CN109766744A - A kind of identification of Bursaphelenchus xylophilus sick tree and localization method and system - Google Patents
A kind of identification of Bursaphelenchus xylophilus sick tree and localization method and system Download PDFInfo
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Abstract
The present invention provides a kind of identifications of Bursaphelenchus xylophilus sick tree and localization method and system, comprising: according to the visible images in region to be identified, obtains the doubtful pathogeny point of Bursaphelenchus xylophilus；The corresponding visible light stereo-picture of doubtful pathogeny point is obtained, and obtains the confidence level of doubtful pathogeny point according to visible light stereo-picture；According to the confidence level of doubtful pathogeny point, in conjunction with the corresponding visible light time-series image of doubtful pathogeny point, multispectral image or transpiration rate image, judge whether doubtful pathogeny point is pathogeny point；If judging to know doubtful pathogeny point as pathogeny point, the location information of sick tree where obtaining pathogeny point, and generate the location information with all pathogeny points in region to be identified and the first remote sensing images of corresponding Bursaphelenchus xylophilus disease information；First remote sensing images are sent to auditor and carry out manual examination and verification.A variety of images are utilized during confirming pathogeny point in this method, carry out multiple authentication to doubtful pathogeny point, high to the discrimination of pathogeny point, and positioning accuracy is high.
Description
Technical field
The present embodiments relate to pest control technical fields, identify more particularly, to a kind of Bursaphelenchus xylophilus sick tree
With localization method and system.
Background technique
Pine nematode, also known as pine tree wilt disease are to colonize in feeding nutrition in Pinus tree species body by Bursaphelenchus xylophilus and lead
A kind of rapid dead especially big destructive disease of trees is caused, this disease can cause pine tree withered in 60-90 days after infection, propagate climing
Prolong rapidly, 3-5 results in the pernicious disaster of large area deforestation, and difficulty of prevention and cure is very big, belongs to international important quarantine object, column
First of forest disease and pest, referred to as " smokeless forest calamity ".Pine nematode monitoring is the key means of Bursaphelenchus xylophilus prevention and control,
Traditional pine nematode monitoring mainly determines Bursaphelenchus xylophilus using the method manually now investigated to check to epidemic-stricken area
Disease locus, then to morbidity pine tree carry out harmless treatment, to reduce the propagation of Bursaphelenchus xylophilus.Since pine forest area is big,
And most of pine forest is distributed in the close mountain area of the steep woods in slope, personal monitoring takes time and effort.
Unmanned aerial vehicle remote sensing can obtain multi-time Scales, multi-angle, multispectral, high-precision remote sensing image in time, be in time decision
Person provides pine nematode early monitoring and prevention and control foundation, has in pine nematode prevention and control and huge utilizes space.With distant
Sense, GIS-Geographic Information System and unmanned air vehicle technique fast development, amalgamation remote sensing image, unmanned air vehicle technique, automatic navigation technology into
Row pest and disease monitoring, positioning and prevention and treatment will become trend.
But the pest and disease monitoring method based on unmanned aerial vehicle remote sensing in the prior art is often only obtained using single remote sensing camera
The influence in region to be monitored is taken, the more information of acquisition is single, causes not high to pathogeny point discrimination, and positioning accuracy is poor.
Summary of the invention
The embodiment of the invention provides a kind of pine lines for overcoming the above problem or at least being partially solved the above problem
The identification of parasitosis tree and localization method and system.
First aspect is identified the embodiment of the invention provides a kind of Bursaphelenchus xylophilus sick tree and localization method, comprising:
According to the visible images in region to be identified, the doubtful pathogeny point of Bursaphelenchus xylophilus is obtained；
The corresponding visible light stereo-picture of the doubtful pathogeny point is obtained, and institute is obtained according to the visible light stereo-picture
State the confidence level of doubtful pathogeny point；
According to the confidence level of the doubtful pathogeny point, in conjunction with the corresponding visible light time series chart of the doubtful pathogeny point
Picture, multispectral image or transpiration rate image, judge whether the doubtful pathogeny point is pathogeny point；
If judging to know the doubtful pathogeny point as pathogeny point, the location information of sick tree where obtaining the pathogeny point,
And the band in the region to be identified is generated according to the location information and corresponding Bursaphelenchus xylophilus disease information of all pathogeny points
First remote sensing images of the location information of ill source point and corresponding Bursaphelenchus xylophilus disease information；
First remote sensing images are sent to auditor and carry out manual examination and verification.
On the other hand the embodiment of the invention provides a kind of identification of Bursaphelenchus xylophilus sick tree and positioning systems, comprising:
Doubtful pathogeny point obtains module and obtains the doubtful of Bursaphelenchus xylophilus for the visible images according to region to be identified
Pathogeny point；
The confidence level of doubtful pathogeny point obtains module, for obtaining the corresponding visible light perspective view of the doubtful pathogeny point
Picture, and obtain according to the visible light stereo-picture confidence level of the doubtful pathogeny point；
Pathogeny point judgment module, it is corresponding in conjunction with the doubtful pathogeny point for the confidence level according to the doubtful pathogeny point
Visible light time-series image, multispectral image or transpiration rate image, judge whether the doubtful pathogeny point is pathogeny
Point；
Remote sensing images generation module, if for judging to know that the doubtful pathogeny point as pathogeny point, obtains the pathogeny
The location information of sick tree where point, and institute is generated according to the location information of all pathogeny points and corresponding Bursaphelenchus xylophilus disease information
State the location information with all pathogeny points in region to be identified and the first remote sensing images of corresponding Bursaphelenchus xylophilus disease information；
Manual examination and verification module carries out manual examination and verification for first remote sensing images to be sent to auditor.
