CN110487793A - Pest and disease damage time DYNAMIC DISTRIBUTION monitoring method and system - Google Patents
Pest and disease damage time DYNAMIC DISTRIBUTION monitoring method and system Download PDFInfo
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Abstract
This application discloses a kind of pest and disease damage time DYNAMIC DISTRIBUTION monitoring method and systems, it is related to plant pest monitoring technical field, it include: acquisition crops each breeding time corresponding canopy spectra data, for the crops of contemporaneity, crops spectrum is analyzed, obtains crops by the reflectivity of the sensitive band of pest and disease damage；EVI is constructed using the reflectivity of sensitive band；According to the time series data of canopy spectra data acquisition EVI；The time series data of EVI is filtered and is reconstructed, the timing curve by pest and disease damage crops and healthy crops is obtained；According to the state of the timing curve by pest and disease damage crops and healthy crops, pest and disease damage situation is judged.The application utilizes time series data collection, extracts crop vegetation index time-serial position, and analysis occurs pest and disease damage and the variation characteristic of the vegetation index timing curve of pest and disease damage does not occur, realizes the monitoring of pest and disease damage time DYNAMIC DISTRIBUTION.
Description
Technical field
This application involves plant pest monitoring technical fields, specifically, being related to a kind of pest and disease damage time DYNAMIC DISTRIBUTION
Monitoring method and system.
Background technique
Diseases and pests of agronomic crop is the abbreviation of the disease of damage to crops, insect pest.Diseases and pests of agronomic crop inhibits crop growth, most
Direct harm is the underproduction for causing large area crop.China has a vast territory, weather and pattern of farming are complicated, and agricultural production is easy
Being fallen ill by a variety of disease pests influences, and some researches show that the Major Diseases of China damage to crops have 724 kinds, 833 kinds of insect pest.According to statistics,
Grain loss caused by pest and disease damage about 10% is an important factor for endangering agricultural production, economic development.
Currently, China is fallen ill hair-like condition, distribution etc. mainly by ground surface sample station data using station for acquiring disease pest
Information monitors agricultural pest.Although the accuracy for monitoring pest and disease damage using sampling point data is preferable, monitoring range is past
Toward being single or multiple sampling points, it is difficult state and degree a wide range of, that timely reflect pest and disease damage, while the monitoring of ground station
Higher cost.Therefore, it needs to propose a kind of method and system for carrying out the monitoring of pest and disease damage time DYNAMIC DISTRIBUTION using remote sensing technology.
Summary of the invention
In view of this, utilizing time sequence this application provides a kind of pest and disease damage time DYNAMIC DISTRIBUTION monitoring method and system
Column data collection, extracts crop vegetation index time-serial position, and analysis occurs pest and disease damage and the vegetation index of pest and disease damage does not occur
The variation characteristic of timing curve realizes the monitoring of pest and disease damage time DYNAMIC DISTRIBUTION.
In order to solve the above-mentioned technical problem, the application has following technical solution:
On the one hand, this application provides a kind of pest and disease damage time DYNAMIC DISTRIBUTION monitoring methods characterized by comprising
Crops each breeding time corresponding canopy spectra data are acquired, for the crops of contemporaneity, are obtained slight
Pest and disease damage spectrum, moderate pest and disease damage spectrum, severe pest and disease damage spectrum and healthy crops spectrum；
Respectively to the slight pest and disease damage spectrum of the crops, moderate pest and disease damage spectrum, severe pest and disease damage spectrum and health
Crops spectrum is analyzed, and obtains the crops by the reflectivity of the sensitive band of pest and disease damage；
EVI is constructed using the reflectivity of the sensitive band；
According to the time series data of EVI described in the canopy spectra data acquisition；
The time series data of the EVI is filtered and is reconstructed, obtain by pest and disease damage crops and healthy crops when
Overture line；
According to the state of the timing curve by pest and disease damage crops and healthy crops, pest and disease damage situation is judged:
When timing curve is the first state, double-peak feature is presented in curve, is illustrated in bimodal corresponding primary peak
Section pest and disease damage outburst, the period pest and disease damage between bimodal are inhibited；
When timing curve is second of state, there is abnormal unimodal feature in curve, illustrates that pest and disease damage is quick-fried in the wave crest period
It sends out and pest and disease damage is not inhibited；
When timing curve is the third state, curve does not occur peak value, illustrates that pest and disease damage degree is slight and not to disease
Insect pest is inhibited.
Optionally, in which:
The crops are red spectral band, blue wave band and near infrared band, the utilization by the sensitive band of pest and disease damage
The reflectivity of the sensitive band constructs EVI, specifically:Wherein,
RNIRIndicate the reflectivity of near infrared band, RREDIndicate the reflectivity of red spectral band, RBLUEIndicate the reflectivity of blue wave band.
Optionally, in which:
The time series data to the EVI is filtered and reconstructs, specifically: using S-G filter method to the EVI's
Time series data is filtered and reconstructs.
Optionally, in which:
The time series data to the EVI is filtered and reconstructs, specifically: using double curve matching sides Logistic
Method is filtered and reconstructs to the time series data of the EVI.
Optionally, in which:
The time series data to the EVI is filtered and reconstructs, specifically: use asymmetric Gaussian function fitting side
Method is filtered and reconstructs to the time series data of the EVI.
