CN114219847B - Method and system for determining crop planting area based on phenological characteristics and storage medium - Google Patents
Method and system for determining crop planting area based on phenological characteristics and storage medium Download PDFInfo
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Abstract
The invention provides a method, a system and a storage medium for determining a crop planting area based on a phenological characteristic, belonging to the technical field of computers, and the method comprises the steps of collecting remote sensing image data of a crop planting area to be detected according to set time; determining the vegetation index statistical characteristics and the vegetation phenological characteristics of the crop planting area to be detected according to the remote sensing image data; determining a crop planting area characteristic vector of the crop planting area to be detected according to the statistical feature of the vegetation index and the phenological feature of the vegetation; inputting the characteristic vector of the crop planting area into a pre-trained crop planting area extraction model based on a random forest, and performing remote sensing extraction to determine a crop planting area distribution diagram of the crop planting area to be detected; and analyzing the distribution diagram of the crop planting area to determine the planting area data of the crop planting area to be detected. The invention has the technical effects of low cost, high efficiency and capability of finely depicting the spatial distribution of the planting area.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a method and a system for determining a crop planting area based on phenological characteristics and a storage medium.
Background
The traditional method for acquiring the crop planting area relies on field investigation and statistical reporting, and the investigation mode is high in cost and low in efficiency and does not have the capability of describing the spatial distribution of the planting area in detail. The phenological condition refers to the periodic changes of the growth and development of the organism, such as the phenomena of plant germination, leaf expansion, flowering, leaf falling and the like, and different types of crops show different phenological stages and characteristics. The acquisition of the crop phenological stage and characteristic information cannot be met by depending on field investigation and statistical reporting.
In the prior art, the remote sensing technology is applied to the field of crop planting area acquisition because of the characteristics of wide coverage range, fast data acquisition, dynamic ground observation and the like. Remote sensing technology fully excavates differences of spectra, phenological phenomena and textures of crops and other surface cover types, and achieves classification between certain crops and other crops or non-crops by means of expert knowledge, machine learning and deep learning algorithms. However, the existing remote sensing technology for acquiring the crop planting area has the following disadvantages: because the crops have various varieties and belong to the vegetation, the spectral difference among the vegetation varieties is not obvious under the influence of factors such as meteorological conditions, farmland management measures and the like; the crops with the same object, different spectrum and the same foreign object spectrum can not be identified by the remote sensing technology.
Therefore, there is a need for a method of determining the planting area of a crop based on the phenological characteristics that can be used for crop classification.
Disclosure of Invention
The invention provides a method, a system, electronic equipment and a storage medium for determining a crop planting area based on a phenological characteristic, which are used for overcoming at least one technical problem in the prior art.
In order to achieve the purpose, the invention provides a crop planting area determining method based on the phenological characteristics, which comprises the following steps:
collecting remote sensing image data of a crop planting area to be detected according to set time;
determining the vegetation index statistical characteristics and the vegetation phenological characteristics of the crop planting area to be detected according to the remote sensing image data;
determining a crop planting area characteristic vector of the crop planting area to be detected according to the vegetation index statistical characteristic and the vegetation phenological characteristic;
inputting the characteristic vector of the crop planting area into a pre-trained crop planting area extraction model based on a random forest, and performing remote sensing extraction to determine a crop planting area distribution diagram of the crop planting area to be detected;
and analyzing the distribution diagram of the crop planting area to determine the planting area data of the crop planting area to be detected.
Further, preferably, the training method for extracting the model based on the crop planting area of the random forest comprises the following steps,
acquiring a preprocessed crop planting data set; wherein 30% of sample points in the crop planting data set are used as a test set, and 70% of sample points in the crop planting data set are used as a training set;
training a random forest-based crop planting area extraction pre-model by utilizing a training set and a pre-acquired classification characteristic image set;
stopping training until the precision evaluation index and the out-of-package error estimation value reach preset standards, and obtaining a crop planting area extraction model based on random forests;
the precision evaluation index is obtained by performing precision inspection on a crop planting area extraction pre-model based on a random forest by using a confusion matrix generated by a test set.
Further, preferably, the method for preprocessing the crop planting data set comprises,
obtaining sample point data of the crop planting data set according to the year;
performing sample time migration on the sample point data;
screening sample point data after sample time migration by using a spectrum angle, and acquiring the sample point data with the spectrum angle meeting a set standard as sample data which does not change remarkably between the years;
and forming a crop planting sample library by the acquired sample data which does not change remarkably between the years, and finishing the pretreatment of the crop planting data set.
