CN113743819A - Method and device for crop yield estimation, electronic equipment and storage medium - Google Patents

Method and device for crop yield estimation, electronic equipment and storage medium Download PDF

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CN113743819A
CN113743819A CN202111078078.1A CN202111078078A CN113743819A CN 113743819 A CN113743819 A CN 113743819A CN 202111078078 A CN202111078078 A CN 202111078078A CN 113743819 A CN113743819 A CN 113743819A
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关盛勇
周会珍
朱菊蕊
陈晨
李冬冬
谌华
何建军
文强
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Twenty First Century Aerospace Technology Co ltd
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Abstract

The invention relates to a method, a device, electronic equipment and a storage medium for crop yield estimation, wherein the method comprises the following steps: determining a key time phase of a remote sensing image based on a key phenological period of crops to be estimated; obtaining the remote sensing image of the key time phase, and calculating a plurality of vegetation indexes; respectively constructing regression models of the vegetation indexes and the actually measured yield data; sequencing the goodness of fit of the regression models of the vegetation indexes and the actually measured yield data in the same growth period, and screening out the optimal vegetation indexes of a key time phase; and constructing an estimated yield model based on the optimal vegetation index and the actually measured yield data of the key time phase, and estimating the yield of the crops. The regional crop yield estimation method is based on remote sensing data, combines crop growth characteristics, constructs a regional crop yield estimation model through crop key phenological period analysis and combination of the optimized vegetation index and the measured data, and can be used for rapid regional yield estimation and improve the reliability of yield estimation results.

Description

Method and device for crop yield estimation, electronic equipment and storage medium
Technical Field
The present invention relates to the field of crop production technologies, and in particular, to a method, an apparatus, an electronic device, and a storage medium for crop yield estimation.
Background
The dynamic monitoring of the growth condition of the crops and the timely and accurate prediction of the yield have important significance for the establishment of national food policies, the development of rural economy and foreign food trade. With the development of remote sensing technology, remote sensing data with high time resolution, high spatial resolution and high spectral resolution are continuously emerged, so that the fineness and accuracy of the remote sensing data in crop estimation are continuously improved, and the prediction of crop yield by combining the remote sensing data with statistical data becomes the trend in the field of remote sensing estimation. Remote sensing estimation research is also developed from single crops to multiple crops, from small-area application to large-area application, from single information sources to comprehensive application of multiple remote sensing information sources, and monitoring accuracy is continuously improved.
However, the existing methods for crop assessment have the following disadvantages:
(1) the empirical statistical model based on single parameter modeling is simple in principle, but not high in stability;
(2) the light energy utilization rate model has insufficient consideration on the difference of physiological processes of crops in different growth periods and is strongly interfered by human factors;
(3) the crop growth model has excessive parameters, high operation cost and long parameter localization period, and only single-point crop yield can be simulated and then interpolated to a regional range, so that the regional yield result is not accurate.
(4) The coupling model construction process is complex, the different region parameter obtaining approaches are unstable, and the coupling principle and method are yet to be further researched and perfected.
The modeling image has single time phase, single modeling index, simple and easy model realization but poor model stability, and unsatisfactory estimation result; or too many modeling parameters, difficult acquisition of parameters, high cost operation, complex model, difficult realization, long production estimation period and difficult large-scale popularization are adopted, and the factors limit the further application and development of the regional remote sensing production estimation technology.
Disclosure of Invention
The invention mainly aims to provide a method, a device, an electronic device and a storage medium for crop yield estimation, and aims to solve the technical problem of enabling crop yield estimation to be faster and more accurate.
The purpose of the invention and the technical problem to be solved are realized by adopting the following technical scheme. The invention provides a method for estimating the yield of crops, which comprises the following steps:
determining a key time phase of a remote sensing image for estimating the yield of the crops to be estimated based on the key phenological period of the crops to be estimated;
obtaining the remote sensing image of the key time phase, and calculating a plurality of vegetation indexes of the remote sensing image of the key time phase;
acquiring actual measurement yield data, and respectively constructing regression models of all vegetation indexes and the actual measurement yield data;
sequencing the goodness of fit of the regression models of the vegetation indexes and the actually measured yield data in the same growth period, and screening out the optimal vegetation indexes of a key time phase;
and constructing an estimated yield model based on the optimal vegetation index and the actually measured yield data of the key time phase, and estimating the yield of the crops.
