CN116843082A - Honeysuckle yield evaluation method and device based on space-sky-earth integrated monitoring technology - Google Patents
Honeysuckle yield evaluation method and device based on space-sky-earth integrated monitoring technology Download PDFInfo
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Abstract
The application relates to the technical field of flower tea estimation and production, and discloses a honeysuckle yield estimation method and device based on an aerospace-ground integrated monitoring technology; the method comprises the following steps: acquiring honeysuckle land block satellite images, unmanned aerial vehicle images and ground Internet of things environment monitoring data, and analyzing the data to acquire honeysuckle yield evaluation factors; sample estimated production data of each estimated index is obtained according to the index characteristics, and each index weight is analyzed and calculated according to the sample data; establishing a honeysuckle yield per unit evaluation model, and extracting planting areas through satellites and unmanned aerial vehicles to realize honeysuckle yield evaluation; the method has the advantages that the space-earth integrated monitoring technology is adopted to monitor holographic data of various dimensions, a honeysuckle space-earth integrated estimated product mathematical model is established by analyzing the holographic data, and estimated products from different space scales of the space-earth can be estimated, so that the problem that the estimated product accuracy is low by mainly relying on single remote sensing data is solved.
Description
Technical Field
The application relates to the technical field of flower tea estimation, in particular to a honeysuckle yield estimation method and device based on an aerospace-ground integrated monitoring technology.
Background
The honeysuckle is a traditional authentic medicinal material, has economic value and medicinal value, accurately predicts the yield of the honeysuckle in areas and even nationwide, and can provide technical support for the establishment of planting management for governments in major areas and large planting households; in the past, the yield evaluation is mainly realized by adopting a satellite remote sensing monitoring method, the method is single, and the evaluation precision is lower; for the method relying on manpower, the honeysuckle flower has a short flowering period, so that the estimation of the yield in a large area cannot be performed.
Along with the rapid development of image technology and Internet of things technology in the civil field, the related technology is used in estimating the yield of the honeysuckle, so that the yield estimation efficiency and the yield estimation precision can be effectively improved, and the yield estimation of the honeysuckle in a large area can be completed in a short time through an algorithm.
Disclosure of Invention
The application aims to provide a honeysuckle yield evaluation method and device based on an air-space-ground integrated monitoring technology, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present application provides the following technical solutions:
the honeysuckle yield evaluation method based on the space-earth integrated monitoring technology is characterized by comprising the following steps of:
acquiring a plurality of groups of estimated production reference data of different categories in the bud period and storing the estimated production reference data into a database;
based on a preset yield evaluation method and the acquired data category, dividing the estimated yield factors of the honeysuckle into satellite factors, air factors and environmental factors;
based on historical data, simulating and establishing the correlation among the satellite factors, the aerial factors, the environmental factors and the honeysuckle yield, and establishing an aerial-ground integrated honeysuckle unit yield model;
the honeysuckle planting area is obtained by adopting a maximum likelihood estimation method based on satellite and unmanned aerial vehicle images and combining a GIS system, and the total yield of the honeysuckle in the area is estimated by using a honeysuckle unit yield model.
As a further aspect of the application: the estimated production reference data specifically comprises satellite image spectrum data, unmanned plane visible light image data, internet of things sensing data and environmental meteorological data of honeysuckle flowers from germination to bud, wherein the Internet of things sensing data and the environmental meteorological data comprise temperature, humidity, illumination and soil nitrogen, potassium and phosphorus content.
As still further aspects of the application: the honeysuckle yield estimation factors specifically comprise:
the satellite factors include vegetation index and terrain elevation;
the aerial factors comprise leaf area indexes and the number of flower buds in unit area;
the environmental factors include surface temperature, light accumulation and soil nitrogen, phosphorus and potassium.
