CN108760660A - A kind of period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method - Google Patents
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Abstract
A kind of period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method disclosed by the invention, including：Obtain the period of seedling establishment leaves of winter wheat chlorophyll contents actual measured value of sampled point；Obtain the multi-spectral remote sensing image of sampled point that unmanned plane shoots and transmits；Multi-spectral remote sensing image is pre-processed, band image of the image reflectance in predetermined threshold value is obtained；Each wave band reflectance value that the sampled point corresponds to pixel is extracted in band image；Chlorophyll content actual measured value and each wave band reflectance value are subjected to correlation analysis with service solution with statistical product, obtain sensitive band；By multiple linear regression analysis method, chlorophyll content appraising model is built；Chlorophyll content appraising model is screened to obtain maximum likelihood estimation model；The chlorophyll content of period of seedling establishment winter wheat in region to be measured is estimated using the maximum likelihood estimation model selected.Chlorophyll content evaluation method provided by the invention based on unmanned plane multispectral image, solves the problems such as existing method is time-consuming and laborious, time stability is poor low with spatial resolution.
Description
Technical field
The present invention relates to crops and agricultural product harmless quantitative remote sensing monitoring fields, more particularly to multispectral based on unmanned plane
The period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method of remote sensing images, and in particular to a kind of period of seedling establishment leaves of winter wheat chlorophyll contents are estimated
Calculation method.
Background technology
Wheat is China's staple food crop, how to improve its yield and quality and is paid more and more attention.For the high yield of wheat
High-quality target, the nutritional status information monitored to quick nondestructive during growth are particularly important.Winter wheat period of seedling establishment refers to morning
Spring wheat field one leaf of wheat seeding more than half grows part and reaches the period that 1-2cm is about one month, which mainly takes root, grows
Leaf and tiller are that late weak seedling is promoted to upgrade, control prosperous seedling excessive growth, when adjusting group size and determining the key of percentage of earbearing tiller height
Phase.For chlorophyll as pigment important in plant photosynthesis, the chlorophyll content in plant leaf blade can not only show plant
With external environment carry out Exchange of material and energy ability, and with the upgrowth situation of crop, primary productivity, carbon sequestration capacity and nitrogen
Utilization rate etc. has close relationship, is the important indicator and the indicator in growth and development of plants stage of crop growing state evaluation.
The real-time monitoring of period of seedling establishment leaves of winter wheat chlorophyll contents is of great significance for understanding wheat growing way, improving yield.
The method of traditional monitoring chlorophyll content in leaf blades mostly uses greatly the methods of spectrophotometer, chemical analysis, and sampling is accurate
It is standby it is long with detection time, efficiency is low, cumbersome, time-consuming and laborious, and has destructiveness to sample, can not be in crop growth period
It is measured in real time, is unfavorable for being operated and being promoted in the actual production process.Though the SPAD values determination methods in field can be fast
Fast indirect gain leaf chlorophyll situation, but be difficult to obtain the space distribution information of field chlorophyll in real time.With remote sensing technology
Development, the direction of remote sensing estimation crop index forward direction quantification and precision is developed, but the satellite remote sensing technology of current main-stream
Due to limiting factors such as revisiting period is long, influenced by weather, image resolution deficiencies, in data stability and spatial and temporal resolution
Etc. be difficult to meet the needs of precision agriculture research.Meanwhile also space shuttle can be utilized to obtain data, but due to boat
Empty aircraft is not easily accessible civil field, so aerial remote sensing images are not easy to obtain.
With scientific and technological progress, unmanned air vehicle technique gradually comes into civil field, unmanned aerial vehicle remote sensing platform easily builds, is at low cost,
Flight range is motor-driven, flying height is flexible, duty cycle is short, and the remotely-sensed data room and time resolution ratio of acquisition is relatively high,
It is not easy to be limited by period and weather condition, therefore, unmanned aerial vehicle remote sensing assessment technology becomes functionization in present precision agriculture and grinds
The hot spot studied carefully.
Therefore, in order to improve or solve the methods of above-mentioned traditional spectrophotometer, chemical analysis and SPAD values measurement side
The problem of method and satellite remote sensing technology, urgently works out one in period of seedling establishment leaves of winter wheat chlorophyll contents estimation field at present
The period of seedling establishment leaves of winter wheat chlorophyll contents estimation models that kind is built based on unmanned plane multi-spectral remote sensing image, to further increase
The precision and time stability and spatial resolution of period of seedling establishment leaves of winter wheat chlorophyll contents remote sensing monitoring are chlorophyll content of plant
Monitoring provides technical support in real time.