The object of the present invention and the technical problems solved thereby can be further achieved by the following technical measures.
Preferably, the method for estimating crop yield, wherein the determining a key time phase of the remote sensing image for estimating the crop yield based on the key phenological period of the crop to be estimated, comprises:
selecting a research area of crops to be estimated, and determining the phenological period and the phenological characteristics of the crops to be estimated in the research area;
screening out the period of vigorous growth of crops and observation of obvious vegetation information on a remote sensing image based on different growth stages of the crops, and determining a key phenological period for estimating the crop yield;
and determining a key time phase of a remote sensing image for estimating the yield of the crops to be estimated based on the key phenological period of the crop yield estimation.
Preferably, the method for crop yield estimation, wherein the obtaining of the remote sensing image of the key time phase and the calculating of the plurality of vegetation indexes of the remote sensing image of the key time phase include:
after obtaining the remote sensing image of the key time phase of the crop to be estimated, preprocessing the remote sensing image;
and calculating the preprocessed remote sensing image of the key time phase to obtain a plurality of vegetation indexes of the remote sensing image of the key time phase.
Preferably, the method for estimating the yield of the crops, wherein the pretreatment comprises: radiometric calibration, atmospheric correction, geometric fine correction and image registration; wherein, the air correction adopts a FLAASH air correction model.
Preferably, the method for crop yield estimation described above, wherein said plurality of vegetation indices comprises: a green normalized vegetation index, a difference vegetation index, a crop nitrogen response index, a vegetation index for adjusting soil brightness, a vegetation decay index, a ratio vegetation index, a soil adjustment vegetation index, and a structure-enhancing pigment vegetation index.
Preferably, the method for crop yield estimation, wherein obtaining measured yield data and respectively constructing a regression model of each vegetation index and the measured yield data, includes:
checking the actual measurement yield points one by one, and screening out actual measurement yield error points to obtain actual measurement yield data;
and respectively constructing a regression model of each vegetation index of the key time phase and the actually measured yield data.
Preferably, the method for crop yield estimation, wherein the step of ranking the goodness of fit of the regression models of the vegetation indexes and the actually measured yield data in the same growth period and screening out the optimal vegetation index at a key time phase includes:
evaluating the regression models of the vegetation indexes and the actually measured yield data of the crop key time phase respectively by adopting the goodness of fit of the models;
and 3-5 preferable vegetation indexes capable of reflecting the growth state of the crops are screened according to the goodness of fit.
The object of the present invention and the technical problem to be solved are also achieved by the following technical means. According to the present invention, there is provided an apparatus for estimating yield of crops, the apparatus comprising:
the determining unit is used for determining a key time phase of a remote sensing image for estimating the yield of the crops to be estimated based on the key phenological period of the crops to be estimated;
the acquisition unit is used for acquiring the remote sensing image of the key time phase and calculating a plurality of vegetation indexes of the remote sensing image of the key time phase;
the model construction unit is used for acquiring measured yield data and respectively constructing regression models of all vegetation indexes and the measured yield data;
the screening unit is used for sequencing the goodness of fit of the regression models of the vegetation indexes and the actually measured yield data in the same growth period and screening out the optimal vegetation indexes of a key time phase;
and the yield estimation unit is used for constructing a yield estimation model based on the optimal vegetation index and the actually measured yield data of the key time phase and estimating the yield of the crops.
The object of the present invention and the technical problem to be solved are also achieved by the following technical means. An electronic device according to the present invention includes a memory and a processor, wherein the memory stores computer program instructions, and the computer program instructions are read by the processor and executed to perform the steps of any of the methods described above.
The object of the present invention and the technical problem to be solved are also achieved by the following technical means. A storage medium according to the present invention has stored thereon computer program instructions which, when read and executed by a computer, perform the steps of the method of any one of the preceding claims.
By the technical scheme, the method, the device, the electronic equipment and the storage medium for estimating the yield of the crops, provided by the invention, have the following advantages at least:
1. according to the method, the regional crop yield is estimated by analyzing the crop key phenological period and combining the optimized vegetation index and the measured data modeling method. The invention fully considers the relationship between the vegetation index and the crop growth state and the complementary relationship of different vegetation index information, aims to overcome the defects of poor stability of a single parameter experience statistical model, insufficient consideration of difference of a light energy utilization model to physiological processes of crops in different growth periods and strong interference of human factors, overcomes the defects of excessive parameters, high operation cost and longer estimation period of a crop growth model, overcomes the defects of complex construction process, difficult parameter acquisition and limited local application of a coupling model, and aims to ensure that the regional crop yield estimation is more accurate, rapid and convenient, is easy to popularize and use in a large range and has more economic benefit.