As still further aspects of the application: based on historical data, the correlation between the satellite factors, the air factors and the environmental factors and the honeysuckle yield is simulated and established, and the step of establishing the space-earth integrated honeysuckle unit yield model specifically comprises the following steps:
based on historical satellite data, obtaining and analyzing normalized vegetation index of the bud period of the honeysuckle in the area, and calculating an NDVI (non-natural killer) correlation coefficient p according to the recorded yield data by a formula p 1 To obtain the NDVI estimated linear equation Y 1 Ndvi+b, and establishing a terrain correlation coefficient p from the terrain elevation data 2 And yield estimation equation Y 2 ；
Identifying the number of flower buds in a plurality of sample areas based on unmanned aerial vehicle aerial photographing data, establishing an unmanned aerial vehicle monitoring yield sample library based on the actual yield of the corresponding areas, and calculating a flower bud number correlation coefficient p 3 And yield estimation equation Y 3 The method comprises the steps of carrying out a first treatment on the surface of the Extracting honeysuckle land parcel leaf area index based on unmanned aerial vehicle image, and establishing leaf area correlation coefficient p 4 And yield estimation equation Y 4 ；
The corresponding strong environmental data, soil nitrogen, phosphorus and potassium data are respectively calculated and selected, the surface area and light area data before the bud period from germination are calculated and selected, and the heat accumulation coefficient p is calculated 5 Light product coefficient p 6 Soil coefficient p 7 Obtaining an estimated yield equation Y 5 、Y 6 Y and Y 7 ；
Correlating a plurality of yield coefficients p i (i= … 7) and establishing a judgment coefficient a=p for every two indexes j /p i *(s j -s i +1), wherein s is an arrangement number, and constructing a coefficient matrix A (a) based on the judgment coefficients ij ) nxn Wherein a is ij ＝1/a ji ；
Calculating a characteristic vector w of the coefficient matrix A, and carrying out normalization processing to obtain a relative weight Wi;
building a honeysuckle flower unit yield model Y=Σ (Y) based on space-earth integration i *W i )，(i＝1…7)。
As still further aspects of the application: the formula p is:
wherein X is the NDVI index and Y is the actual yield.
The embodiment of the application aims to provide a honeysuckle yield evaluation device with an integrated monitoring technology, which comprises:
the data acquisition module is used for acquiring a plurality of groups of estimated production reference data of different categories in the bud period of the honeysuckle and storing the estimated production reference data into the database;
the data processing module is used for dividing the estimated honeysuckle yield factors into satellite factors, air factors and environmental factors based on a preset yield estimation method and acquired data types;
the model generation module is used for simulating and establishing the association among the satellite factors, the air factors and the environmental factors and the honeysuckle yield based on the historical data, and establishing an air-ground integrated honeysuckle unit yield model;
and the yield evaluation module is used for acquiring the honeysuckle planting area by adopting a maximum likelihood estimation method based on satellite and unmanned aerial vehicle images and combining a GIS system, and estimating the total yield of the honeysuckle in the area by using a honeysuckle unit yield model.
As a further aspect of the application: the estimated production reference data specifically comprises satellite image spectrum data, unmanned plane visible light image data, internet of things sensing data and environmental meteorological data of honeysuckle flowers from germination to bud, wherein the Internet of things sensing data and the environmental meteorological data comprise temperature, humidity, illumination and soil nitrogen, potassium and phosphorus content.
As still further aspects of the application: the honeysuckle yield estimation factors specifically comprise:
the satellite factors include vegetation index and terrain elevation;
the aerial factors comprise leaf area indexes and the number of flower buds in unit area;
the environmental factors include surface temperature, light accumulation and soil nitrogen, phosphorus and potassium.
