farmland monitoring system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011004358A1 (en) * | 2009-07-08 | 2011-01-13 | Elbit Systems Ltd. | Automatic video surveillance system and method |
| CN102044209A (en) * | 2009-10-22 | 2011-05-04 | 佳能株式会社 | Image processing apparatus and method of controlling the same |
| US20130002866A1 (en) * | 2010-12-20 | 2013-01-03 | International Business Machines Corporation | Detection and Tracking of Moving Objects |
| CN104881017A (en) * | 2015-06-11 | 2015-09-02 | 张迪 | Beidou-based crop growth monitoring system |
| CN105052134A (en) * | 2012-10-01 | 2015-11-11 | Ge视频压缩有限责任公司 | Scalable video coding using inter-layer prediction of spatial intra prediction parameters |
| CN105547366A (en) * | 2015-12-30 | 2016-05-04 | 东北农业大学 | Miniaturized unmanned aerial vehicle crop information obtaining and fertilization irrigation guiding apparatus |
| US20170053169A1 (en) * | 2015-08-20 | 2017-02-23 | Motionloft, Inc. | Object detection and analysis via unmanned aerial vehicle |
| CN106956778A (en) * | 2017-05-23 | 2017-07-18 | 广东容祺智能科技有限公司 | A kind of unmanned plane pesticide spraying method and system |
-
2018
- 2018-08-04 CN CN201810883433.4A patent/CN108982521A/en not_active Withdrawn
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011004358A1 (en) * | 2009-07-08 | 2011-01-13 | Elbit Systems Ltd. | Automatic video surveillance system and method |
| CN102044209A (en) * | 2009-10-22 | 2011-05-04 | 佳能株式会社 | Image processing apparatus and method of controlling the same |
| US20130002866A1 (en) * | 2010-12-20 | 2013-01-03 | International Business Machines Corporation | Detection and Tracking of Moving Objects |
| CN105052134A (en) * | 2012-10-01 | 2015-11-11 | Ge视频压缩有限责任公司 | Scalable video coding using inter-layer prediction of spatial intra prediction parameters |
| CN104881017A (en) * | 2015-06-11 | 2015-09-02 | 张迪 | Beidou-based crop growth monitoring system |
| US20170053169A1 (en) * | 2015-08-20 | 2017-02-23 | Motionloft, Inc. | Object detection and analysis via unmanned aerial vehicle |
| CN105547366A (en) * | 2015-12-30 | 2016-05-04 | 东北农业大学 | Miniaturized unmanned aerial vehicle crop information obtaining and fertilization irrigation guiding apparatus |
| CN106956778A (en) * | 2017-05-23 | 2017-07-18 | 广东容祺智能科技有限公司 | A kind of unmanned plane pesticide spraying method and system |
Non-Patent Citations (2)
| Title |
|---|
| 刘小龙: "基于无人机遥感平台图像采集处理***的研究", 《万方学位论文数据库》 * |
| 肖武等: "无人机遥感在矿区监测与土地复垦中的应用前景", 《中国矿业》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI799850B (en) * | 2021-05-05 | 2023-04-21 | 嶺東科技大學 | Farmland monitoring system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109013677A (en) | A kind of soil organic pollutants-contaminated environment: A research review monitoring system | |
| CN106442420A (en) | Qualitative and quantitative combination water quality monitoring method | |
| Horion et al. | Mapping European ecosystem change types in response to land‐use change, extreme climate events, and land degradation | |
| Reavie et al. | Diatom-based weighted-averaging transfer functions for Great Lakes coastal water quality: relationships to watershed characteristics | |
| Estrada-Peña et al. | Methodological caveats in the environmental modelling and projections of climate niche for ticks, with examples for Ixodes ricinus (Ixodidae) | |
| Johansen et al. | Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations | |
| Wang et al. | Classifying diurnal changes of cyanobacterial blooms in Lake Taihu to identify hot patterns, seasons and hotspots based on hourly GOCI observations | |
| CN109034252A (en) | The automatic identification method of air quality website monitoring data exception | |
| Song et al. | A unified model for high resolution mapping of global lake (> 1 ha) clarity using Landsat imagery data | |
| Bagheri et al. | Chlorophyll-a estimation in New Jersey's coastal waters using Thematic Mapper data | |
| Iqbal et al. | Seasonal effect of agricultural pollutants on coastline environment: a case study of the southern estuarine water ecosystem of the boseong county Korea. | |
| CN108982521A (en) | Visualize the horizontal detection device of soil health | |
| Tang et al. | Spatiotemporal deep-learning-based algal bloom prediction for Lake Okeechobee using multisource data fusion | |
| CN109146862A (en) | Soil remediation condition intelligent detection device | |
| CN108917724A (en) | A kind of improvement detection system of contaminatedground | |
| CN102175863A (en) | Early warning system and method for Legionella | |
| CN110988286B (en) | Intelligent water resource long-term detection system | |
| CN109141365A (en) | Soil remediation method for monitoring state | |
| CN109063873A (en) | A kind of soil restoration dynamic early-warning method | |
| Dörnhöfer et al. | Water colour analysis of Lake Kummerow using time series of remote sensing and in situ data | |
| Schreuder et al. | Long-term strategy for the statistical design of a forest health monitoring system | |
| Van Wynsberge et al. | Alert thresholds for monitoring environmental variables: a new approach applied to seagrass beds diversity in New Caledonia | |
| Mishra et al. | A multi-cloud cyber infrastructure for monitoring global proliferation of cyanobacterial harmful algal blooms | |
| Sah et al. | Acoustic signal-based indigenous real-time rainfall monitoring system for sustainable environment | |
| Zappalà et al. | Integrated marine measurements in Civitavecchia, near Rome |
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 | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20181211 |
|
| WW01 | Invention patent application withdrawn after publication |