2. The regional crop yield estimation method is based on remote sensing data, combines crop growth characteristics, constructs a regional crop yield estimation model by combining crop key phenological period analysis and preferred vegetation indexes and actual measurement data, can be used for rapid regional yield estimation, enables regional crop yield estimation to be rapid and accurate, and is wide in application range, high in precision, low in operation cost, short in yield estimation period and easy to popularize and use in a large range.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
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In order to more clearly explain the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for crop yield assessment according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating a method for crop yield assessment according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an apparatus for crop yield assessment according to an embodiment of the present application;
FIG. 5a is a graph showing a distribution of spring corn yield in Xinjiang county according to an embodiment of the present application;
fig. 5b is a partial enlarged view of a portion a of fig. 5 a.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects of the method, apparatus, electronic device and storage medium for crop yield assessment according to the present invention will be made with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "an embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Referring to fig. 1, an embodiment of the present disclosure provides a schematic structural diagram of an electronic device 100, which may be a personal computer, a tablet computer, a smart phone, a personal digital assistant, and the like.
The electronic apparatus 100 includes: a processor 101, a memory 102, a communication interface 103, and a communication bus for enabling connection communication of these components.
The Memory 102 is used for storing various data such as remote sensing images of crops to be estimated in each period and a computer program instruction corresponding to the crop estimation method and apparatus provided in the embodiment of the present application, wherein the Memory 102 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 101 is configured to execute the steps of the crop yield estimation method provided in the embodiment of the present application when reading and running the computer program instructions stored in the memory, so as to obtain a key time phase remote sensing image of a crop to be estimated from the memory, calculate a plurality of vegetation indexes of a key time phase based on the key time phase remote sensing image, and then respectively construct a regression model of each vegetation index of the key time phase and actual measurement yield data based on the actual measurement yield data; sequencing the goodness of fit of the regression models of the vegetation indexes and the actually measured yield data in the same growth period, and screening out key time phase optimal vegetation indexes; and finally, constructing an estimated yield model based on the key time phase optimal vegetation index and the actually measured yield data, and estimating the yield of the crops.
Further, the processor 101 may be an integrated circuit chip having signal processing capabilities. The Processor 101 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The communication interface 103 may be any transceiver or the like for transmitting the yield estimation result of the crop to a user terminal communicatively connected to the electronic device 100 for display.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for crop yield assessment according to an embodiment of the present application, the method being applied to the electronic device 100 shown in fig. 1, and the flowchart shown in fig. 2 will be described in detail below, and the method includes the following steps:
s100, determining a key time phase of a remote sensing image for estimating the yield of the crop to be estimated based on a key phenological period of the crop to be estimated;
the step S100 specifically includes:
s101, selecting a research area of crops to be estimated, and determining the phenological period and the phenological characteristics of the crops to be estimated in the research area;
s102, screening out periods when crops grow vigorously and obvious vegetation information can be observed on remote sensing images based on different growth stages of the crops, and determining a key phenological period for estimating the crop yield;
s103, determining a key time phase of a remote sensing image used for estimating the yield of the crops to be estimated based on the key phenological period of the crop yield estimation.
In step S100, the phenological period is a reflection of the laws of growth, development, and activity of animals and plants and the change of organisms on the phenological period, and is called the phenological period when such a reflection is generated. Analyzing the phenological period suitable for remote sensing to estimate yield of crops, namely the key phenological period, and taking the vigorous growth stage of plants with typical vegetation spectral characteristics as the key phenological period of crops to be estimated.
The key phenological period in S100 is a phenological period that is closely related to the crop species and has a large influence on the yield of the crop. The crop has several key phenological periods, and different crops have different key phenological periods. According to the embodiment of the application, a more accurate estimated production result can be obtained by adopting the key phenological period, and the calculation complexity is lower.