On the other hand, this application provides a kind of pest and disease damage time DYNAMIC DISTRIBUTIONs to monitor system characterized by comprising
Acquisition module, analysis module, EVI building module, EVI time series data obtain module, filtering reconstructed module and judgment module；
The acquisition module, for acquiring crops each breeding time corresponding canopy spectra data, for contemporaneity
Crops, obtain slight pest and disease damage spectrum, moderate pest and disease damage spectrum, severe pest and disease damage spectrum and healthy crops spectrum；
The analysis module, for respectively to the slight pest and disease damage spectrum of the crops, moderate pest and disease damage spectrum, severe
Pest and disease damage spectrum and healthy crops spectrum are analyzed, and obtain the crops by the reflectivity of the sensitive band of pest and disease damage；
The EVI constructs module, for constructing EVI using the reflectivity of the sensitive band；
The EVI time series data obtains module, the when ordinal number for the EVI according to the canopy spectra data acquisition
According to；
The filtering reconstructed module is filtered and reconstructs for the time series data to the EVI, obtains by pest and disease damage agriculture
The timing curve of crop and healthy crops；
Judgment module judges disease pest for the state according to the timing curve by pest and disease damage crops and healthy crops
Evil situation: when timing curve is the first state, double-peak feature is presented in curve, is illustrated in the bimodal corresponding primary peak period
Pest and disease damage outburst, the period pest and disease damage between bimodal are inhibited；When timing curve is second of state, curve occurs different
Chang Danfeng feature illustrates to break out in wave crest period pest and disease damage and not inhibit to pest and disease damage；When timing curve is the third shape
When state, curve does not occur peak value, illustrates that pest and disease damage degree is slight and does not inhibit to pest and disease damage.
Optionally, in which:
The filtering reconstructed module includes S-G filter, and the S-G filter is used for using S-G filter method to the EVI
Time series data be filtered and reconstruct.
Optionally, in which:
The filtering reconstructed module includes double Logistic curve filters, and double Logistic curve filters are used for
The time series data of the EVI is filtered and is reconstructed using double Logistic curve-fitting methods.
Optionally, in which:
The filtering reconstructed module includes asymmetric Gaussian filter, and the asymmetric Gaussian filter is used for using non-right
Gaussian function fitting method is claimed to be filtered and reconstruct the time series data of the EVI.
Compared with prior art, pest and disease damage time DYNAMIC DISTRIBUTION monitoring method and system provided herein, reach
Following effect:
Pest and disease damage time DYNAMIC DISTRIBUTION monitoring method provided herein and system, are obtained by the spectroscopic data of crops
The EVI time series data of crops is taken, and reconstruct is filtered to EVI time series data, the crops vegetation after denoising can be obtained
The timing curve of index, by analyze timing curve, can quick obtaining crop disease and insect occur time behavioral characteristics, realize
The time DYNAMIC DISTRIBUTION of pest and disease damage monitors, time-consuming, laborious and timeliness when so as to avoid manually carrying out field sampling and investigation
Property difference problem, also can solve and be difficult to time DYNAMIC DISTRIBUTION that is a wide range of, timely reacting pest and disease damage when monitoring using website
The problem of, be conducive to the accuracy and timeliness that improve pest and disease monitoring while cost is reduced.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present application, constitutes part of this application, this Shen
Illustrative embodiments and their description please are not constituted an undue limitation on the present application for explaining the application.In the accompanying drawings:
Fig. 1 show a kind of flow chart of pest and disease damage time DYNAMIC DISTRIBUTION monitoring method provided by the embodiment of the present application；
Fig. 2 show the crops curve of spectrum under different degrees of pest and disease damage provided by the embodiment of the present application；
Fig. 3 is shown provided by the embodiment of the present application by the timing curve of pest and disease damage crops and healthy crops；
Fig. 4 show a kind of structure chart of the monitoring system of pest and disease damage time DYNAMIC DISTRIBUTION provided by the embodiment of the present application.
Specific embodiment
As used some vocabulary to censure specific components in the specification and claims.Those skilled in the art answer
It is understood that hardware manufacturer may call the same component with different nouns.This specification and claims are not with name
The difference of title is as the mode for distinguishing component, but with the difference of component functionally as the criterion of differentiation.Such as logical
The "comprising" of piece specification and claim mentioned in is an open language, therefore should be construed to " include but do not limit
In "." substantially " refer within the acceptable error range, those skilled in the art can within a certain error range solve described in
Technical problem basically reaches the technical effect.In addition, " coupling " word includes any direct and indirect electric property coupling herein
Means.Therefore, if it is described herein that a first device is coupled to a second device, then representing the first device can directly electrical coupling
It is connected to the second device, or the second device indirectly electrically coupled through other devices or coupling means.Specification
Subsequent descriptions be implement the application better embodiment, so it is described description be for the purpose of the rule for illustrating the application,
It is not intended to limit the scope of the present application.The protection scope of the application is as defined by the appended claims.
Currently, China is fallen ill hair-like condition, distribution etc. mainly by ground surface sample station data using station for acquiring disease pest
Information monitors agricultural pest.Although the accuracy for monitoring pest and disease damage using sampling point data is preferable, monitoring range is past
Toward being single or multiple sampling points, it is difficult state and degree a wide range of, that timely reflect pest and disease damage, while the monitoring of ground station
Higher cost.Therefore, it needs to propose a kind of method and system for carrying out the monitoring of pest and disease damage time DYNAMIC DISTRIBUTION using remote sensing technology.