Invention content
In order to solve above-mentioned problems of the prior art, the purpose of the present invention is to provide a kind of period of seedling establishment winter wheat
Chlorophyll content evaluation method, to overcome traditional spectrophotometer, chemical analysis method, SPAD values determination methods and satellite remote sensing
Time-consuming and laborious present in technology, the disadvantages such as time stability difference and spatial resolution are low have reached in precision agriculture to returning
The estimation of green phase leaves of winter wheat chlorophyll contents is not limited by period, weather condition, and duty cycle is short, flexibility is high, at low cost
Technique effect.
According to an aspect of the invention, there is provided a kind of period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method, wherein packet
Include following steps：
Obtain the period of seedling establishment leaves of winter wheat chlorophyll contents actual measured value of sampled point；
Obtain the multi-spectral remote sensing image of sampled point that unmanned plane shoots and transmits；
The multi-spectral remote sensing image is pre-processed, band image of the image reflectance in predetermined threshold value is obtained；
Each wave band reflectance value that the sampled point corresponds to pixel is extracted in the band image；
With statistical product and service solution（Statistical Product and Service Solutions, referred to as
SPSS）The chlorophyll content actual measured value and each wave band reflectance value are subjected to correlation analysis, obtain sensitive wave
Section；
Based on the sensitive band and the chlorophyll content actual measured value, by multiple linear regression analysis method, structure leaf is green
Cellulose content appraising model；
The chlorophyll content appraising model is screened to obtain maximum likelihood estimation model using the chlorophyll content actual measured value；
The chlorophyll content of period of seedling establishment winter wheat in region to be measured is estimated using the maximum likelihood estimation model selected.
Further, practical time of measuring is during early spring winter wheat turns green.
Further, the multi-spectral remote sensing image of sampled point for obtaining unmanned plane and shooting and transmitting, including following step
Suddenly：
It is obtained in real time using UAV flight's multispectral camera and the practical multi-spectral remote sensing image measured simultaneously.
Further, the pretreatment includes at least in image mosaic processing, radiant correction processing, geometric correction processing
One.
Further, the band image includes green light band image, red spectral band image, red side band image and close red
Four band images of wave section image.
Further, the sensitive band includes green light band, red spectral band, red side wave section and near infrared band.
Further, polynary gradually linear regression method, polynary input line may be used in the multiple linear regression analysis method
One kind in property homing method or partial least-square regression method.
Further, described that chlorophyll content appraising model is screened to obtain using the chlorophyll content actual measured value
Maximum likelihood estimation model, includes the following steps：
The chlorophyll content actual measured value is divided into modeling sample collection and verification sample set,
Wherein, the modeling sample collection is for building chlorophyll content appraising model and obtaining modeling accuracy, the verification sample
The precision of appraising model of the collection for verifying structure simultaneously obtains verification precision；
Maximum likelihood estimation model is chosen by the modeling accuracy and the verification precision.
Further, it verifies the precision of the appraising model of structure and obtains verification precision, include the following steps：
Each wave band reflectance value in the verification sample set brought into chlorophyll content appraising model to acquire corresponding leaf green
Cellulose content estimated value；
Based on the chlorophyll content estimated value and corresponding chlorophyll content actual measured value in the verification sample set, utilize
Approximating method is verified precision.
Further, the modeling accuracy of the maximum likelihood estimation model is 0.712, and verification precision is 0.616.
According to another aspect of the present invention, a kind of period of seedling establishment leaves of winter wheat chlorophyll contents estimation device is provided, it is described
Equipment includes：
One or more processors；
Memory, for storing one or more programs,
When one or more of programs are executed by one or more of processors so that one or more of processors
Execute method as described in any one of the above embodiments.
According to another aspect of the present invention, a kind of computer-readable storage medium being stored with computer program is provided
Matter, the program realize method as described in any one of the above embodiments when being executed by processor.
Compared with prior art, the invention has the advantages that：
Period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method disclosed by the invention is green based on unmanned plane multispectral image estimation leaf
Cellulose content.Evaluation method disclosed by the invention has been saved compared with the methods of spectrophotometer, chemical analysis needed for sampling
It is time and detection time, efficient, it is easy to operate, time saving and energy saving, and sample will not be destroyed, it can be in crop growth period
It is measured in real time, is conducive to be operated and promoted in the actual production process.It is disclosed by the invention to be based on unmanned plane mostly light
Spectrogram picture estimation chlorophyll content can obtain the chlorophyll spatial distribution state under field scale in real time, be more suitable under large scale
Chlorophyll content estimation.