Specifically, in order to reduce the computational complexity of the yield estimation method on the basis of ensuring the accuracy of the yield estimation result, for each crop to be estimated, the yield of the crop is accurately estimated only through the remote sensing image of the crop in the key phenological stage, for example, the rice can be estimated only by acquiring the data of the rice in the induction stage, heading stage or milk stage, and the rice yield is estimated without acquiring the data of the rice in the seedling stage, tillering stage, wax stage and mature stage. For example, the wheat can be estimated only by acquiring the data of the wheat in the booting stage, heading stage and filling stage, and the data of the wheat in the sowing stage, emergence stage, jointing stage, wintering stage, green turning stage, rising stage (biological jointing), flowering stage and mature stage are not required to be acquired.
In one possible embodiment, S100 may be implemented as follows: the electronic device 100 receives the remote sensing image of the crop to be estimated in the key phenological period, stores the remote sensing image of the phenological period in the memory 102, and acquires the remote sensing image of the crop to be estimated in the key phenological period from the memory 102 when the yield of the crop to be estimated needs to be estimated. In the present embodiment, the crop to be estimated may be, but is not limited to, rice, wheat, corn, soybean, etc.
In another possible embodiment, the remote sensing image of the crop to be estimated in the key phenological period may be a remote sensing image taken by a satellite.
Because the resolution ratio of the remote sensing image obtained by the unmanned aerial vehicle is higher than that of the remote sensing image obtained by the satellite, the unmanned aerial vehicle can be used for obtaining the image data of crops in the embodiment of the application.
S200, obtaining the remote sensing image of the key time phase, and calculating a plurality of vegetation indexes of the remote sensing image of the key time phase;
the step S200 specifically includes:
s201, preprocessing the remote sensing image of the key time phase of the crop to be estimated after acquiring the remote sensing image;
wherein the pre-processing comprises: radiometric calibration, atmospheric correction, geometric fine correction, and image registration.
Further, an FLAASH atmospheric correction model is selected for atmospheric correction, FLAASH is generated based on an MODTRAN4+ radiation transmission model, the scattering effect of water vapor and aerosol during remote sensing imaging is effectively removed, and meanwhile, the adjacent effect of cross radiation of a target pixel and an adjacent pixel is corrected based on pixel-level correction.
S202, calculating the preprocessed remote sensing image of the key time phase to obtain a plurality of vegetation indexes of the remote sensing image of the key time phase.
In this embodiment, the plurality of vegetation indexes of the remote sensing image of the key time phase include, but are not limited to: green normalized vegetation index (GNDVI), normalized vegetation index (NDVI), Differential Vegetation Index (DVI), crop Nitrogen Response Index (NRI), vegetation for soil brightness adjustment index (OSAVI), vegetation decay index (PSRI), Ratio Vegetation Index (RVI), Soil Adjustment Vegetation Index (SAVI) and structure-enhancing pigment vegetation index (SIPI), Greenness Vegetation Index (GVI), vertical vegetation index (PVI), Atmospheric Resistance Vegetation Index (ARVI), greenness sum index (SG).
In some preferred embodiments, the plurality of vegetation indices of the remote-sensed images of the key time phase comprise: green normalized vegetation index (GNDVI), normalized vegetation index (NDVI), Differential Vegetation Index (DVI), crop Nitrogen Response Index (NRI), vegetation index to adjust soil brightness (OSAVI), vegetation decay index (PSRI), Ratio Vegetation Index (RVI), Soil Adjusted Vegetation Index (SAVI), and structure-enhancing pigment vegetation index (SIPI).
The vegetation indexes selected above can reflect the growth and development state of crops, and have typicality and representativeness.
And calculating a plurality of vegetation indexes of the key time phase according to the blue wave band, the green wave band, the red wave band and the near infrared wave band of the remote sensing image of the key time phase.
The calculation formulas of the green normalized vegetation index (GNDVI), the normalized vegetation index (NDVI), the Difference Vegetation Index (DVI), the crop Nitrogen Response Index (NRI), the vegetation index for adjusting soil brightness (OSAVI), the vegetation decay index (PSRI), the Ratio Vegetation Index (RVI), the Soil Adjusted Vegetation Index (SAVI) and the structure strengthening pigment vegetation index (SIPI) are respectively shown in the following formulas (1) to (9):
GNDVI＝(B4-B2)/(B4+B2) (1)
NDVI＝(B4-B2)/(B4+B2) (2)
DVI＝(B4-B3)/(B4+B3) (3)
NRI＝(B2-B3)/(B2+B3) (4)
OSAVI＝(1+0.16)×B4-B3)/(B4+B3+0.16) (5)
PSRI＝(B3-B1)/B4 (6)
RVI＝B4/B3 (7)
SAVI＝(B4-B3)/(B4+B3+0.5)×(1+0.5) (8)
SIPI＝(B4+B1)/(B4-B1) (9)
wherein, B1, B2, B3 and B4 respectively represent a blue wave band, a green wave band, a red wave band and a near infrared wave band of the remote sensing image.