In view of this, utilizing time sequence this application provides a kind of pest and disease damage time DYNAMIC DISTRIBUTION monitoring method and system
Column remotely-sensed data collection, extracts crop vegetation index time-serial position, and analysis occurs pest and disease damage and the vegetation of pest and disease damage does not occur
The variation characteristic of index timing curve realizes the monitoring of pest and disease damage time DYNAMIC DISTRIBUTION.
It is described in detail below in conjunction with the drawings and specific embodiments.
Fig. 1 show a kind of flow chart of pest and disease damage time DYNAMIC DISTRIBUTION monitoring method provided by the embodiment of the present application,
Fig. 2 show the crops curve of spectrum under different degrees of pest and disease damage provided by the embodiment of the present application, and Fig. 3 show the application
By the timing curve of pest and disease damage crops and healthy crops provided by embodiment ,-Fig. 3 referring to Figure 1, the embodiment of the present application
Provided pest and disease damage time DYNAMIC DISTRIBUTION monitoring method, comprising:
Step 10: acquisition crops each breeding time, corresponding canopy spectra data were obtained for the crops of contemporaneity
To slight pest and disease damage spectrum, moderate pest and disease damage spectrum, severe pest and disease damage spectrum and healthy crops spectrum；
Step 20: respectively to the slight pest and disease damage spectrum of crops, moderate pest and disease damage spectrum, severe pest and disease damage spectrum and strong
Health crops spectrum is analyzed, and obtains crops by the reflectivity of the sensitive band of pest and disease damage；
Step 30: utilizing reflectivity building EVI (Enhanced Vegetation Index, the enhancing vegetation of sensitive band
Index)；
Step 40: according to the time series data of canopy spectra data acquisition EVI；
Step 50: the time series data of EVI being filtered and reconstructed, is obtained by pest and disease damage crops and healthy crops
Timing curve；
Step 60: according to the state of the timing curve by pest and disease damage crops and healthy crops, judge pest and disease damage situation:
When timing curve is the first state, such as the curve 104 in Fig. 3 is referred to, which is presented double-peak feature,
Illustrate to break out in bimodal corresponding primary peak period pest and disease damage, the period pest and disease damage between bimodal is inhibited；
When timing curve is second of state, such as the curve 105 in Fig. 3, which there is abnormal unimodal feature, explanation
It breaks out in wave crest period pest and disease damage and pest and disease damage is not inhibited；
When timing curve is the third state, such as the curve 106 in Fig. 3, which does not occur peak value, illustrates pest and disease damage
Degree is slight and does not inhibit to pest and disease damage.
Specifically ,-Fig. 2 referring to Figure 1, pest and disease damage time DYNAMIC DISTRIBUTION monitoring method provided by the embodiment of the present application,
The each breeding time canopy spectra data of crops are acquired by step 10, due in visible-range, with pest and disease damage degree
Reinforce, the content of crops Determination of Chlorophyll can gradually decrease, crops to the absorption reduction of light and reflectivity increases, in the application
According to the reflectivity in the crops curve of spectrum of contemporaneity, by pest and disease damage degree be divided into slight pest and disease damage, moderate pest and disease damage,
Severe pest and disease damage, if curve 101 indicates slight pest and disease damage in the reflectivity of each wave band in Fig. 2, curve 102 indicates moderate disease pest
Evil each wave band reflectivity, curve 103 indicate severe pest and disease damage each wave band reflectivity, obtain the curve of spectrum after,
In step 20, for the crops of contemporaneity, the crops curve of spectrum under different pest and disease damage degree, such as Fig. 2 are analyzed
In, as diseases and pests of agronomic crop degree is reinforced, crops enhance in the reflectivity of blue wave band and red wave band, especially in red wave
Section, reflectivity enhancing, and in the case where crop growth is luxuriant or health status, in the spectrum of the near infrared band of 700nm-1300nm
There is higher reflectivity, but as pest and disease damage degree is reinforced, leaf tissue is destroyed, light can not carry out anti-in blade interior
It penetrates, reflectivity is caused to decline, it is known that crops are blue wave band, red wave band and near infrared band, root by the sensitive band of pest and disease damage
According to the available different pest and disease damage degree of Fig. 2 crops each sensitive band reflectivity.