Meanwhile chlorophyll content evaluation method disclosed by the invention eliminates compared with remote sensing image data evaluation method
Satellite passes by the influences of period and weather conditions, improves flexibility and the stability of time of measuring, drone flying height drop
It is low so that spatial resolution is dropped to the cm grades of unmanned aerial vehicle remote sensing by the 10m grades of satellite remote sensing, is promoted thousands of times, can effectively be gone
Except mixed pixel influences, accurate expression enough to nuance performance under field scale, in the standard for improving estimation down to a certain degree
True property.
Description of the drawings
Fig. 1 is the flow chart of Determination of Chlorophyll Concentration estimation of the embodiment of the present invention；
Fig. 2 is the schematic diagram of chlorophyll content measured value and estimated value fitting under the best-estimated model in the embodiment of the present invention.
Specific implementation mode
The application is described in further detail with reference to embodiment and Figure of description.It is understood that this
The described specific embodiment in place is used only for explaining related invention, rather than the restriction to the invention.Further need exist for explanation
It is to illustrate only for ease of description, in attached drawing and invent relevant part.
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.The application is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
A kind of period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method is present embodiments provided, is included the following steps：
S1, the period of seedling establishment leaves of winter wheat chlorophyll contents actual measured value for obtaining sampled point；
S2, the multi-spectral remote sensing image of sampled point that unmanned plane shoots and transmits is obtained；
S3, the multi-spectral remote sensing image is pre-processed, obtains band image of the image reflectance in predetermined threshold value；
S4, each wave band reflectance value that the sampled point corresponds to pixel is extracted in the band image；
S5, with statistical product with service solution（Statistical Product and Service Solutions,
Abbreviation SPSS）The chlorophyll content actual measured value and each wave band reflectance value are subjected to correlation analysis, obtained quick
Feel wave band；
S6, leaf is built by multiple linear regression analysis method based on the sensitive band and the chlorophyll content actual measured value
Chlorophyll contents appraising model；
S7, chlorophyll content appraising model is screened using the chlorophyll content actual measured value to obtain maximum likelihood estimation model；
S8, the chlorophyll content that period of seedling establishment winter wheat in region to be measured is estimated using the maximum likelihood estimation model selected.
For ease of the understanding of the present invention, estimated with reference to period of seedling establishment leaves of winter wheat chlorophyll contents provided in this embodiment
Method and attached drawing Fig. 1 and Fig. 2, are further described the principle of the present invention：
Unmanned aerial vehicle remote sensing platform used in the present embodiment is carried by big 600 pro of boundary Matrice, six rotor wing unmanned aerial vehicles
Sequoia multispectral cameras composition.
In the method that existing remote sensing image data estimates period of seedling establishment leaves of winter wheat chlorophyll contents, remote sensing platform is built
It is broadly divided into two parts：What sensor and aircraft, wherein sensor referred to is exactly camera, aircraft be exactly unmanned plane, aircraft or
Satellite, aircraft are related to temporal resolution.Existing satellite remote sensing technology, since satellite has certain airborne period, generally
It it is 5-30 days, therefore the technologies such as satellite remote sensing technology is influenced there are revisiting period length, by weather, image resolution deficiency are asked
Topic.And the unmanned plane advantages that have that flight range is motor-driven, flying height is flexible, duty cycle is short etc., as long as and unmanned plane having
It can fulfil assignment, not limited by time restriction and weather in the case of illumination, therefore utilize UAV flight's sensor, have
The relatively high advantage of the remotely-sensed data room and time resolution ratio of acquisition.Meanwhile the flying height of unmanned plane is relatively low, can make
The image spatial resolution that the sensor being mounted on unmanned plane obtains is higher, and spatial resolution is higher, represented by a pixel
Floor area with regard to smaller, the more suitable high-precision estimation of small area.On the other hand, it is loaded between unmanned plane and various sensors
Flexibly, suitable sensor can be selected to arrange in pairs or groups with unmanned plane according to the actual demand of survey region, composition unmanned plane is distant
Feel platform.