As still further aspects of the application: the model generation module specifically comprises:
the satellite data calculation unit is used for acquiring and analyzing the normalized vegetation index of the bud period of the area where the honeysuckle is positioned based on the historical satellite data, and calculating the NDVI (non-natural frequency scale) correlation coefficient p according to the recorded yield data by a formula p 1 To obtain the NDVI estimated linear equation Y 1 Ndvi+b, and establishing a terrain correlation coefficient p from the terrain elevation data 2 And yield estimation equation Y 2 ；
Unmanned aerial vehicle data fitting unit for identifying a plurality of sample areas based on unmanned aerial vehicle aerial photographing dataThe number of flower buds, establishing an unmanned aerial vehicle monitoring yield sample library based on the actual yield of the corresponding area, and calculating a flower bud number correlation coefficient p 3 And yield estimation equation Y 3 The method comprises the steps of carrying out a first treatment on the surface of the Extracting honeysuckle land parcel leaf area index based on unmanned aerial vehicle image, and establishing leaf area correlation coefficient p 4 And yield estimation equation Y 4 ；
The data calculation unit of the Internet of things is used for calling corresponding strong environment data, soil nitrogen, phosphorus and potassium data, respectively calculating and selecting the surface area data and the photo-area data before the bud period from the germination to the bud period, and calculating the heat accumulation coefficient p 5 Light product coefficient p 6 Soil coefficient p 7 Obtaining an estimated yield equation Y 5 、Y 6 Y and Y 7 ；
A parameter weight processing unit for processing a plurality of yield correlation coefficients p i (i= … 7) and establishing a judgment coefficient a=p for every two indexes j /p i *(s j -s i +1), wherein s is an arrangement number, and constructing a coefficient matrix A (a) based on the judgment coefficients ij ) nxn Wherein a is ij ＝1/a ji Calculating a characteristic vector w of the coefficient matrix A, and carrying out normalization processing to obtain a relative weight Wi;
an evaluation model generation unit for establishing a honeysuckle flower unit yield model y=Σ (Y i *W i )，(i＝1…7)。
As still further aspects of the application: the formula p in the satellite data fitting unit is:
wherein X is the NDVI index and Y is the actual yield.
Compared with the prior art, the application has the beneficial effects that: by adopting an air-space-ground integrated monitoring technology, under the multi-dimensional cooperation of a satellite, an image unmanned aerial vehicle and an Internet of things network, holographic data of various dimensions are monitored, and a honeysuckle air-space-ground integrated estimated product mathematical model is established by analyzing the holographic data, and the estimated products of different space dimensions of the air-space can be estimated, so that the problem that the estimated product accuracy is low by mainly relying on single remote sensing data is solved.
Drawings
Fig. 1 is a flow chart of a honeysuckle yield evaluation method based on an air-ground integrated monitoring technology.
Fig. 2 is a structural composition of a honeysuckle yield evaluation method based on an aerospace-ground integrated monitoring technology.
Fig. 3 is a block flow diagram of a model established in the honeysuckle yield evaluation method based on the space-earth integrated monitoring technology.
Fig. 4 is a block diagram of a honeysuckle yield evaluation device based on an air-ground integrated monitoring technology.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Specific implementations of the application are described in detail below in connection with specific embodiments.
As shown in fig. 1, the honeysuckle yield evaluation method based on the space-to-ground integrated monitoring technology provided by the embodiment of the application comprises the following steps:
s10, acquiring a plurality of groups of estimated yield reference data of different categories in the bud period of the honeysuckle and storing the estimated yield reference data into a database.
S20, dividing the estimated honeysuckle yield factors into satellite factors, air factors and environmental factors based on a preset yield estimation method and acquired data types.
And S30, based on historical data, simulating and establishing the association among the satellite factors, the air factors and the environmental factors and the honeysuckle yield, and establishing an air-ground integrated honeysuckle unit yield model.
S40, obtaining the honeysuckle planting area by adopting a maximum likelihood estimation method based on satellite and unmanned aerial vehicle images and combining a GIS system, and estimating the total yield of the honeysuckle in the area by using a honeysuckle unit yield model.
Further, the estimated production reference data specifically comprises satellite image spectrum data of multiple periods of honeysuckle flower bud period, unmanned plane visible light image data, and Internet of things sensing data and environmental meteorological data of honeysuckle flower from germination to bud period, wherein the Internet of things sensing data and the environmental meteorological data comprise temperature, humidity, illumination and soil nitrogen, potassium and phosphorus content.
Further, the estimating factors of the honeysuckle flowers specifically include:
the satellite factors include vegetation index and terrain elevation;
the aerial factors comprise leaf area indexes and the number of flower buds in unit area;
the environmental factors include surface temperature, light accumulation and soil nitrogen, phosphorus and potassium.