The embodiment of the invention provides include processor, communication interface, memory and bus for the third aspect, wherein processing
Device, communication interface, memory complete mutual communication by bus, and processor can call the logical order in memory,
To execute the identification of Bursaphelenchus xylophilus sick tree and the localization method of first aspect offer.
The embodiment of the invention provides a kind of non-transient computer readable storage medium, the non-transient calculating for fourth aspect
Machine readable storage medium storing program for executing stores computer instruction, and the computer instruction makes the computer execute the pine that first aspect provides
The identification of nematode sick tree and localization method.
A kind of Bursaphelenchus xylophilus sick tree identification provided in an embodiment of the present invention and localization method and system, first according to be identified
The visible images in region obtain doubtful pathogeny point；The doubtful disease is obtained further according to the visible light stereo-picture at doubtful pathogeny point
The confidence level of source point；In conjunction with visible light time-series image, multispectral image and the transpiration rate figure of the doubtful pathogeny point
As that can judge whether the doubtful pathogeny point is real pathogeny point, and finally identify and position out material nematode sick tree；Root again
Remote sensing images are generated according to the location information and defect information of each pathogeny point, and pedestrian's work is clicked through to the pathogeny on the second remote sensing images
A variety of images are utilized during confirming pathogeny point in audit, this method, multiple authentication are carried out to doubtful pathogeny point, to pathogeny
The discrimination of point is high, and positioning accuracy is high.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the flow chart of a kind of Bursaphelenchus xylophilus sick tree identification and localization method provided in an embodiment of the present invention；
Fig. 2 is the schematic illustration that the visible light stereo-picture of doubtful pathogeny point is obtained in the embodiment of the present invention；
Fig. 3 is to confirm that I grades of doubtful pathogeny points are the schematic illustration of pathogeny point in the embodiment of the present invention；
Fig. 4 is the structural block diagram of a kind of Bursaphelenchus xylophilus sick tree identification and positioning system provided in an embodiment of the present invention；
Fig. 5 is the structural schematic diagram of a kind of electronic equipment provided in an embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical solution in the embodiment of the present invention is explicitly described, it is clear that described embodiment is the present invention
A part of the embodiment, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not having
Every other embodiment obtained under the premise of creative work is made, shall fall within the protection scope of the present invention.
Fig. 1 is the flow chart of a kind of Bursaphelenchus xylophilus sick tree identification and localization method provided in an embodiment of the present invention, such as Fig. 1 institute
Show, comprising:
S101 obtains the doubtful pathogeny point of Bursaphelenchus xylophilus according to the visible images in region to be identified；
S102 obtains the corresponding visible light stereo-picture of the doubtful pathogeny point, and according to the visible light stereo-picture
Obtain the confidence level of the doubtful pathogeny point；
S103, according to the confidence level of the doubtful pathogeny point, in conjunction with the corresponding visible light time sequence of the doubtful pathogeny point
Column image, multispectral image or transpiration rate image judge whether the doubtful pathogeny point is pathogeny point；
S104, if judging to know the doubtful pathogeny point as pathogeny point, the position of sick tree where obtaining the pathogeny point
Information, and generate according to the location information of all pathogeny points and corresponding Bursaphelenchus xylophilus disease information the band in the region to be identified
The first remote sensing images of the location information of ill source point and corresponding Bursaphelenchus xylophilus disease information；
First remote sensing images are sent to auditor and carry out manual examination and verification by S105.
Wherein, in step s101, it is seen that light image is one kind of unmanned plane image, is getting region to be identified
Visible images after, can be found out visible according to the visible images in region to be identified using preset image processing algorithm
Doubtful pathogeny point in light image.
In step s 102, the possibility that pathogeny point gets the wrong sow by the ear, institute are carried out using the visible images in region to be identified
Having certain probability with doubtful pathogeny point is real pathogeny point, and also having certain probability not is real pathogeny point.In order to further
Determine the authenticity of doubtful pathogeny point, here using the stereo-picture of visible light come to further looking at doubtful pathogeny point, thus
Different confidence levels is assigned for different doubtful pathogeny points.
In step s 103, the visible light time-series image of doubtful pathogeny point is within a certain period of time at preset timed intervals
Several visible images of the doubtful pathogeny point of interval acquiring, according to the chronological situation of change of these visible images,
It can reflect the situation of catching an illness of doubtful pathogeny point.Doubtful pathogeny point difference catch an illness period spectral signature it is different, and transpiration rate
There is the principle of certain correlation with pine tree infection Bursaphelenchus xylophilus disease severity.Based on visible light time-series image, more
Whether the above-mentioned relation of spectrum picture and transpiration rate image and situation of catching an illness can determine doubtful pathogeny point in conjunction with confidence level
For real pathogeny point.