Further, preferably, the method for acquiring the classification feature image set includes,
acquiring remote sensing image data in a set space and time;
acquiring vegetation indexes of the remote sensing image data according to a set waveband, and acquiring statistical characteristics of the vegetation indexes according to the vegetation indexes; wherein the vegetation index comprises a normalized vegetation index, an enhanced vegetation index, a green chlorophyll vegetation index, a surface moisture index, a normalized differential senescence vegetation index and a normalized tillage index;
fitting the time sequence of the enhanced vegetation index through a linear harmonic model and a double Logistic model respectively to obtain vegetation phenological characteristics;
and synthesizing the vegetation index statistical characteristics and the vegetation phenological characteristics to obtain a classification characteristic image set.
Further, preferably, the time series of enhanced vegetation indexes is fitted by a linear harmonic model, which is implemented by the following formula:
wherein the content of the first and second substances,f(t)is as followstAn enhanced vegetation index value for the day fit,ais a constant term and is a constant number,bis the coefficient of the first-order term,Mthe number of the harmonic wave combinations is,c、dthe coefficients are respectively cosine function and sine function;ωis the reciprocal of the number of days in a year,tthe number of days is the number of days,eis the residual value.
Further, preferably, the time series of the enhanced vegetation index is fitted by a double Logistic model, and is realized by the following formula:
wherein the content of the first and second substances,f(t)is as followstA day-fitted enhanced vegetation index value;v 1 andv 2 respectively an annual background value and an amplitude value of the enhanced vegetation index;m 1 、n 1 、m 2 andn 2 the parameters are pair parameters captured in the variation trend of vegetation growth period and aging period.
Further, preferably, bym 1 Andn 1 acquiring the phenological characteristics at the SOS node; wherein the SOS node is a time point when the derivative of the enhanced vegetation index time sequence reaches the maximum value;
obtaining a phenological characteristic at the EOS node through m2 and n 2; and the EOS node is a time point when the derivative of the enhanced vegetation index time sequence reaches the minimum value.
In order to solve the above problems, the present invention further provides a system for determining a crop planting area based on a phenological characteristic, including:
the acquisition unit is used for acquiring remote sensing image data of a crop planting area to be detected according to set time;
the crop planting area characteristic vector acquisition unit is used for determining the vegetation index statistical characteristic and the vegetation phenological characteristic of the crop planting area to be detected according to the remote sensing image data; determining a crop planting area characteristic vector of the crop planting area to be detected according to the statistical feature of the vegetation index and the phenological feature of the vegetation;
the planting area data acquisition unit is used for inputting the characteristic vector of the crop planting area into a pre-trained random forest-based crop planting area extraction model and performing remote sensing extraction to determine a crop planting area distribution diagram of the crop planting area to be detected; and analyzing the distribution diagram of the crop planting area to determine the planting area data of the crop planting area to be detected.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one instruction; and
and the processor executes the instructions stored in the memory to realize the steps of the method for determining the crop planting area based on the climatic characteristics.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above method for determining a crop planting area based on a phenological characteristic.
According to the method, the system, the electronic equipment and the storage medium for determining the crop planting area based on the phenological characteristics, the crop planting areas are classified based on a remote sensing technology, a random forest-based crop planting area extraction model is established based on the phenological characteristics, and the difference between the types of crops is fully excavated; the method has the technical effects of low cost, high efficiency and capability of finely depicting the spatial distribution of the planting area.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for determining a crop planting area based on a phenological characteristic according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a method for determining a crop planting area based on a climatic characteristic according to an embodiment of the present invention;
FIG. 3 is a block diagram of a system for determining crop planting area based on climatic characteristics according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an internal structure of an electronic device for implementing a method for determining a crop planting area based on a phenological characteristic according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, a schematic flow chart of a method for determining a crop planting area based on a phenological characteristic according to an embodiment of the present invention is shown. The method may be performed by a system, which may be implemented by software and/or hardware.