In the embodiment, according to the evaluation area, acquiring honeysuckle land satellite image, unmanned aerial vehicle image and ground Internet of things environment monitoring data (namely three data monitoring acquisition paths corresponding to sky, day and earth respectively), selecting multi-time satellite image spectrum data and unmanned aerial vehicle visible light image data in the bud period of the honeysuckle; acquiring sensing data of the Internet of things and environmental meteorological data of honeysuckle from germination to bud period, wherein the sensing data comprise temperature, humidity, illumination and soil nitrogen, phosphorus and potassium; dividing the estimated yield factors of the honeysuckle into satellite factors, air factors and environmental factors according to the known crop yield evaluation method and the acquired data types, wherein the satellite factors comprise vegetation indexes (normalized vegetation indexes are adopted), terrain heights, the air factors comprise leaf area indexes and flower bud numbers in unit area, the environmental factors comprise surface area temperature, light area and soil nitrogen phosphorus potassium, after a yield evaluation model is established based on the data content analysis, images are acquired through a satellite unmanned aerial vehicle, the honeysuckle planting area is extracted by adopting a maximum likelihood estimation method, the honeysuckle planting area is acquired by combining a GIS system, and the total yield of the honeysuckle in the area is estimated by using a honeysuckle unit yield model Y; by adopting the space-earth integrated monitoring technology to monitor various dimensional holographic data and analyze the holographic data, a honeysuckle space-earth integrated estimated product mathematical model is established, and the estimated products from different space scales of the space-earth can be estimated, so that the problem that the estimated product accuracy is not high by mainly relying on single remote sensing data at present is solved.
As another preferred embodiment of the present application, the step of establishing the space-earth integrated honeysuckle unit production model based on the historical data by simulating and establishing the correlation among the satellite factor, the space factor and the environmental factor and the honeysuckle yield specifically comprises:
based on historical satellite data, obtaining and analyzing normalized vegetation index of the bud period of the honeysuckle in the area, and calculating an NDVI (non-natural killer) correlation coefficient p according to the recorded yield data by a formula p 1 To obtain the NDVI estimated linear equation Y 1 Ndvi+b, and establishing a terrain correlation coefficient p from the terrain elevation data 2 And yield estimation equation Y 2 。
Identifying the number of flower buds in a plurality of sample areas based on unmanned aerial vehicle aerial photographing data, establishing an unmanned aerial vehicle monitoring yield sample library based on the actual yield of the corresponding areas, and calculating a flower bud number correlation coefficient p 3 And yield estimation equation Y 3 The method comprises the steps of carrying out a first treatment on the surface of the Extracting honeysuckle land parcel leaf area index based on unmanned aerial vehicle image, and establishing leaf area correlation coefficient p 4 And yield estimation equation Y 4 。
The corresponding strong environmental data, soil nitrogen, phosphorus and potassium data are respectively calculated and selected, the surface area and light area data before the bud period from germination are calculated and selected, and the heat accumulation coefficient p is calculated 5 Light product coefficient p 6 Soil coefficient p 7 Obtaining an estimated yield equation Y 5 、Y 6 Y and Y 7 。
Correlating a plurality of yield coefficients p i (i= … 7) and establishing a judgment coefficient a=p for every two indexes j /p i *(s j -s i +1), wherein s is an arrangement number, and constructing a coefficient matrix A (a) based on the judgment coefficients ij ) nxn Wherein a is ij ＝1/a ji 。
And calculating a characteristic vector w of the coefficient matrix A, and carrying out normalization processing to obtain a relative weight Wi.
Building a honeysuckle flower unit yield model Y=Σ (Y) based on space-earth integration i *W i )，(i＝1…7)。
Further, the formula p is:
wherein X is the NDVI index and Y is the actual yield.
In the embodiment, based on historical data, correlation is established between all factors and yield of the space, the satellite data of the first N years (N > 3) from a national satellite meteorological center is selected, and the normalized vegetation index (NDVI) of the bud period of the area where the honeysuckle is located is analyzed; the unmanned aerial vehicle assists in establishing a sample library, the flying height of the unmanned aerial vehicle is greater than 100 meters during aerial photography, the number of flower buds is identified through images, a plurality of sample areas are selected, picking, drying and weighing are carried out, and a honeysuckle unmanned aerial vehicle monitoring yield sample library is established; for meteorological environment data and the like (i.e., ground data), the data of the previous N years is called as basic training data content, wherein N is greater than 3.