S300, acquiring measured yield data, and respectively constructing regression models of the vegetation indexes and the measured yield data;
the step S300 specifically includes:
s301, checking the actually measured yield points one by one, and screening out actually measured yield error points to obtain actually measured yield data;
in this step, the actual measurement yield refers to that the sampled plot of the crop to be estimated is uniformly selected in the area to be estimated and actual measurement is performed, so as to obtain the actual yield of the sampled plot, i.e. the actual measurement yield. Because errors, such as yield recording errors, positioning longitude and latitude deviation and the like, exist in the actual cutting and actual measuring process, the actual measuring yield with abnormal values and abnormal positioning is screened out. When the actual measurement yield points are inspected one by one, the actual measurement points are overlapped with the remote sensing images and the spatial distribution data of the plots, and if the actual measurement points are not located in the corn plots, such as on the road or other types of plots, the actual measurement points are screened out.
S302, respectively constructing regression models of all vegetation indexes of key time phases and the actually measured yield data.
The regression model of each multiple vegetation index and actual measurement yield data of the crop key time phase is as follows:
Y＝agndviXgndvi+Zgndvi (10)
Y＝andviXndvi+Zndvi (11)
Y＝adviXdvi+Zdvi (12)
Y＝anriXnri+Znri (13)
Y＝aosaviXosavi+Zosavi (14)
Y＝apsriXpsri+Zpsri (15)
Y＝arvXrvi+Zrvi (16)
Y＝asaviXsavi+Zsavi (17)
Y＝asipiXsipi+Zsipi (18)
wherein Y represents actual point yield data, agndvi,…,asipiRepresenting the coefficients of a regression equation, Xgndvi,…,XsipiRepresenting individual vegetation index data, Zgndvi,…,ZsipiRepresenting an error term.
S400, sorting the goodness of fit of the regression models of the vegetation indexes and the actually measured yield data in the same growth period, and screening out the optimal vegetation indexes of a key time phase;
goodness of fit (R) using models2) Respectively evaluating regression models of crop key time phase vegetation indexes and crop yield;
Figure BDA0003262968680000111
wherein, yiAnd
Figure BDA0003262968680000112
measured yield and estimated yield, respectively, n is the sample size,
Figure BDA0003262968680000113
is the average value of the measured yield.
Further, screening out 3-5 preferable vegetation indexes which can reflect the growth state of crops in the crop key time phase according to the fitting goodness;
considering that the stability of the estimated yield model with a single parameter is poor, and too many parameters may introduce a vegetation index with small correlation with the measured yield, 3 to 5 vegetation indexes are preferably used as the preferable vegetation index in the embodiment.
In particular, in terms of goodness of fit (R)2) The vegetation indexes are arranged from big to small, and the vegetation indexes corresponding to the first 3-5 positions are selected as the preferable vegetation indexes of the key time phase. For example, when the top 5 bits are selected as the preferred vegetation Index, Index1, Index2, Index3, Index4, and Index5, respectively; when the first 4 bits are selected as the preferred vegetation Index, Index1, Index2, Index3 and Index4, respectively; when the first 3 bits are selected as the preferred vegetation Index, Index1, Index2, and Index3, respectively.
Further, the preferred vegetation Index for the first 3 bits as key phase is selected as Index1, Index2, Index3, respectively.
These indices have high correlation with yield and can reflect the growth state and yield change of crops well, so Index1, Index2 and Index3 are preferable vegetation indices.
S500, constructing an estimated yield model based on the optimal vegetation index and the actually measured yield data of the key time phase, and estimating the yield of the crops.