S1, the chlorophyll content actual measured value for obtaining sampled point
Fieldwork selects during early spring winter wheat period of seedling establishment, is carried out with unmanned aerial vehicle remote sensing image capture synchronization.Entirely grinding
Sampled point is laid within the scope of the areas Jiu Yang, research zoning is divided into multiple homogeneous sample prescriptions, selection one is with generation in each sample prescription region
The sampled point of table, it is desirable that sampled point is evenly distributed as much as possible within the scope of entirely research sample area.Using SPAD chlorophyll meters and
Trimble GEO 7X Centimeter Levels handhold GPSs record the wheat chlorophyll content and coordinate of each sampled point respectively.
S2, the multi-spectral remote sensing image of sampled point that unmanned plane shoots and transmits is obtained
Sequoia multispectral cameras are carried using big 600 pro of boundary Matrice, six rotor wing unmanned aerial vehicle platforms, are recorded according to GPS
Each sampled point location information, control unmanned plane 100 meters of height above sample area, it is continuous to obtain in real time and fieldwork
Period of seedling establishment winter wheat multi-spectral remote sensing image of the same period.
S3, band image is obtained
Unmanned plane is shot and the multi-spectral remote sensing image that transmits spliced, the pretreatments such as radiant correction and geometric correction, obtain
Reach 4-5cm to image resolution ratio, including four green light, feux rouges, red side and near-infrared band images.
S4, each wave band reflectance value of extraction
Using Pixel locator tools in ENVI5.1 Classic, the GPS recorded in step S1 is input to by locating in advance
On the unmanned aerial vehicle remote sensing image of reason, corresponding pixel is found, and extracts each wave band reflectance value of the Xiang Yuan.
S5, sensitive band is obtained
With statistical product and service solution（Statistical Product and Service Solutions, referred to as
SPSS）The actual measured value of each sampled point chlorophyll content and each wave band reflectance value of remote sensing images are subjected to correlation analysis,
Obtain the sensitive band high with chlorophyll content correlation：G（Green light band）,R（Red spectral band）,REG（Red side wave section）And NIR
（Near infrared band）.
The spectral signature of chlorophyll is concentrated mainly on four green light, feux rouges, red side and near-infrared wave bands, i.e. Sequoia is more
Four wave bands that spectrum camera is included, EO-1 hyperion camera might have more rich spectral information, but for vegetation coverage
For estimation, only four green light, feux rouges, red side and near-infrared wave bands are sufficient, and abundant spectral information can only be brought greatly
The data redundancy of amount increases the difficulty in data handling procedure.Table 1 is sensitive band and chlorophyll content in the embodiment of the present invention
Related coefficient.
S6, structure chlorophyll content appraising model
It is modeling sample collection by the chlorophyll content actual measured value of all samples（About total sample 2/3）With verification sample set（About
Total sample 1/3）Two parts.
Modeling sample collection is chosen, using 4 sensitive bands screened in step S5 as independent variable, chlorophyll content actual measurement
Value is dependent variable, and multiple linear regression is carried out by a variety of recurrence modes to independent variable and dependent variable, is obtained distant based on unmanned plane
Feel the chlorophyll content appraising model of image.
Wherein, recurrence mode can select polynary gradually linear regression, the recurrence of polynary input linear and offset minimum binary to return
The modes such as return.
S7, screening obtain maximum likelihood estimation model
Model is carried out to the multiple chlorophyll content appraising models obtained above by a variety of recurrence modes with verification sample set
Verification：Each wave band reflectance value for verifying sampling point in sample set is brought into respectively in multiple chlorophyll content appraising models and is acquired
Corresponding chlorophyll content estimated value, by the reality of obtained chlorophyll content estimated value and corresponding each sampling point in verification sample set
Border measured value is fitted, and is verified precision.
In the present embodiment, with verification precision, the maximum likelihood estimation model preferably obtained is comprehensive modeling precision：
Wherein, Y is chlorophyll content estimated value；G is green light band reflectance value；R is red spectral band reflectance value；REG is red
Side wave section reflectance value and NIR are near infrared band reflectance value.
The modeling accuracy of maximum likelihood estimation model is 0.712 in the present embodiment, and verification precision is 0.616.
S8, the chlorophyll content for estimating period of seedling establishment winter wheat in region to be measured
The leaf green content estimation optimal models that above-mentioned the present embodiment obtains are applied to Kenli area of Dongying city farmland,
Land use pattern is predominantly ploughed and unused land, and main Winter Wheat Planted of ploughing carries out vegetation fraction estimation, estimated
It is 0.742 to calculate precision.