As shown in fig. 4, the present application further provides a honeysuckle yield evaluation device based on an integrated space-earth monitoring technology, which comprises:
the data acquisition module 100 is used for acquiring a plurality of groups of estimated production reference data of different categories in the bud period and storing the estimated production reference data into a database.
The data processing module 200 is configured to divide the estimated honeysuckle yield factors into satellite factors, air factors and environmental factors based on a preset yield evaluation method and acquired data types.
The model generation module 300 is used for simulating and establishing the association among the satellite factors, the air factors and the environmental factors and the honeysuckle yield based on the historical data, and establishing an air-ground integrated honeysuckle unit yield model.
The yield evaluation module 400 is configured to obtain a honeysuckle planting area by adopting a maximum likelihood estimation method based on satellite and unmanned aerial vehicle images and combining a GIS system, and estimate the total yield of the honeysuckle in the area by using a honeysuckle unit yield model.
As another preferred embodiment of the application, the estimated production reference data specifically comprises satellite image spectrum data of multiple periods of honeysuckle flower bud period, unmanned plane visible light image data, and Internet of things sensing data and environmental meteorological data of honeysuckle flower from germination to bud period, wherein the Internet of things sensing data and the environmental meteorological data comprise temperature, humidity, illumination and soil nitrogen, potassium and phosphorus content.
As another preferred embodiment of the present application, the plurality of estimated honeysuckle factors specifically include:
the satellite factors include vegetation index and terrain elevation;
the aerial factors comprise leaf area indexes and the number of flower buds in unit area;
the environmental factors include surface temperature, light accumulation and soil nitrogen, phosphorus and potassium.
As another preferred embodiment of the present application, the model generating module specifically includes:
the satellite data calculation unit is used for acquiring and analyzing the normalized vegetation index of the bud period of the area where the honeysuckle is positioned based on the historical satellite data, and calculating the NDVI (non-natural frequency scale) correlation coefficient p according to the recorded yield data by a formula p 1 To obtain the NDVI estimated linear equation Y 1 Ndvi+b, and establishing a terrain correlation coefficient p from the terrain elevation data 2 And yield estimation equation Y 2 。
The unmanned aerial vehicle data calculation unit is used for identifying the number of the flower buds of a plurality of sample areas based on unmanned aerial vehicle aerial photographing data, establishing an unmanned aerial vehicle monitoring yield sample library based on the actual yield of the corresponding areas, and calculating a flower bud number correlation coefficient p 3 And yield estimation equation Y 3 The method comprises the steps of carrying out a first treatment on the surface of the Extracting honeysuckle land parcel leaf area index based on unmanned aerial vehicle image, and establishing leaf area correlation coefficient p 4 And yield estimation equation Y 4 。
The data calculation unit of the Internet of things is used for calling corresponding strong environment data, soil nitrogen, phosphorus and potassium data, respectively calculating and selecting the surface area data and the photo-area data before the bud period from the germination to the bud period, and calculating the heat accumulation coefficient p 5 Light product coefficient p 6 Soil coefficient p 7 Obtaining an estimated yield equation Y 5 、Y 6 Y and Y 7 。
A parameter weight processing unit for processing a plurality of yield correlation coefficients p i (i= … 7) and establishing a judgment coefficient a=p for every two indexes j /p i *(s j -s i +1), wherein s is an arrangement number, and constructing a coefficient matrix A (a) based on the judgment coefficients ij ) nxn Wherein a is ij ＝1/a ji And calculating a characteristic vector w of the coefficient matrix A, and carrying out normalization processing to obtain a relative weight Wi.