In step S104, after pathogeny point has been determined, it can assign each image according to the positioning system of unmanned plane
The location information that location information obtains pathogeny point is obtained according to location information and the corresponding Bursaphelenchus xylophilus disease information of each pathogeny point
Take the first remote sensing images of location information and corresponding Bursaphelenchus xylophilus disease information with all pathogeny points.
In step s105, the point of the pathogeny as determined by step S101-S103 is also possible to not be genuine pathogeny point, therefore
In order to further increase the accuracy of identification, the saving later period is artificial, needs to carry out manual examination and verification to the first remote sensing images, to guarantee
The pathogeny point of subsequent processing is strictly real pathogeny point.
Specifically, according to the visible images in region to be identified, the doubtful pathogeny point in region to be identified is obtained, is recycled
Visible light stereo-picture at doubtful pathogeny point assigns the confidence level that this doubtful pathogeny point one is real pathogeny point.In order to
Further determine that whether the doubtful pathogeny point is real pathogeny point, using the confidence level of the doubtful pathogeny point, in conjunction with pine tree
The related visible light time-series image of situation of catching an illness, multispectral image and transpiration rate image, can determine that this is doubtful
Whether pathogeny point is real pathogeny point.If judging to know that the doubtful pathogeny point as pathogeny point, obtains pathogeny point institute
In the location information of sick tree, and according to the location information of all pathogeny points and corresponding Bursaphelenchus xylophilus disease information generate it is described to
The location information with all pathogeny points of identification region and the first remote sensing images of corresponding Bursaphelenchus xylophilus disease information；In order to
Accuracy of identification is further increased, needs to carry out manual examination and verification to the first remote sensing images, i.e., further by first remote sensing images
It is sent to auditor and carries out manual examination and verification.Wherein location information can according to the positioning device of each image collecting device directly or
Connect determination.
A kind of Bursaphelenchus xylophilus sick tree identification provided in an embodiment of the present invention and localization method, first according to region to be identified
Visible images obtain doubtful pathogeny point；The doubtful pathogeny point is obtained further according to the visible light stereo-picture at doubtful pathogeny point
Confidence level；In conjunction with visible light time-series image, multispectral image and the transpiration rate image of the doubtful pathogeny point
Judge whether the doubtful pathogeny point is real pathogeny point, and finally identifies and positions out material nematode sick tree；Further according to each disease
The location information and defect information of source point generate remote sensing images, and carry out manual examination and verification to the pathogeny point on the second remote sensing images,
A variety of images are utilized during confirming pathogeny point in this method, multiple authentication are carried out to doubtful pathogeny point, to pathogeny point
Discrimination is high, and positioning accuracy is high.
In the above-described embodiments, in the visible images according to region to be identified, the doubtful pathogeny point of Bursaphelenchus xylophilus is obtained
Before, further includes:
The area to be identified is acquired respectively using Visible Light Camera, multispectral camera and the thermal imaging camera that unmanned plane carries
Visible images, multispectral image and the transpiration rate image in domain；
According to the Visible Light Camera multiple few station acquisitions the visible images comprising the doubtful pathogeny point,
Obtain the visible light stereo-picture；According to the Visible Light Camera multiple and different moment acquire comprising the doubtful pathogeny
The visible images of point, obtain the visible light time-series image.
Wherein, Visible Light Camera, multispectral camera and thermal imaging camera are carried using unmanned plane, obtained respectively to be identified
Visible images, multispectral image and the transpiration rate image in region.
Specifically, using monocular Visible Light Camera stereoscopic vision generation technique, the visible light for generating doubtful pathogeny point is three-dimensional
Image.Principle is treated identification region progress visible images and is adopted as shown in Fig. 2, the cruise of UAV flight's visible light monocular camera
Collection.If B point is doubtful pine nematode source point, the visible light figure shot during unmanned plane cruise in M point and M ' point is transferred
Picture extracts the corresponding pixel characteristic point of image, forms the stereopsis of B point target after image processing system, is used for image
Identifying system differentiates the dead wood and other similar object distinguished and fallen down to the ground in the withered tree of three-dimensional Bursaphelenchus xylophilus and forest zone, increases system to reach
The effect of system recognition accuracy, and then assign different doubtful pathogeny points different confidence levels.
The cruise of UAV flight's visible light monocular camera treats identification region and carries out visible images acquisition, doubts when determining
After pathogeny point, obtain the visible light sequential image at several doubtful pathogeny points in chronological order, as doubtful pathogeny point can
Light-exposed time-series image.
In the above-described embodiments, the confidence level according to the doubtful pathogeny point, it is corresponding in conjunction with the doubtful pathogeny point
Visible light time-series image, multispectral image or transpiration rate image judge whether the doubtful pathogeny point is pathogeny point,
It specifically includes:
If judgement knows the confidence level less than the first preset threshold, when the visible light time sequence of the doubtful pathogeny point
When column image meets the first preset condition, determine that the doubtful pathogeny point is pathogeny point；If judgement knows that the confidence level is not small
In first preset threshold and less than the second preset threshold, then when the multispectral image and transpiration rate of the doubtful pathogeny point
When image meets the second preset condition, determine that the doubtful pathogeny point is pathogeny point；If judgement knows that the confidence level is greater than institute
State the second preset threshold, it is determined that the doubtful pathogeny point is pathogeny point.