Specifically, an estimated yield model of the crop key time phase preferred vegetation Index is constructed by using the obtained Index1, Index2 and Index3, and is represented by a formula (20);
Yield＝b1Index1+b2Index2+b3Index3+Z (20)
where Yield represents the final Yield of the model simulation, b1、b2、b3Represents the contribution coefficient of the preferred vegetation Index, Index1, Index2, Index3 represent the preferred vegetation Index of the crop key phase, and Z represents the error term.
The yield distribution result of regional crops can be rapidly calculated by using the obtained estimated yield model based on the key time phase optimal vegetation index, and the result is mapped and output.
Referring to fig. 3, a detailed flowchart of a crop yield assessment method according to an embodiment of the present disclosure is provided, wherein the method includes:
the first stage is as follows: determining a key time phase of a remote sensing image for estimating the yield of crops to be estimated, and acquiring the key time phase remote sensing image; the key time phase remote sensing image needs to have a blue band, a green band, a red band and a near infrared band.
In the second stage, calculating a plurality of vegetation indexes of the remote sensing image of the key time phase based on the remote sensing image of the key time phase; wherein, the plurality of vegetation indexes are respectively green normalized vegetation index (GNDVI), normalized vegetation index (NDVI), Difference Vegetation Index (DVI), crop Nitrogen Response Index (NRI), vegetation index for adjusting soil brightness (OSAVI), vegetation decay index (PSRI), Ratio Vegetation Index (RVI), Soil Adjusted Vegetation Index (SAVI) and structure strengthening pigment vegetation index (SIPI) which are respectively shown in formulas (1) to (9);
the fourth stage, acquiring measured yield data, and respectively constructing regression models of each vegetation index and the measured yield data; and goodness of fit (R) to regression models of individual vegetation indices and measured yield data over the same growth period2) Sorting is carried out, and the optimal vegetation index of the key time phase is screened out; the preferred vegetation Index for the first 3 positions as key phases are Index1, Index2, Index3, respectively.
And in the fifth stage, constructing an estimation model based on the key time phase optimal vegetation index and the actually measured yield data, and estimating the yield of crops to be estimated in the area. And (3) constructing an estimated yield model of the key phase preferable vegetation Index of the crops by using the obtained Index1, Index2 and Index3, and determining the yield of the crops as shown in a formula (20).
Referring to fig. 4, fig. 4 is a block diagram illustrating an apparatus 400 for crop assessment according to an embodiment of the present disclosure. The block diagram of fig. 4 will be explained, and the apparatus shown comprises:
the determining unit 410 is used for determining a key time phase of a remote sensing image for estimating the yield of the crop to be estimated based on the key phenological period of the crop to be estimated;
an obtaining unit 420, configured to obtain the remote sensing image at the key time phase, and calculate a plurality of vegetation indexes of the remote sensing image at the key time phase;
the model construction unit 430 is configured to obtain measured yield data, and respectively construct regression models of each vegetation index and the measured yield data;
the screening unit 440 is used for sorting the goodness of fit of the regression models of the vegetation indexes and the actually measured yield data in the same growth period, and screening out the optimal vegetation indexes of a key time phase;
and the yield estimation unit 450 is configured to construct a yield estimation model based on the optimal vegetation index and the actually measured yield data of the key time phase, and estimate the yield of the crop.
For the process of implementing each function by each functional unit in this embodiment, please refer to the content described in the embodiment shown in fig. 2, which is not described herein again.
In addition, the present application embodiment also provides a storage medium, in which a computer program is stored, and when the computer program runs on a computer, the computer is caused to execute the method provided by any one of the embodiments of the present application.
In summary, embodiments of the present application provide a method, an apparatus, an electronic device, and a storage medium for crop yield estimation, where the method includes: determining a key time phase remote sensing image of the crop to be estimated based on a key phenological period of the crop to be estimated; obtaining the key time phase remote sensing image, and determining a plurality of vegetation indexes of the key time phase; respectively constructing regression models of all vegetation indexes of key time phases based on the actually measured yield data; sequencing the fitting goodness of the regression models of the yield and each vegetation index in the same growth period, and screening out a key time phase optimal vegetation index; and constructing an estimated yield model based on the key time phase optimal vegetation index and the actually measured yield data, and estimating the yield of the crops.
The regional crop yield estimation method is based on remote sensing data, combines crop growth characteristics, constructs a regional crop yield estimation model by combining crop key phenological period analysis and preferred vegetation indexes and actual measurement data, can be used for rapid regional yield estimation, enables regional crop yield estimation to be rapid and accurate, and is wide in application range, high in precision, low in operation cost, short in yield estimation period and easy to popularize and use in a large range.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.