Continuing with being that reflection vegetation index by pest and disease damage stress is changed more sensitive vegetation index referring to Fig. 1 and Fig. 3, EVI,
Crops are obtained after the reflectivity of sensitive band, utilize the reflectivity building enhancing vegetation index of sensitive band in step 30
EVI, then in step 40 according to the EVI time series data of canopy spectra data acquisition crops, by satellite sensor from figure
The influence of the factors such as part, cloud covering, shade, EVI time series data inevitably generates noise, therefore, right by step 50
EVI time series data is filtered and reconstructs, and obtains the filtered timing curve by pest and disease damage crops and healthy crops, such as
Fig. 3, and pest and disease damage situation is judged according to the state of the timing curve by pest and disease damage crops in a step 60, it can be with from Fig. 3
Find out that unimodal feature is presented in the timing curve 107 of healthy crops, at the 200th day or so, the EVI of healthy crops reached maximum
Value；And when crops are influenced by pest and disease damage, three kinds of states are presented in the timing curve of EVI, when timing curve is the first shape
When state, such as the curve 104 in Fig. 3 is referred to, which is presented double-peak feature, when -200 days 170 days, EVI decline,
Illustrate to break out in this period pest and disease damage, by control of artificially going and buy Chinese medicine, pest and disease damage is inhibited, so that EVI rises again, thus
There is the second secondary wave crest；When timing curve is second of state, such as the curve 105 in Fig. 3, which there is abnormal unimodal spy
Sign, when curve reaches wave crest, pest and disease damage is broken out, and due to not inhibiting to pest and disease damage, EVI will not rise again；At that time
Overture line be the third state when, such as the curve 106 in Fig. 3, which does not occur peak value, illustrate pest and disease damage degree be it is slight,
But due to not inhibiting to pest and disease damage, also exert a certain influence to the growth of crops, so that timing curve does not occur
Peak value.
Pest and disease damage time DYNAMIC DISTRIBUTION monitoring method provided by the embodiment of the present application, by having quick, coverage area
Extensively, the remote sensing technology of the features such as at low cost, obtains the crops spectroscopic data of high time resolution, and passes through high time resolution
Crops spectroscopic data quick obtaining crop occur pest and disease damage when temporal characteristics, can not only make up website monitoring not
Foot can also improve the precision of monitoring pest and disease damage to a certain extent.In addition, being obtained in the application by the spectroscopic data of crops
After taking the EVI time series data of crops, reconstruct is filtered to EVI time series data, the crops vegetation index after being denoised
Timing curve, and by analysis timing curve, can quick obtaining crop disease and insect occur spatial distribution and crop hair
Temporal characteristics when sick insect pest realize the time DYNAMIC DISTRIBUTION monitoring of pest and disease damage, so as to avoid manually carrying out field taking
When sample and investigation the problem of time-consuming, laborious and poor in timeliness, be conducive to improve pest and disease monitoring while reducing human cost
Accuracy and timeliness.
Optionally, Fig. 2 is referred to, crops are red spectral band, blue wave band and near-infrared by the sensitive band of pest and disease damage
Wave band in above-mentioned steps 30, constructs EVI using the reflectivity of sensitive band, specifically:Wherein, RNIRIndicate the reflectivity of near infrared band, RREDIndicate red
The reflectivity of optical band, RBLUEIndicate the reflectivity of blue wave band.Specifically, by analyzing the spectroscopic data in Fig. 2,
Known to crops by the sensitive band of pest and disease damage be red spectral band, blue wave band and near infrared band, in step 30 using quick
When feeling the reflectivity building EVI of wave band, formula can be passed throughCarry out structure
It builds, wherein RNIRIndicate the reflectivity of near infrared band, RREDIndicate the reflectivity of red spectral band, RBLUEIndicate blue wave band
Reflectivity.By constructing EVI, the spectral signal of each sensitive band can be comprehensively utilized, enhances vegetation information and reduces non-plant
By information, convenient for carrying out pest and disease damage analysis using vegetation index.
Optionally, in above-mentioned steps 50, the time series data of EVI is filtered and is reconstructed, specifically: it is filtered using S-G
Method is filtered and reconstructs to the time series data of EVI.Specifically, in above-mentioned steps 50, the time series data of EVI is filtered and
When reconstruct, using Savitzky-Golay filter method (abbreviation S-G filter method), according to formulaTo EVI
Time series data be filtered reconstruct, whereinTo reconstruct time series data, Tj+1For original temporal data, CiFor i-th of vegetation
Filter factor when exponent filtering, N is the time series data quantity in sliding window, i.e. convolution number, equal to the width 2m+ of array
1, coefficient j are the coefficients of original vegetation index data, and m is the coefficient of glide filter window.S-G filtering is in time domain based on office
Domain multinomial least square method fitting filtering method, effect depend primarily on filter window size and local it is polynomial
Number, when window is larger, obtained result is more smooth, and when window is smaller, then the result obtained is more nearly true number
According to, therefore raw noise may also be retained；When selecting lower number, available more smooth data, and select compared with
High number, when being fitted again, can generate new noise although noise can be removed.Using S-G filter method, guaranteeing
While filtering out noise, the waveform and essential characteristic of initial data can be retained to greatest extent, that is to say, that the shape of signal
It is constant with width, and this is particularly important by variation of the drought stress in time scale to monitoring vegetation, therefore, is filtered using S-G
The timing curve that the filtering reconstruct of wave method obtains, can preferably realize and dynamically be monitored to crops by the time of pest and disease damage.When
So, S-G filter method, there is also certain limitations, such as cover the summit of more serious region or vegetation growth in cloud
Occur abnormal；It is also possible to the vegetation index value that crops caused by harvesting should reduce may increase.