Wherein, the first preset threshold, the second preset threshold can be set according to actual needs, for example, the first preset threshold
It is set as 75%, the second preset threshold is set as 85%.
Specifically, doubtful pathogeny point is classified firstly, being equivalent to after setting preset threshold, confidence level is less than first
The doubtful pathogeny point of preset threshold is I grades, and confidence level is not less than the first preset threshold and the doubtful disease less than the second preset threshold
Source point is II grades, and confidence level is greater than the doubtful pathogeny point III level of the second preset threshold.Next, to the doubtful disease of different stage
Source point is determined using different preset conditions, to determine whether the doubtful pathogeny point of different stage is real pathogeny point.I
The confidence level of the doubtful pathogeny point of grade is minimum, then needs its visible light time-series image to meet corresponding condition, just can determine that it
For real pathogeny point；The confidence level of II grades of doubtful pathogeny points is placed in the middle, then needs its multispectral image and transpiration rate image full
The corresponding condition of foot, can just determine that it is real pathogeny point；The confidence level of the doubtful pathogeny point of III level is higher, can directly recognize
It is real pathogeny point for it.It is to be appreciated that the first preset condition and the second default condition can be set according to actual needs.
In the above-described embodiments, described when the visible light time-series image of the doubtful pathogeny point meets the first default item
When part, determines that the doubtful pathogeny point is pathogeny point, specifically includes:
When the area of affected area is in chronological order successively in each visible images in the visible light time-series image
When increase, determine that the doubtful pathogeny point is pathogeny point.
Specifically, as shown in Figure 3, it is assumed that A point is I grades of doubtful pathogeny points, is judging that A point is to be true positive pathogeny point
It is to obtain the visible light time-series image at A point, successively includes sequence image 1, sequence image 2, sequence image 3 and sequence
Image 4.The image pixel feature on every width sequence image at A point is extracted, and four width sequence images are overlapped and are compared, when
When the area of affected area is sequentially increased in chronological order in four width sequence images in visible light time-series image, determine doubtful
Pathogeny point A point is pathogeny point.
In the above-described embodiments, described when the multispectral image and transpiration rate image of the doubtful pathogeny point meet second
When preset condition, determines that the doubtful pathogeny point is pathogeny point, specifically includes:
When the corresponding spectrum characteristic parameter of the multispectral image is in the first preset range or the transpiration rate figure
When being in the second preset range as corresponding transpiration rate, determine that the doubtful pathogeny point is pathogeny point.
Specifically, when doubtful pathogeny point is II grades, only when the corresponding spectrum characteristic parameter of multispectral image is in first
In preset range, and when the corresponding transpiration rate of transpiration rate image is in the second preset range, doubtful pathogeny just can determine that
Point is real pathogeny point.
In the above-described embodiments, before determining the doubtful pathogeny point for pathogeny point, further includes:
The corresponding spectrum characteristic parameter is obtained according to the multispectral image, according to transpiration rate image acquisition pair
The transpiration rate answered.
In the above-described embodiments, it after first remote sensing images to be sent to auditor and carry out manual examination and verification, also wraps
It includes:
It rejects the pathogeny point on first remote sensing images not by manual examination and verification to obtain the second remote sensing images, it will be described
Second remote sensing images are split to obtain multiple area under one's jurisdictions by different area under one's jurisdictions；
The location information of each local pathogeny point is sent to corresponding area under one's jurisdiction administrator, then passes through the area under one's jurisdiction administrator
The location information is sent to the hand-held navigation terminal of Management offorestry person.
Specifically, the above method provided in an embodiment of the present invention is further described below by an example, is needed
It is noted that following is only an example, protection scope of the present invention is not limited.
The Visible Light Camera resolution ratio of UAV flight is 4000 × 3000, the resolution ratio of thermal imaging camera is 640 ×
512, multispectral camera is twin-lens multi-spectral imager, spectral region: 400-2500nm.
Unmanned plane carries remote sensing image equipment and periodically cruises target forest zone, including Visible Light Camera, multispectral phase
Machine and thermal imaging camera, it is seen that light camera acquires remote sensing image in real time and doubted by Remote Sensing Image Processing System to pine nematode
Identification judgement is carried out like point.Judge that B location region decision for the doubtful point of pine nematode, calls the different cruise flight positions of unmanned plane
The remote sensing images of the captured doubtful point of B location region pine nematode carry out vector quantization, radiant correction, throwing to remote sensing images
Shadow transformation and the processing such as feature point extraction, carried out again using visible light monocular vision technique compound stereoscopic image identification and according to
Doubtful pixel confidence section is classified, if it is determined that doubtful pixel confidence d < 75%, then be divided into I grades；It is judged as doubtful
Pixel confidence 75%≤d < 85% is then divided into II grades；Be judged as doubtful pixel confidence d >=85%, then be divided into III level.