An evaluation model generation unit for establishing a honeysuckle flower unit yield model y=Σ (Y i *W i )，(i＝1…7)。
As another preferred embodiment of the present application, the formula p in the satellite data fitting unit is:
wherein X is the NDVI index and Y is the actual yield.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. The honeysuckle yield evaluation method based on the space-earth integrated monitoring technology is characterized by comprising the following steps of:
acquiring a plurality of groups of estimated production reference data of different categories in the bud period and storing the estimated production reference data into a database;
based on a preset yield evaluation method and the acquired data category, dividing the estimated yield factors of the honeysuckle into satellite factors, air factors and environmental factors;
based on historical data, simulating and establishing the correlation among the satellite factors, the aerial factors, the environmental factors and the honeysuckle yield, and establishing an aerial-ground integrated honeysuckle unit yield model;
the honeysuckle planting area is obtained by adopting a maximum likelihood estimation method based on satellite and unmanned aerial vehicle images and combining a GIS system, and the total yield of the honeysuckle in the area is estimated by using a honeysuckle unit yield model.
2. The honeysuckle yield evaluation method based on the space-earth integrated monitoring technology according to claim 1, wherein the yield evaluation reference data specifically comprises satellite image spectrum data of multiple periods of honeysuckle bud period, unmanned aerial vehicle visible light image data, and Internet of things sensing data and environmental meteorological data of honeysuckle from germination to bud period, wherein the Internet of things sensing data and the environmental meteorological data comprise temperature, humidity, illumination and soil nitrogen, potassium and phosphorus content.
3. The method for evaluating the yield of honeysuckle based on the space-time integrated monitoring technology according to claim 2, wherein the plurality of honeysuckle yield evaluating factors specifically comprise:
the satellite factors include vegetation index and terrain elevation;
the aerial factors comprise leaf area indexes and the number of flower buds in unit area;
the environmental factors include surface temperature, light accumulation and soil nitrogen, phosphorus and potassium.
4. The method for evaluating the yield of honeysuckle based on the space-earth integrated monitoring technology according to claim 3, wherein the step of establishing the space-earth integrated honeysuckle unit yield model based on the historical data by simulating and establishing the correlation among the satellite factors, the air factors and the environmental factors and the yield of honeysuckle specifically comprises the following steps:
based on historical satellite data, obtaining and analyzing normalized vegetation index of the bud period of the honeysuckle in the area, and calculating an NDVI (non-natural killer) correlation coefficient p according to the recorded yield data by a formula p 1 To obtain the NDVI estimated linear equation Y 1 =a×nvdi+b, and building a terrain correlation coefficient p from terrain elevation data 2 And yield estimation equation Y 2 ；
Identifying the number of flower buds in a plurality of sample areas based on unmanned aerial vehicle aerial photographing data, establishing an unmanned aerial vehicle monitoring yield sample library based on the actual yield of the corresponding areas, and calculating a flower bud number correlation coefficient p 3 And yield estimation equation Y 3 The method comprises the steps of carrying out a first treatment on the surface of the Extracting honeysuckle land parcel leaf area index based on unmanned aerial vehicle image, and establishing leaf area correlation coefficient p 4 And yield estimation equation Y 4 ；
The corresponding strong environmental data, soil nitrogen, phosphorus and potassium data are respectively calculated and selected from germination to the earth area before bud periodMild light product data, calculating the temperature coefficient p 5 Light product coefficient p 6 Soil coefficient p 7 Obtaining an estimated yield equation Y 5 、Y 6 Y and Y 7 ；
Correlating a plurality of yield coefficients p i (i= … 7) and establishing a judgment coefficient a=p for every two indexes j /p i *(s j -s i +1), wherein s is an arrangement number, and constructing a coefficient matrix A (a) based on the judgment coefficients ij ) nxn Wherein a is ij ＝1/a ji ；
Calculating a characteristic vector w of the coefficient matrix A, and carrying out normalization processing to obtain a relative weight Wi;
building a honeysuckle flower unit yield model Y=Σ (Y) based on space-earth integration i *W i )，(i＝1…7)。
5. The method for evaluating the yield of honeysuckle based on the space-earth integrated monitoring technology according to claim 4, wherein the formula p is:
wherein X is the NDVI index and Y is the actual yield.