Optionally, in above-mentioned steps 50, the time series data of EVI is filtered and is reconstructed, specifically: using double
Logistic curve-fitting method is filtered and reconstructs to the time series data of EVI.Specifically, in above-mentioned steps 50, to EVI's
When time series data is filtered and reconstructs, using double Logistic curve-fitting methods.Double Logistic function-fitting methods are bases
In locally to global fit procedure, the corresponding value of time point in entire time series data is pressed into maximum value or minimum value point first
At multiple sections, pass through function f (t)=f (t respectively；c1,c2,a1,a2…a4)=c1+c2g(t；c1,c2,a1,a2…a4) to each
A section carries out local fit, wherein g (t；c1,c2,a1,a2…a4) it is double Logistic functions, a1And a2Determining function curve
Left and right part corner position, a3And a4Control the rate of change at inflection point, c1And c2The benchmark of control function curve and
Then amplitude passes through functionOverall fit is carried out, wherein fL(t), fC
(t) and fR(t) it is illustrated respectively in [tL,tR] left side minimum value, intermediate maximum and the corresponding part of the right minimum value are quasi- in section
Close function, [tL,tR] it is constant interval to fitting part in time series data, α (t) and β (t) they are between 0 to 1
Cut function.
Optionally, in above-mentioned steps 50, the time series data of EVI is filtered and is reconstructed, specifically: use asymmetric height
This Function Fitting method is filtered and reconstructs to the time series data of EVI.Specifically, in above-mentioned steps 50, to the when ordinal number of EVI
When according to being filtered and reconstruct, using asymmetric Gaussian function fitting method, first by choosing local fit section, height is used
This shape fitting function f (t)=f (t；c1,c2,a1,a2…a5)=c1+c2g(t；c1,c2,a1,a2…a5) to this interval censored data into
Row is fitted, wherein g (t；c1,c2,a1,a2…a5) it is Gaussian function, a1The maximum value of determining function curve and the position of minimum value,
a2,a3Control the steepness (amount related with the tall and straight degree of curve peak) of function curve, a4,a5The left and right portion of curve is determined respectively
The width divided, a2, a3, a4And a5State modulator fitting result adapts to time-serial position under asymmetric condition as far as possible and is fitted, c1
And c2The benchmark and amplitude of control function curve；Then pass through global fitting functionLocal fit result is merged, wherein fL(t), fC(t) and
fR(t) it is illustrated respectively in [tL,tR] left side minimum value, intermediate maximum and the corresponding local fit letter of the right minimum value in section
Number, [tL,tR] it is constant interval to fitting part in time series data, α (t) and β (t) they are the cuttings between 0 to 1
Function.
During asymmetric Gaussian function fitting, choose local fit section when, be by sliding average window come
Realize data smoothing extraction time window, the data for participating in data smoothing are to select the left and right of window center point adjacent to point data,
It is handled by weighting and carries out data smoothing.The approximating method is suitable for the phenology information extraction in Long time scale, takes segmentation
The method of fitting, the interference that local data can be fitted to avoid global data, since the maximum value of each curve is all mutual
It is independent, therefore, in the complicated time-serial position of fitting, there is good applicability, the curve after fitting is closer to very
Truth condition.
Based on the same inventive concept, the embodiment of the present application also provides a kind of pest and disease damage time DYNAMIC DISTRIBUTION monitoring system, Fig. 4
It is shown a kind of structure chart of the monitoring system of pest and disease damage time DYNAMIC DISTRIBUTION provided by the embodiment of the present application, refers to Fig. 4, this
Apply embodiment provided by pest and disease damage time DYNAMIC DISTRIBUTION monitor system 100, comprising: acquisition module 110, analysis module 120,
EVI constructs module 130, EVI time series data obtains module 140, filters reconstructed module 150 and judgment module 160；
Acquisition module 110, for acquiring crops each breeding time corresponding canopy spectra data, for contemporaneity
Crops obtain slight pest and disease damage spectrum, moderate pest and disease damage spectrum, severe pest and disease damage spectrum and healthy crops spectrum；
Analysis module 120, for respectively to the slight pest and disease damage spectrum of crops, moderate pest and disease damage spectrum, severe disease pest
Evil spectrum and healthy crops spectrum are analyzed, and obtain crops by the reflectivity of the sensitive band of pest and disease damage；
EVI constructs module 130, for constructing EVI using the reflectivity of sensitive band；
EVI time series data obtains module 140, for the time series data according to canopy spectra data acquisition EVI；
Reconstructed module 150 is filtered, is filtered and reconstructs for the time series data to EVI, obtain by pest and disease damage crops
With the timing curve of healthy crops；
Judgment module 160 judges pest and disease damage situation for the state according to the timing curve by pest and disease damage crops: when
When timing curve is the first state, such as the curve 104 in Fig. 3 is referred to, which is presented double-peak feature, illustrates bimodal
Corresponding primary peak period pest and disease damage outburst, the period pest and disease damage between bimodal are inhibited；When timing curve is second
When kind of state, such as the curve 105 in Fig. 3, which there is abnormal unimodal feature, illustrate to break out in wave crest period pest and disease damage and not
Pest and disease damage is inhibited；When timing curve is the third state, such as the curve 106 in Fig. 3, which does not occur peak value,
Illustrate that pest and disease damage degree is slight and does not inhibit to pest and disease damage.