Image recognition processing system takes different identification methods further to be known the doubtful point of different stage confidence level respectively
Do not judge.If the confidence level of the doubtful point of B location region pine nematode is divided into III level by image grading identifying system,
The doubtful point of Bursaphelenchus xylophilus is determined as pine nematode source point by system.If image grading identifying system is by B location region pine
The confidence level of the doubtful point of nematodiasis is divided into II grades, and system then transfers multispectral camera and thermal imaging camera is doubtful to Bursaphelenchus xylophilus
Point carry out spectral signature acquisition and transpiration rate detection, by in database infect pine nematode different times spectral signature
It compares, and then judges whether it is pine nematode source point；It is serious based on transpiration rate and pine tree infection Bursaphelenchus xylophilus disease
There is degree the principle of certain correlation can further be identified by the detection to the doubtful transpiration rate in B location region
Judge whether it is pine nematode source point.If image grading identifying system is set the B location region doubtful point of pine nematode
Reliability is divided into I grades, then utilizes the point of the morbidity to the Bursaphelenchus xylophilus epidemic-stricken area, once cruised every three days, stores same
The remote sensing images of pathogeny point different time shooting, system generate time series using the B location regional remote sensing image repeatedly to cruise
Image observe by the onset state to pine nematode identifying whether point of falling ill for Bursaphelenchus xylophilus.
After doubtful automatic identification of Bursaphelenchus xylophilus terminates, image processing system generate with geographical coordinate position information and
The complete image of Bursaphelenchus xylophilus disease information.The complete image of generation is distributed to by Bursaphelenchus xylophilus monitoring prevention and treatment data management platform
The responsible person in each area under one's jurisdiction in Bursaphelenchus xylophilus epidemic-stricken area audits, to Bursaphelenchus xylophilus morbidity point location information and insect pest information into
Row confirmation, the information of wrong identification is rejected, and Bursaphelenchus xylophilus monitoring prevention and treatment data management platform is committed to after audit is errorless, is realized
Artificial audit again.Bursaphelenchus xylophilus monitoring prevention and treatment data management platform is divided by the area under one's jurisdiction that each Management offorestry person is responsible for
It cuts, it is whole that the segmented image for corresponding to each Management offorestry person in forest zone is distributed to the Bursaphelenchus xylophilus location navigation held to it
End, navigation terminal guide Management offorestry person to the Bursaphelenchus xylophilus morbidity point given directions, infect the serious of pine nematode according to pine tree
Degree takes different harmless treatment modes.
Fig. 4 is the structural block diagram of a kind of Bursaphelenchus xylophilus sick tree identification and positioning system provided in an embodiment of the present invention, such as Fig. 4
It is shown, comprising: doubtful pathogeny point obtains module 401, the confidence level acquisition module 402 of the doubtful pathogeny point of disease, pathogeny point and judges mould
Block 403, remote sensing images generation module 404 and manual examination and verification module 405.Wherein:
Doubtful pathogeny point obtains module 401 for the visible images according to region to be identified, obtains doubting for Bursaphelenchus xylophilus
Like pathogeny point.It is three-dimensional for obtaining the corresponding visible light of the doubtful pathogeny point that the confidence level of doubtful pathogeny point obtains module 402
Image, and obtain according to the visible light stereo-picture confidence level of the doubtful pathogeny point.Pathogeny point judgment module 403 is used for
According to the confidence level of the doubtful pathogeny point, in conjunction with the corresponding visible light time-series image of the doubtful pathogeny point, multispectral
Image or transpiration rate image judge whether the doubtful pathogeny point is pathogeny point.If remote sensing images generation module 404 is used for
Judge to know the doubtful pathogeny point as pathogeny point, then the location information of sick tree where obtaining the pathogeny point, and according to all
The location information of pathogeny point and corresponding Bursaphelenchus xylophilus disease information generate the region to be identified with all pathogeny points
First remote sensing images of location information and corresponding Bursaphelenchus xylophilus disease information.Manual examination and verification module 405 is used for described first
Remote sensing images are sent to auditor and carry out manual examination and verification.
Specifically, the system also includes image collection modules, are specifically used for:
The area to be identified is acquired respectively using Visible Light Camera, multispectral camera and the thermal imaging camera that unmanned plane carries
Visible images, multispectral image and the transpiration rate image in domain；
According to the Visible Light Camera multiple few station acquisitions the visible images comprising the doubtful pathogeny point,
Obtain the visible light stereo-picture；According to the Visible Light Camera multiple and different moment acquire comprising the doubtful pathogeny
The visible images of point, obtain the visible light time-series image.
Further, the confidence level of the doubtful pathogeny point obtains module 402 and is specifically used for:
If judgement knows the confidence level less than the first preset threshold, when the visible light time sequence of the doubtful pathogeny point
When column image meets the first preset condition, determine that the doubtful pathogeny point is pathogeny point；If judgement knows that the confidence level is not small
In first preset threshold and less than the second preset threshold, then when the multispectral image and transpiration rate of the doubtful pathogeny point
When image meets the second preset condition, determine that the doubtful pathogeny point is pathogeny point；If judgement knows that the confidence level is greater than institute
State the second preset threshold, it is determined that the doubtful pathogeny point is pathogeny point.