The present invention will be further described with reference to the following specific examples, which should not be construed as limiting the scope of the invention, but rather as providing those skilled in the art with certain insubstantial modifications and adaptations of the invention based on the teachings of the invention set forth herein.
The present invention will be described in detail below with reference to the drawings and the following embodiments by taking an example of Xinjiang Uygur autonomous region.
Examples
A spring corn rapid yield estimation method based on an optimal vegetation index specifically comprises the following steps:
step one, combining spectral characteristic analysis of the county spring corn, determining a key phenological period of spring corn yield estimation, wherein phenological period analysis is shown in table 1.
TABLE 1 spring maize phenological period
Figure BDA0003262968680000141
As can be seen from table 1, the county spring corn is sown in late 4 months, seedlings emerge in late 5 months, the spectral feature is bare land information, and the vigorous growth stage begins at the jointing stage of 6 months, and accords with the spectral feature of vegetation; the blooming period and the maturity period from the middle 7 th to the last 7 th of the month belong to the vigorous vegetation growth stage, and have typical vegetation spectral characteristics; the vegetation from the early 8 to 9 months of age enters the mature period, which also belongs to the vigorous growth stage and has the typical spectral characteristics of vegetation. In summary, the key phenological period for estimating the yield of spring corn is from late 7 to early 9 months.
Therefore, the key time for the county spring corn to estimate the remote sensing data is finally determined to be 7 months and 30 days by combining the spectral feature analysis and the regional phenological analysis of the spring corn and the availability of the remote sensing data.
Acquiring a key time phase remote sensing image and preprocessing the key time phase remote sensing image;
in this embodiment, a satellite image of the county HJ-1 of 7 months and 30 days is acquired for preprocessing, the multispectral image wave bands are four wave bands of near infrared, red, green and blue, and after the image is subjected to radiometric calibration and FLAASH atmospheric correction preprocessing, a Landsat/TM 30 m orthographic image is used as a reference image for geometric fine correction, the correction precision is 0.5-1 pixel of a plain area, and the accuracy of the geometric position of the image is ensured.
Step three, calculating various vegetation indexes capable of reflecting the growth and development states of crops for the preprocessed remote sensing images;
calculating vegetation indexes capable of reflecting the growth and development states of the spring corn on the preprocessed image, wherein the vegetation indexes are as follows: the vegetation index of the soil conditioner is characterized by comprising a green normalized vegetation index (GNDVI), a normalized vegetation index (NDVI), a Difference Vegetation Index (DVI), a crop Nitrogen Response Index (NRI), a vegetation index for adjusting soil brightness (OSAVI), a vegetation decay index (PSRI), a Ratio Vegetation Index (RVI), a Soil Adjustment Vegetation Index (SAVI) and a structure-enhancing pigment vegetation index (SIPI), wherein the calculation formulas are shown in (1) to (9).
Step four, constructing a key time phase vegetation index optimal selection model of the spring corn;
(1) and checking and screening the yield data of the actual measuring points, analyzing the reasonability of data distribution, the correctness of crop varieties and the like, and rejecting unreasonable data.
(2) And constructing a regression model of the key time phase vegetation index and the actually measured yield of the spring corn.
And establishing a regression model for the 9 vegetation indexes and the actually measured yield which are respectively calculated by the key time phase images. Goodness of fit R to yield and each exponential model in the same growth period2Sorting is performed when the goodness of fit (R) is satisfied2) And the model input vegetation index when reaching the 3 bits before the maximum value of the period is the optimal vegetation index of the key time phase of the spring corn. Preferred vegetation indices for this period are NRI, RVI, SIPI;
step five, constructing a regression model by using the key time phase optimal vegetation index and the actually measured data, obtaining crop yield data of the whole area and drawing, wherein the obtained final estimated yield model is as follows:
Y＝863.7439NRI+372.645RVI+1398.564PSRI+779.231 (21)
the root mean square error of the model is 18.93, and the regression coefficient of the model is 0.74, which indicates that the model has high precision and high reliability.
The county spring corn yield area distribution graph is obtained through an estimation model, the county spring corn specific yield distribution is shown in fig. 5a and 5b, and the average estimation yield of the county spring corn in 2019 is 861.39 kg/mu.