6. Honeysuckle output evaluation device based on space-earth integration monitoring technology, its characterized in that contains:
the data acquisition module is used for acquiring a plurality of groups of estimated production reference data of different categories in the bud period of the honeysuckle and storing the estimated production reference data into the database;
the data processing module is used for dividing the estimated honeysuckle yield factors into satellite factors, air factors and environmental factors based on a preset yield estimation method and acquired data types;
the model generation module is used for simulating and establishing the association among the satellite factors, the air factors and the environmental factors and the honeysuckle yield based on the historical data, and establishing an air-ground integrated honeysuckle unit yield model;
and the yield evaluation module is used for acquiring the honeysuckle planting area by adopting a maximum likelihood estimation method based on satellite and unmanned aerial vehicle images and combining a GIS system, and estimating the total yield of the honeysuckle in the area by using a honeysuckle unit yield model.
7. The honeysuckle yield evaluation device based on the space-earth integrated monitoring technology according to claim 6, wherein the yield evaluation reference data specifically comprises satellite image spectrum data of multiple periods of honeysuckle bud period, unmanned aerial vehicle visible light image data, and Internet of things sensing data and environmental meteorological data of honeysuckle from germination to bud period, wherein the Internet of things sensing data and the environmental meteorological data comprise temperature, humidity, illumination and soil nitrogen, potassium and phosphorus content.
8. The honeysuckle yield evaluation device based on the space-time integrated monitoring technology according to claim 7, wherein the plurality of honeysuckle yield evaluation factors specifically comprise:
the satellite factors include vegetation index and terrain elevation;
the aerial factors comprise leaf area indexes and the number of flower buds in unit area;
the environmental factors include surface temperature, light accumulation and soil nitrogen, phosphorus and potassium.
9. The honeysuckle yield evaluation device based on the space-earth integrated monitoring technology according to claim 8, wherein the model generation module specifically comprises:
the satellite data calculation unit is used for acquiring and analyzing the normalized vegetation index of the bud period of the area where the honeysuckle is positioned based on the historical satellite data, and calculating the NDVI (non-natural frequency scale) correlation coefficient p according to the recorded yield data by a formula p 1 To obtain the NDVI estimated linear equation Y 1 =a×nvdi+b, and building a terrain correlation coefficient p from terrain elevation data 2 And yield estimation equation Y 2 ；
Unmanned aerial vehicle data fitting unit for identifying based on unmanned aerial vehicle aerial photographing dataThe number of flower buds in a plurality of sample areas is distinguished, an unmanned aerial vehicle monitoring yield sample library is built based on the actual yield of the corresponding areas, and a flower bud number correlation coefficient p is calculated 3 And yield estimation equation Y 3 The method comprises the steps of carrying out a first treatment on the surface of the Extracting honeysuckle land parcel leaf area index based on unmanned aerial vehicle image, and establishing leaf area correlation coefficient p 4 And yield estimation equation Y 4 ；
The data calculation unit of the Internet of things is used for calling corresponding strong environment data, soil nitrogen, phosphorus and potassium data, respectively calculating and selecting the surface area data and the photo-area data before the bud period from the germination to the bud period, and calculating the heat accumulation coefficient p 5 Light product coefficient p 6 Soil coefficient p 7 Obtaining an estimated yield equation Y 5 、Y 6 Y and Y 7 ；
A parameter weight processing unit for processing a plurality of yield correlation coefficients p i (i= … 7) and establishing a judgment coefficient a=p for every two indexes j /p i *(s j -s i +1), wherein s is an arrangement number, and constructing a coefficient matrix A (a) based on the judgment coefficients ij ) nxn Wherein a is ij ＝1/a ji Calculating a characteristic vector w of the coefficient matrix A, and carrying out normalization processing to obtain a relative weight Wi;
an evaluation model generation unit for establishing a honeysuckle flower unit yield model y=Σ (Y i *W i )，(i＝1…7)。
10. The honeysuckle yield evaluation device based on the space-to-ground integrated monitoring technology according to claim 9, wherein the formula p in the satellite data calculation unit is:
wherein X is the NDVI index and Y is the actual yield.
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