Further, the confidence level of the doubtful pathogeny point obtains module 402 and is further used for:
When the area of affected area is in chronological order successively in each visible images in the visible light time-series image
When increase, determine that the doubtful pathogeny point is pathogeny point.
Further, the confidence level of the doubtful pathogeny point obtains module 402 and is further used for:
When the corresponding spectrum characteristic parameter of the multispectral image is in the first preset range, and the transpiration rate image
When corresponding transpiration rate is in the second preset range, determine that the doubtful pathogeny point is pathogeny point.
Further, the system also includes sending module is specifically used for:
It rejects the pathogeny point on first remote sensing images not by manual examination and verification to obtain the second remote sensing images, it will be described
Second remote sensing images are split to obtain multiple area under one's jurisdictions by different area under one's jurisdictions；
The location information of each local pathogeny point is sent to corresponding area under one's jurisdiction administrator, then passes through the area under one's jurisdiction administrator
The location information is sent to the hand-held navigation terminal of Management offorestry person.
A kind of Bursaphelenchus xylophilus sick tree identification provided in an embodiment of the present invention and positioning system, first according to region to be identified
Visible images obtain doubtful pathogeny point；The doubtful pathogeny point is obtained further according to the visible light stereo-picture at doubtful pathogeny point
Confidence level；In conjunction with visible light time-series image, multispectral image and the transpiration rate image of the doubtful pathogeny point
Judge whether the doubtful pathogeny point is real pathogeny point, and finally identifies and positions out material nematode sick tree；Further according to each disease
The location information and defect information of source point generate remote sensing images, and carry out manual examination and verification to the pathogeny point on the second remote sensing images,
A variety of images are utilized during confirming pathogeny point in this method, multiple authentication are carried out to doubtful pathogeny point, to pathogeny point
Discrimination is high, and positioning accuracy is high.
Fig. 5 is the structural schematic diagram of a kind of electronic equipment provided in an embodiment of the present invention, as shown in figure 5, electronic equipment packet
It includes: processor (processor) 501, communication interface (Communications Interface) 502, memory (memory)
503 and bus 504, wherein processor 501, communication interface 502, memory 503 complete mutual communication by bus 504.
Processor 501 can call the logical order in memory 503, to execute following method, for example, according to region to be identified
Visible images, obtain the doubtful pathogeny point of Bursaphelenchus xylophilus；The corresponding visible light stereo-picture of the doubtful pathogeny point is obtained,
And according to the visible light stereo-picture, the confidence level of the doubtful pathogeny point is obtained；According to the confidence of the doubtful pathogeny point
Degree, in conjunction with the corresponding visible light time-series image of the doubtful pathogeny point, multispectral image or transpiration rate image, judgement
Whether the doubtful pathogeny point is pathogeny point；If judging to know that the doubtful pathogeny point as pathogeny point, obtains the pathogeny point
The location information of place sick tree, and according to the location information of all pathogeny points and the generation of corresponding Bursaphelenchus xylophilus disease information
The location information with all pathogeny points in region to be identified and the first remote sensing images of corresponding Bursaphelenchus xylophilus disease information；It will
First remote sensing images are sent to auditor and carry out manual examination and verification.
Logical order in above-mentioned memory 502 can be realized and as independent by way of SFU software functional unit
Product when selling or using, can store in a computer readable storage medium.Based on this understanding, of the invention
Substantially the part of the part that contributes to existing technology or the technical solution can be produced technical solution in other words with software
The form of product embodies, which is stored in a storage medium, including some instructions are used so that one
Platform computer equipment (can be personal computer, server or the network equipment etc.) executes described in each embodiment of the present invention
The all or part of the steps of method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-
Only Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can be with
Store the medium of program code.
The embodiment of the present invention provides a kind of non-transient computer readable storage medium, the non-transient computer readable storage
Medium storing computer instruction, the computer instruction make the computer execute side provided by above-mentioned each method embodiment
Method, for example, according to the visible images in region to be identified, obtain the doubtful pathogeny point of Bursaphelenchus xylophilus；It obtains described doubtful
The corresponding visible light stereo-picture of pathogeny point, and according to the visible light stereo-picture, obtain the confidence of the doubtful pathogeny point
Degree；According to the confidence level of the doubtful pathogeny point, in conjunction with the doubtful pathogeny point corresponding visible light time-series image, mostly light
Spectrogram picture or transpiration rate image judge whether the doubtful pathogeny point is pathogeny point；If the doubtful pathogeny is known in judgement
Point is pathogeny point, then the location information of sick tree where obtaining the pathogeny point, and according to the location information of all pathogeny points and right
The Bursaphelenchus xylophilus disease information answered generates the location information and corresponding pine with all pathogeny points in the region to be identified
First remote sensing images of nematodiasis information；First remote sensing images are sent to auditor and carry out manual examination and verification.
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above method embodiment can pass through
The relevant hardware of program instruction is completed, and program above-mentioned can be stored in a computer readable storage medium, the program
When being executed, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned includes: ROM, RAM, magnetic disk or light
The various media that can store program code such as disk.