Specifically, Fig. 4 is referred to, pest and disease damage time DYNAMIC DISTRIBUTION provided by the embodiment of the present application monitors system, including
Acquisition module 110, analysis module 120, EVI building module 130, EVI time series data obtain module 140, filtering reconstructed module 150
With judgment module 160, each breeding time canopy spectra data of crops are acquired by acquisition module 110 first, due to visible
In optical range, as pest and disease damage degree is reinforced, the content of crops Determination of Chlorophyll can be gradually decreased, and crops subtract the absorption of light
Less and reflectivity increases, according to the reflectivity in the crops curve of spectrum of contemporaneity in the application, by pest and disease damage degree point
For slight pest and disease damage, moderate pest and disease damage, severe pest and disease damage, after obtaining the curve of spectrum, by analysis module 120, for same a period of time
The crops of phase analyze the crops curve of spectrum under different pest and disease damage degree, in Fig. 2, with diseases and pests of agronomic crop journey
Degree is reinforced, and crops enhance in the reflectivity of blue wave band and red wave band, and especially in red wave band, reflectivity enhances, and in farming
Under object riotous growth or health status, in the higher reflectivity of spectrum appearance of the near infrared band of 700nm-1300nm, but with
Pest and disease damage degree reinforce, leaf tissue destroyed, and light can not be reflected in blade interior, and reflectivity is caused to decline, can
Know that crops by the sensitive band of pest and disease damage are blue wave band, red wave band and near infrared band, and obtains different pest and disease damages according to fig. 2
The crops of degree are in the reflectivity of each sensitive band, and curve 101 indicates slight pest and disease damage in the reflection of each wave band in Fig. 2
Rate, curve 102 indicate moderate pest and disease damage in the reflectivity of each wave band, and curve 103 indicates severe pest and disease damage in each wave band
Reflectivity.
Continuing with being that reflection vegetation index by pest and disease damage stress is changed more sensitive vegetation index referring to Fig. 3-Fig. 4, EVI,
Crops are obtained after the reflectivity of sensitive band, module 130 is constructed by EVI and constructs enhancing using the reflectivity of sensitive band
Vegetation index EVI, and pass through ordinal number when EVI time series data obtains EVI of the module 140 according to canopy spectra data acquisition crops
According to due to being influenced by factors such as satellite sensor self-condition, cloud covering, shades, EVI time series data is inevitably produced
Therefore raw noise is filtered and reconstructs to EVI time series data by filtering reconstructed module 150, obtain filtered by disease pest
The timing curve of evil crops and healthy crops, such as Fig. 3, and using judgment module 160 according to by pest and disease damage crops when
The state of overture line judges pest and disease damage situation, and as can be seen from Figure 3 unimodal spy is presented in the timing curve 107 of healthy crops
Sign, at the 200th day or so, the EVI of healthy crops reached maximum value；And when crops are influenced by pest and disease damage, EVI when
Three kinds of states are presented in overture line, when timing curve is the first state, such as refer to the curve 104 in Fig. 3, which is in
Existing double-peak feature, when -200 days 170 days, EVI decline illustrates to break out in this period pest and disease damage, by artificially going and buy Chinese medicine
Control, pest and disease damage is inhibited, so that EVI rises again, to the second secondary wave crest occur；When timing curve is second of state
When, such as the curve 105 in Fig. 3, which there is abnormal unimodal feature, and when curve reaches wave crest, pest and disease damage is broken out, due to not
Pest and disease damage is inhibited, therefore EVI will not rise again；When timing curve is the third state, such as the curve in Fig. 3
106, which does not occur peak value, illustrates that pest and disease damage degree is slight, but due to not inhibiting to pest and disease damage, also to agriculture
The growth of crop exerts a certain influence, so that timing curve does not occur peak value.
Pest and disease damage time DYNAMIC DISTRIBUTION provided by the embodiment of the present application monitors system, is obtained by the spectroscopic data of crops
The EVI time series data of crops is taken, and reconstruct is filtered to EVI time series data, the crops vegetation after denoising can be obtained
The timing curve of index, by analyze timing curve, can quick obtaining crop disease and insect occur spatial distribution and crop
Temporal characteristics when pest and disease damage occur, the time DYNAMIC DISTRIBUTION monitoring of pest and disease damage are realized, so as to avoid manually carrying out field
When sampling and investigation the problem of time-consuming, laborious and poor in timeliness, be conducive to improve pest and disease damage prison while reducing human cost
The accuracy and timeliness of survey.
Optionally, filtering reconstructed module 150 includes S-G filter, and S-G filter is used for using S-G filter method to EVI's
Time series data is filtered and reconstructs.Specifically, when the time series data of EVI being filtered and reconstructed, using S-G filter,
According to formulaReconstruct is filtered to the time series data of EVI, whereinTo reconstruct time series data,
Tj+1For original temporal data, CiFilter factor when filtering for i-th of vegetation index, N are the time series data number in sliding window
Amount, i.e. convolution number, equal to the coefficient that the width 2m+1 of array, coefficient j are original vegetation index data, m is glide filter window
The coefficient of mouth.S-G filtering is the filtering method based on the fitting of local multinomial least square method in time domain, and effect mainly takes
Certainly in the size of filter window and the polynomial number of local, when window is larger, obtained result is more smooth, when window compared with
Hour, then the result obtained is more nearly truthful data, therefore may also retain raw noise；When selecting lower number,
Available more smooth data, and higher number is selected, although noise can be removed, when being fitted again, can produce
Raw new noise.The wave of initial data can be retained while guaranteeing to filter out noise using S-G filter method to greatest extent
Shape and essential characteristic, that is to say, that the shape and width of signal are constant, and this to monitoring vegetation by drought stress in time scale
On variation it is particularly important, therefore, using the obtained timing curve of S-G filter method filtering reconstruct, can preferably realize to agriculture
Crop is dynamically monitored by the time of pest and disease damage.Certainly, S-G filter method, there is also certain limitations, for example, cloud covering compared with
Peak for critical regions or vegetation growth will appear exception；It is also possible to what crops caused by harvesting should reduce
Vegetation index value may increase.