The present embodiment additionally provides a kind of period of seedling establishment leaves of winter wheat chlorophyll contents estimation device, and the equipment includes：
One or more processors；
Memory, for storing one or more programs,
When one or more of programs are executed by one or more of processors so that one or more of processors
Execute any one of them method as above.
The present embodiment additionally provides a kind of computer readable storage medium being stored with computer program, which is handled
Device realizes any one of them method as above when executing.
Above description is only the preferred embodiment of the application and the explanation to institute's application technology principle.People in the art
Member should be appreciated that invention scope involved in the application, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic
Scheme, while should also cover in the case where not departing from the inventive concept, it is carried out by above-mentioned technical characteristic or its equivalent feature
Other technical solutions of arbitrary combination and formation.Such as features described above has similar work(with (but not limited to) disclosed herein
Energy.
Claims (10)
1. a kind of period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method, which is characterized in that include the following steps：
Obtain the period of seedling establishment leaves of winter wheat chlorophyll contents actual measured value of sampled point；
Obtain the multi-spectral remote sensing image of sampled point that unmanned plane shoots and transmits；
The multi-spectral remote sensing image is pre-processed, band image of the image reflectance in predetermined threshold value is obtained；
Each wave band reflectance value that the sampled point corresponds to pixel is extracted in the band image；
With statistical product and service solution（Statistical Product and Service Solutions, referred to as
SPSS）The chlorophyll content actual measured value and each wave band reflectance value are subjected to correlation analysis, obtain sensitive wave
Section；
Based on the sensitive band and the chlorophyll content actual measured value, by multiple linear regression analysis method, structure leaf is green
Cellulose content appraising model；
Chlorophyll content appraising model is screened using the chlorophyll content actual measured value to obtain maximum likelihood estimation model；
The chlorophyll content of period of seedling establishment winter wheat in region to be measured is estimated using the maximum likelihood estimation model selected.
2. period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method according to claim 1, which is characterized in that when practical measurement
Between turn green for early spring winter wheat during.
3. period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method according to claim 1, which is characterized in that the acquisition nothing
The multi-spectral remote sensing image of the sampled point of man-machine shooting and transmission, includes the following steps：
It is obtained in real time using UAV flight's multispectral camera and the practical multi-spectral remote sensing image measured simultaneously.
4. period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method according to claim 1, which is characterized in that the pretreatment
Including at least one in image mosaic processing, radiant correction processing, geometric correction processing.
5. period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method according to claim 1, which is characterized in that the wave band figure
As including four green light band image, red spectral band image, red side band image and near infrared band image band images.
6. period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method according to claim 1, which is characterized in that the sensitivity wave
Section includes green light band, red spectral band, red side wave section and near infrared band.
7. period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method according to claim 1, which is characterized in that the polynary line
Polynary gradually linear regression method, polynary input linear homing method or Partial Least Squares Regression side may be used in property homing method
One kind in method.
8. period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method according to claim 1, which is characterized in that use the leaf
Chlorophyll contents actual measured value screens chlorophyll content appraising model to obtain maximum likelihood estimation model, includes the following steps：
The chlorophyll content actual measured value is divided into modeling sample collection and verification sample set,
Wherein, the modeling sample collection is for building chlorophyll content appraising model and obtaining modeling accuracy, the verification sample
The precision of appraising model of the collection for verifying structure simultaneously obtains verification precision；
Maximum likelihood estimation model is chosen by the modeling accuracy and the verification precision.
9. period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method according to claim 8, which is characterized in that verify structure
The precision of appraising model simultaneously obtains verification precision, includes the following steps：
Each wave band reflectance value in the verification sample set brought into chlorophyll content appraising model to acquire corresponding leaf green
Cellulose content estimated value；
Based on the chlorophyll content estimated value and corresponding chlorophyll content actual measured value in the verification sample set, utilize
Approximating method is verified precision.
10. period of seedling establishment leaves of winter wheat chlorophyll contents evaluation method according to claim 9, which is characterized in that described optimal
The modeling accuracy of appraising model is 0.712, and verification precision is 0.616.
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| CN115950838A (en) * | 2022-09-20 | 2023-04-11 | 中国水利水电科学研究院 | Summer corn drought unmanned aerial vehicle rapid monitoring and distinguishing method based on chlorophyll content |
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