The embodiments such as communication equipment described above are only schematical, wherein unit as illustrated by the separation member
It may or may not be physically separated, component shown as a unit may or may not be physics list
Member, it can it is in one place, or may be distributed over multiple network units.It can be selected according to the actual needs
In some or all of the modules achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness
Labour in the case where, it can understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
The method of certain parts of example or embodiment.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of Bursaphelenchus xylophilus sick tree identification and localization method characterized by comprising
According to the visible images in region to be identified, the doubtful pathogeny point of Bursaphelenchus xylophilus is obtained；
The corresponding visible light stereo-picture of the doubtful pathogeny point is obtained, and described doubt is obtained according to the visible light stereo-picture
Like the confidence level of pathogeny point；
According to the confidence level of the doubtful pathogeny point, in conjunction with the corresponding visible light time-series image of the doubtful pathogeny point, more
Spectrum picture or transpiration rate image judge whether the doubtful pathogeny point is pathogeny point；
If judging to know the doubtful pathogeny point as pathogeny point, the location information of sick tree where obtaining the pathogeny point, and root
According to the location information and corresponding Bursaphelenchus xylophilus disease information of all pathogeny points generate the region to be identified with institute it is ill
First remote sensing images of the location information of source point and corresponding Bursaphelenchus xylophilus disease information；
First remote sensing images are sent to auditor and carry out manual examination and verification.
2. method according to claim 1, which is characterized in that in the visible images according to region to be identified, obtain pine
Before the doubtful pathogeny point of nematode, further includes:
The region to be identified is acquired respectively using Visible Light Camera, multispectral camera and the thermal imaging camera that unmanned plane carries
Visible images, multispectral image and transpiration rate image；
According to the Visible Light Camera in the visible images comprising the doubtful pathogeny point of multiple few station acquisitions, obtain
The visible light stereo-picture；According to the Visible Light Camera multiple and different moment acquire comprising the doubtful pathogeny point
Visible images obtain the visible light time-series image.
3. method according to claim 1, which is characterized in that the confidence level according to the doubtful pathogeny point, in conjunction with institute
The corresponding visible light time-series image of doubtful pathogeny point, multispectral image or transpiration rate image are stated, is judged described doubtful
Whether pathogeny point is pathogeny point, is specifically included:
If judgement knows the confidence level less than the first preset threshold, when the visible light time series chart of the doubtful pathogeny point
When as meeting the first preset condition, determine that the doubtful pathogeny point is pathogeny point；If judgement knows the confidence level not less than institute
State the first preset threshold and less than the second preset threshold, then when the multispectral image and transpiration rate image of the doubtful pathogeny point
When meeting the second preset condition, determine that the doubtful pathogeny point is pathogeny point；If judgement knows that the confidence level is greater than described the
Two preset thresholds, it is determined that the doubtful pathogeny point is pathogeny point.
4. method according to claim 3, which is characterized in that the visible light time series chart when the doubtful pathogeny point
When as meeting the first preset condition, determining that the doubtful pathogeny point is pathogeny point, specifically including:
When the area of affected area is sequentially increased in chronological order in each visible images in the visible light time-series image
When, determine that the doubtful pathogeny point is pathogeny point.
5. method according to claim 3, which is characterized in that the multispectral image and transpiration when the doubtful pathogeny point
When rate image meets the second preset condition, determines that the doubtful pathogeny point is pathogeny point, specifically includes:
When the corresponding spectrum characteristic parameter of the multispectral image is in the first preset range or the transpiration rate image pair
When the transpiration rate answered is in the second preset range, determine that the doubtful pathogeny point is pathogeny point.
6. method according to claim 5, which is characterized in that before determining the doubtful pathogeny point for pathogeny point, also wrap
It includes:
The corresponding spectrum characteristic parameter is obtained according to the multispectral image, is obtained according to the transpiration rate image corresponding
Transpiration rate.
7. method according to claim 1, which is characterized in that carry out people first remote sensing images are sent to auditor
After work audit, further includes:
It rejects the pathogeny point on first remote sensing images not by manual examination and verification to obtain the second remote sensing images, by described second
Remote sensing images are split to obtain multiple area under one's jurisdictions by different area under one's jurisdictions；
The location information of each local pathogeny point is sent to corresponding area under one's jurisdiction administrator, then passes through the area under one's jurisdiction administrator for institute
State the hand-held navigation terminal that location information is sent to Management offorestry person.
8. a kind of Bursaphelenchus xylophilus sick tree identification and positioning system characterized by comprising
Doubtful pathogeny point obtains module and obtains the doubtful pathogeny of Bursaphelenchus xylophilus for the visible images according to region to be identified
Point；
The confidence level of doubtful pathogeny point obtains module, for obtaining the corresponding visible light stereo-picture of the doubtful pathogeny point, and
The confidence level of the doubtful pathogeny point is obtained according to the visible light stereo-picture；
Pathogeny point judgment module, for the confidence level according to the doubtful pathogeny point, in conjunction with the doubtful pathogeny point is corresponding can
Light-exposed time-series image, multispectral image or transpiration rate image judge whether the doubtful pathogeny point is pathogeny point；
Remote sensing images generation module, if for judging to know that the doubtful pathogeny point as pathogeny point, obtains pathogeny point institute
In the location information of sick tree, and according to the location information of all pathogeny points and corresponding Bursaphelenchus xylophilus disease information generate it is described to
The location information with all pathogeny points of identification region and the first remote sensing images of corresponding Bursaphelenchus xylophilus disease information；
Manual examination and verification module carries out manual examination and verification for first remote sensing images to be sent to auditor.