Optionally, filtering reconstructed module 150 includes double Logistic curve filters, and double Logistic curve filters are used
The time series data of EVI is filtered and is reconstructed in using double Logistic curve-fitting methods.Specifically, to the timing of EVI
When data are filtered and reconstruct, using double Logistic curve filters, carried out by double Logistic curve-fitting methods
Filtering reconstruct, double Logistic function-fitting methods are based on part to global fit procedure, first by entire time series data
Middle time point corresponding value is divided into multiple sections by maximum value or minimum value, passes through function f (t)=f (t respectively；c1,c2,a1,
a2…a4)=c1+c2g(t；c1,c2,a1,a2…a4) local fit is carried out to each section, wherein g (t；c1,c2,a1,a2…a4)
It is double Logistic functions, a1And a2The corner position of the left and right part of determining function curve, a3And a4It controls at inflection point
Rate of change, c1And c2The benchmark and amplitude of control function curve, then pass through functionOverall fit is carried out, wherein fL(t), fC(t) and fR(t) difference table
Show in [tL,tR] left side minimum value, intermediate maximum and the corresponding local fit function of the right minimum value in section, [tL,tR] be
To the constant interval of fitting part in time series data, α (t) and β (t) are the cutting functions between 0 to 1.
Optionally, filtering reconstructed module 150 includes asymmetric Gaussian filter, and asymmetric Gaussian filter is used for using non-
Symmetrical Gaussian Function Fitting method is filtered and reconstructs to the time series data of EVI.Specifically, the time series data of EVI is carried out
When filtering and reconstruct, using asymmetric Gaussian filter, reconstruct is filtered by asymmetric Gaussian function fitting method, first
By choosing local fit section, Gauss shape fitting function f (t)=f (t is used；c1,c2,a1,a2…a5)=c1+c2g(t；c1,
c2,a1,a2…a5) this interval censored data is fitted, wherein g (t；c1,c2,a1,a2…a5) it is Gaussian function, a1Determine letter
The maximum value of number curve and the position of minimum value, a2,a3The steepness for controlling function curve is (related with the tall and straight degree of curve peak
Amount), a4,a5The width of the left and right part of curve, a are determined respectively2, a3, a4And a5State modulator fitting result adapts to non-as far as possible
Time-serial position is fitted under symmetric condition, c1And c2The benchmark and amplitude of control function curve；Then pass through global fitting functionLocal fit result is merged, wherein fL(t), fC(t) and
fR(t) it is illustrated respectively in [tL,tR] left side minimum value, intermediate maximum and the corresponding local fit letter of the right minimum value in section
Number, [tL,tR] it is constant interval to fitting part in time series data, α (t) and β (t) they are the cuttings between 0 to 1
Function.
During asymmetric Gaussian function fitting, choose local fit section when, be by sliding average window come
Realize data smoothing extraction time window, the data for participating in data smoothing are to select the left and right of window center point adjacent to point data,
It is handled by weighting and carries out data smoothing.The approximating method is suitable for the phenology information extraction in Long time scale, takes segmentation
The method of fitting, the interference that local data can be fitted to avoid global data, since the maximum value of each curve is all mutual
It is independent, therefore, in the complicated time-serial position of fitting, there is good applicability, the curve after fitting is closer to very
Truth condition.
As can be seen from the above embodiments beneficial effect existing for the application is:
Pest and disease damage time DYNAMIC DISTRIBUTION monitoring method provided herein and system, by having quick, coverage area
Extensively, the remote sensing technology of the features such as at low cost, obtains the crops spectroscopic data of high time resolution, and passes through high time resolution
Crops spectroscopic data quick obtaining crop occur pest and disease damage when temporal characteristics, can not only make up website monitoring not
Foot can also improve the precision of monitoring pest and disease damage to a certain extent.In addition, being obtained in the application by the spectroscopic data of crops
After taking the EVI time series data of crops, reconstruct is filtered to EVI time series data, the crops vegetation index after being denoised
Timing curve, and by analysis timing curve, can quick obtaining crop disease and insect occur spatial distribution and crop hair
Temporal characteristics when sick insect pest realize the time DYNAMIC DISTRIBUTION monitoring of pest and disease damage, so as to avoid manually carrying out field taking
When sample and investigation the problem of time-consuming, laborious and poor in timeliness, be conducive to improve pest and disease monitoring while reducing human cost
Accuracy and timeliness.
It should be understood by those skilled in the art that, embodiments herein can provide as method, apparatus or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
Above description shows and describes several preferred embodiments of the present application, but as previously described, it should be understood that the application
Be not limited to forms disclosed herein, should not be regarded as an exclusion of other examples, and can be used for various other combinations,
Modification and environment, and the above teachings or related fields of technology or knowledge can be passed through within that scope of the inventive concept describe herein
It is modified.And changes and modifications made by those skilled in the art do not depart from spirit and scope, then it all should be in this Shen
It please be in the protection scope of appended claims.