According to the crop yield estimation method, the estimation of the crop yield in a large area can be realized only by using the remote sensing image and the actually measured yield data, and the data is easy to obtain, strong in operability, fast and convenient.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A method of estimating crop yield, comprising the steps of:
determining a key time phase of a remote sensing image for estimating the yield of the crops to be estimated based on the key phenological period of the crops to be estimated;
obtaining the remote sensing image of the key time phase, and calculating a plurality of vegetation indexes of the remote sensing image of the key time phase;
acquiring actual measurement yield data, and respectively constructing regression models of all vegetation indexes and the actual measurement yield data;
sequencing the goodness of fit of the regression models of the vegetation indexes and the actually measured yield data in the same growth period, and screening out the optimal vegetation indexes of a key time phase;
and constructing an estimated yield model based on the optimal vegetation index and the actually measured yield data of the key time phase, and estimating the yield of the crops.
2. The method of crop yield assessment according to claim 1, wherein determining a key time phase of a remote sensing image for crop yield assessment based on a key phenological period of a crop to be assessed comprises:
selecting a research area of crops to be estimated, and determining the phenological period and the phenological characteristics of the crops to be estimated in the research area;
screening out the period of vigorous growth of crops and observation of obvious vegetation information on a remote sensing image based on different growth stages of the crops, and determining a key phenological period for estimating the crop yield;
and determining a key time phase of a remote sensing image for estimating the yield of the crops to be estimated based on the key phenological period of the crop yield estimation.
3. The method of claim 1, wherein obtaining the remote-sensed image of the key time phase and calculating the plurality of vegetation indices of the remote-sensed image of the key time phase comprises:
after obtaining the remote sensing image of the key time phase of the crop to be estimated, preprocessing the remote sensing image;
and calculating the preprocessed remote sensing image of the key time phase to obtain a plurality of vegetation indexes of the remote sensing image of the key time phase.
4. The method of crop yield assessment according to claim 3, wherein said pre-treatment comprises: radiometric calibration, atmospheric correction, geometric fine correction and image registration; wherein, the air correction adopts a FLAASH air correction model.
5. The method of crop yield estimation according to claim 3, wherein the plurality of vegetation indices includes: a green normalized vegetation index, a difference vegetation index, a crop nitrogen response index, a vegetation index for adjusting soil brightness, a vegetation decay index, a ratio vegetation index, a soil adjustment vegetation index, and a structure-enhancing pigment vegetation index.
6. The method of claim 1, wherein obtaining measured yield data and constructing a regression model of each vegetation index and the measured yield data, respectively, comprises:
checking the actual measurement yield points one by one, and screening out actual measurement yield error points to obtain actual measurement yield data;
and respectively constructing a regression model of each vegetation index of the key time phase and the actually measured yield data.
7. The method of crop yield estimation according to claim 1, wherein ranking goodness of fit of regression models of individual vegetation indices and measured yield data over the same growth period and screening out the preferred vegetation index for the key time phase comprises:
evaluating the regression models of the vegetation indexes and the actually measured yield data of the crop key time phase respectively by adopting the goodness of fit of the models;
and 3-5 preferable vegetation indexes capable of reflecting the growth state of the crops are screened according to the goodness of fit.
8. An apparatus for crop assessment, the apparatus comprising:
the determining unit is used for determining a key time phase of a remote sensing image for estimating the yield of the crops to be estimated based on the key phenological period of the crops to be estimated;
the acquisition unit is used for acquiring the remote sensing image of the key time phase and calculating a plurality of vegetation indexes of the remote sensing image of the key time phase;
the model construction unit is used for acquiring measured yield data and respectively constructing regression models of all vegetation indexes and the measured yield data;
the screening unit is used for sequencing the goodness of fit of the regression models of the vegetation indexes and the actually measured yield data in the same growth period and screening out the optimal vegetation indexes of a key time phase;
and the yield estimation unit is used for constructing a yield estimation model based on the optimal vegetation index and the actually measured yield data of the key time phase and estimating the yield of the crops.
9. An electronic device comprising a memory and a processor, the memory having stored therein computer program instructions which, when read and executed by the processor, perform the steps of the method of any one of claims 1-7.
10. A storage medium having stored thereon computer program instructions which, when read and executed by a computer, perform the steps of the method according to any one of claims 1-7.
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