9. a kind of electronic equipment, which is characterized in that including processor, communication interface, memory and bus, wherein processor leads to
Believe that interface, memory complete mutual communication by bus, processor can call the logical order in memory, to execute
Bursaphelenchus xylophilus sick tree identification as described in any one of claim 1 to 7 and localization method.
10. a kind of non-transient computer readable storage medium, which is characterized in that the non-transient computer readable storage medium is deposited
Computer instruction is stored up, the computer instruction makes the computer execute Bursaphelenchus xylophilus as described in any one of claim 1 to 7
Sick tree identification and localization method.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110956096A (en) * | 2019-11-13 | 2020-04-03 | 北京农业智能装备技术研究中心 | Forestry infection epidemic wood clearing management method and system |
| CN111414976A (en) * | 2020-04-16 | 2020-07-14 | 北京航空航天大学青岛研究院 | Simple grading method for disposal difficulty of mountain pine wood nematode disease trees |
| CN111523516A (en) * | 2020-05-14 | 2020-08-11 | 宁波工程学院 | Forest harmful wood identification method |
| CN111582176A (en) * | 2020-05-09 | 2020-08-25 | 湖北同诚通用航空有限公司 | Visible light remote sensing image withered and dead wood recognition software system and recognition method |
| CN112183489A (en) * | 2020-11-04 | 2021-01-05 | 长光禹辰信息技术与装备(青岛)有限公司 | Pine color-changing standing tree identification and positioning method, device, equipment and storage medium |
| CN112598265A (en) * | 2020-12-18 | 2021-04-02 | 武汉大学 | Decoupling risk estimation-based rapid detection method for hyperspectral pine nematode disease of unmanned aerial vehicle |
| CN112749619A (en) * | 2020-11-20 | 2021-05-04 | 北京林业大学 | Method for detecting space-time aggregation of pine wilt disease |
| CN114486763A (en) * | 2022-01-26 | 2022-05-13 | 河北润田节水设备有限公司 | Crop disease and insect pest detection system and application method thereof |
| CN114782844A (en) * | 2022-05-06 | 2022-07-22 | 华南农业大学 | Pine wood nematode disease tree identification method, system and storage medium |
| KR20230130895A (en) * | 2022-03-04 | 2023-09-12 | 충남대학교산학협력단 | Method and apparatus for evaluating activity vitality of conifer based on infection symptom of wilt disease |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6168947B1 (en) * | 1999-02-11 | 2001-01-02 | Food Industry Research And Development Institute | Nematophagous fungus Esteya vermicola |
| CN101770577A (en) * | 2010-01-18 | 2010-07-07 | 浙江林学院 | Method for extracting information on expansion and elimination of dead wood of pine wilt disease in air photos of unmanned aerial vehicle |
| CN101776584A (en) * | 2010-01-18 | 2010-07-14 | 浙江林学院 | High-spectrum comprehensive analysis early detection method for Bursaphelenchus xylophilus |
| CN107895369A (en) * | 2017-11-28 | 2018-04-10 | 腾讯科技（深圳）有限公司 | Image classification method, device, storage medium and equipment |
| CN108424182A (en) * | 2018-03-08 | 2018-08-21 | 山东省烟台市农业科学研究院 | A kind of application of biogas slurry concentrate in prevention tomato root-knot eelworm disease |
-
2018
- 2018-11-21 CN CN201811393247.9A patent/CN109766744B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6168947B1 (en) * | 1999-02-11 | 2001-01-02 | Food Industry Research And Development Institute | Nematophagous fungus Esteya vermicola |
| CN101770577A (en) * | 2010-01-18 | 2010-07-07 | 浙江林学院 | Method for extracting information on expansion and elimination of dead wood of pine wilt disease in air photos of unmanned aerial vehicle |
| CN101776584A (en) * | 2010-01-18 | 2010-07-14 | 浙江林学院 | High-spectrum comprehensive analysis early detection method for Bursaphelenchus xylophilus |
| CN107895369A (en) * | 2017-11-28 | 2018-04-10 | 腾讯科技（深圳）有限公司 | Image classification method, device, storage medium and equipment |
| CN108424182A (en) * | 2018-03-08 | 2018-08-21 | 山东省烟台市农业科学研究院 | A kind of application of biogas slurry concentrate in prevention tomato root-knot eelworm disease |
Non-Patent Citations (3)
| Title |
|---|
| LEI WANG ET AL: "A Study on the Changes of Pinus Massoniana Spatial", 《 2006 IEEE INTERNATIONAL SYMPOSIUM ON GEOSCIENCE AND REMOTE SENSING》 * |
| R SKAKUN ET AL: "Aerial, GIS, and Field Data Calibration of a Landsat ETM+ Mountain Pine Beetle", 《 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM》 * |
| 刘宁 等: "基于蒸腾速率与光谱特征的松材线虫病害预测", 《西北农林科技大学学报（自然科学版）》 * |
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