Claims (9)
1. a kind of pest and disease damage time DYNAMIC DISTRIBUTION monitoring method characterized by comprising
Acquisition crops each breeding time corresponding canopy spectra data obtain slight disease pest for the crops of contemporaneity
Evil spectrum, moderate pest and disease damage spectrum, severe pest and disease damage spectrum and healthy crops spectrum；
Respectively to the slight pest and disease damage spectrum of the crops, moderate pest and disease damage spectrum, severe pest and disease damage spectrum and healthy farming
Object light spectrum is analyzed, and obtains the crops by the reflectivity of the sensitive band of pest and disease damage；
EVI is constructed using the reflectivity of the sensitive band；
According to the time series data of EVI described in the canopy spectra data acquisition；
The time series data of the EVI is filtered and is reconstructed, the when overture by pest and disease damage crops and healthy crops is obtained
Line；
According to the state of the timing curve by pest and disease damage crops and healthy crops, pest and disease damage situation is judged:
When timing curve is the first state, double-peak feature is presented in curve, is illustrated in bimodal corresponding primary peak period disease
Insect pest outburst, the period pest and disease damage between bimodal are inhibited；
When timing curve is second of state, there is abnormal unimodal feature in curve, illustrate in the outburst of wave crest period pest and disease damage and
Pest and disease damage is not inhibited；
When timing curve is the third state, curve does not occur peak value, illustrates that pest and disease damage degree is slight and not to pest and disease damage
Inhibited.
2. pest and disease damage time DYNAMIC DISTRIBUTION monitoring method according to claim 1, which is characterized in that the crops are by disease
The sensitive band of insect pest is red spectral band, blue wave band and near infrared band, the reflectivity structure using the sensitive band
EVI is built, specifically:Wherein, RNIRIndicate the reflection of near infrared band
Rate, RREDIndicate the reflectivity of red spectral band, RBLUEIndicate the reflectivity of blue wave band.
3. pest and disease damage time DYNAMIC DISTRIBUTION monitoring method according to claim 1, which is characterized in that described to the EVI
Time series data be filtered and reconstruct, specifically: be filtered using time series data of the S-G filter method to the EVI and again
Structure.
4. pest and disease damage time DYNAMIC DISTRIBUTION monitoring method according to claim 1, which is characterized in that described to the EVI
Time series data be filtered and reconstruct, specifically: using double Logistic curve-fitting methods to the time series data of the EVI
It is filtered and reconstructs.
5. pest and disease damage time DYNAMIC DISTRIBUTION monitoring method according to claim 1, which is characterized in that described to the EVI
Time series data be filtered and reconstruct, specifically: using asymmetric Gaussian function fitting method to the time series data of the EVI
It is filtered and reconstructs.
6. a kind of pest and disease damage time DYNAMIC DISTRIBUTION monitors system characterized by comprising acquisition module, analysis module, EVI structure
Model block, EVI time series data obtains module, filters reconstructed module and judgment module；
The acquisition module, for acquiring crops each breeding time corresponding canopy spectra data, for the agriculture of contemporaneity
Crop obtains slight pest and disease damage spectrum, moderate pest and disease damage spectrum, severe pest and disease damage spectrum and healthy crops spectrum；
The analysis module, for respectively to the slight pest and disease damage spectrum of the crops, moderate pest and disease damage spectrum, severe disease pest
Evil spectrum and healthy crops spectrum are analyzed, and obtain the crops by the reflectivity of the sensitive band of pest and disease damage；
The EVI constructs module, for constructing EVI using the reflectivity of the sensitive band；
The EVI time series data obtains module, the time series data for the EVI according to the canopy spectra data acquisition；
The filtering reconstructed module is filtered and reconstructs for the time series data to the EVI, obtains by pest and disease damage crops
With the timing curve of healthy crops；
Judgment module judges pest and disease damage feelings for the state according to the timing curve by pest and disease damage crops and healthy crops
Condition: when timing curve is the first state, double-peak feature is presented in curve, is illustrated in bimodal corresponding primary peak period disease pest
Evil outburst, the period pest and disease damage between bimodal are inhibited；When timing curve is second of state, curve occurs abnormal single
Peak feature illustrates to break out in wave crest period pest and disease damage and not inhibit to pest and disease damage；When timing curve is the third state,
Curve does not occur peak value, illustrates that pest and disease damage degree is slight and does not inhibit to pest and disease damage.
7. pest and disease damage time DYNAMIC DISTRIBUTION according to claim 6 monitors system, which is characterized in that the filtering reconstructs mould
Block includes S-G filter, and the S-G filter is for being filtered the time series data of the EVI using S-G filter method and again
Structure.
8. pest and disease damage time DYNAMIC DISTRIBUTION according to claim 6 monitors system, which is characterized in that the filtering reconstructs mould
Block includes double Logistic curve filters, and double Logistic curve filters are used for using double Logistic curve matchings
Method is filtered and reconstructs to the time series data of the EVI.
9. pest and disease damage time DYNAMIC DISTRIBUTION according to claim 6 monitors system, which is characterized in that the filtering reconstructs mould
Block includes asymmetric Gaussian filter, and the asymmetric Gaussian filter is used for using asymmetric Gaussian function fitting method to institute
The time series data for stating EVI is filtered and reconstructs.
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