In the embodiment, the method for determining the crop planting area based on the phenological characteristics comprises the steps S110-S150. S110, collecting remote sensing image data of a crop planting area to be detected according to set time; s120, determining the vegetation index statistical characteristics and the vegetation phenological characteristics of the crop planting area to be detected according to the remote sensing image data; s130, determining a crop planting area characteristic vector of the crop planting area to be detected according to the statistical feature of the vegetation index and the phenological feature of the vegetation; s140, inputting the characteristic vector of the crop planting area into a pre-trained random forest-based crop planting area extraction model, and performing remote sensing extraction to determine a crop planting area distribution diagram of the crop planting area to be detected; s150, analyzing the distribution diagram of the crop planting area to determine the planting area data of the crop planting area to be detected.
S110, collecting remote sensing image data of the crop planting area to be detected according to set time. In a specific implementation process, the acquisition of the remote sensing image data can be realized by an instruction transmitter and a data receiver on the ground, an instruction receiver and a remote sensing image data acquisition device arranged on a satellite, a processor and the like. A memory may also be included that is disposed on the ground. The remote sensing image data acquisition equipment is used for acquiring remote sensing image data of the crop planting area in the observation range of the remote sensing image data acquisition equipment according to the observation range of the remote sensing image data acquisition equipment.
And S120, determining the vegetation index statistical characteristics and the vegetation phenological characteristics of the crop planting area to be detected according to the remote sensing image data.
The method for acquiring the vegetation index statistical characteristics comprises the steps of acquiring the vegetation index of remote sensing image data according to a set waveband, and acquiring the vegetation index statistical characteristics according to the vegetation index; wherein the vegetation index comprises a normalized vegetation index, an enhanced vegetation index, a green chlorophyll vegetation index, a surface moisture index, a normalized differential senescence vegetation index, and a normalized tillage index. And fitting the time sequence of the enhanced vegetation index through a linear harmonic model and a double Logistic model respectively to obtain the vegetation phenological characteristics.
Fig. 2 is a schematic diagram illustrating a principle of a method for determining a crop planting area based on a climatic characteristic according to an embodiment of the present invention. As shown in the figure 2 of the drawings,
specifically, the remote sensing image data are processed by a cloud detection tool to generate a cloud mask file and a cultivated land mask, and then vegetation indexes are obtained according to a set waveband.
It should be noted that the cloud detection tool may be, but is not limited to, an ENVI5.3.1 newly-added cloud automatic detection tool, supports Landsat4-5 TM, Landsat7 ETM +, Landsat8 OLI/TIRS and NPP VIIRS sensor data, and can generate a cloud mask file using fmask3.2 algorithm. An atmospheric apparent reflectivity image of a multispectral wave band needs to be input, and the bright and rolling cloud wave band atmospheric apparent reflectivity image is selectable input. Near Infrared spectroscopy (NIR) is a modern analysis technique with high efficiency and rapidness, and it comprehensively uses the latest research results of multiple subjects such as computer technology, spectroscopy and chemometrics, and is increasingly widely applied in multiple fields with its unique advantages.
The Vegetation Index includes a Normalized Difference Vegetation Index (NDVI), an Enhanced Vegetation Index (EVI), a Green Chlorophyll Vegetation Index (GCVI), a Surface moisture Index (Land Surface Water Index, LSWI), a Normalized Differential Senescence Vegetation Index (NDSVI), and a Normalized Tillage Index (NDTI).
Specifically, the normalized vegetation index is one of the common indicators for monitoring the growth status of vegetation. The normalized vegetation index is obtained by the following formula:
NDVI=(B4-B3)/(B4+B3)
wherein, B3 and B4 are respectively the land surface reflectivity of Landsat red light and near infrared band.
An enhanced vegetation index is one that attenuates the effects of atmospheric and soil background on vegetation signals. The enhanced vegetation index is obtained by the following formula:
EVI=G×(B4-B3)/(B4+C1×B3-C2×B1+L)
wherein, B1, B3 and B4 are respectively the land surface reflectivity of Landsat blue light, red light and near infrared wave bands, L is a canopy background adjusting parameter, C1 and C2 are aerosol correction coefficients, and G is a gain factor. L, C1, C2 and G have empirical values of 1, 6, 7.5 and 2.5, respectively.
The green chlorophyll vegetation Index reflects a greater dynamic range than the normalized vegetation Index in the case of dense canopy (higher Leaf Area Index (LAI)). The green chlorophyll vegetation index is obtained by the following formula: GCVI = B4/B2-1
Wherein, B2 and B4 are the earth surface reflectivity of green light and near infrared wave band respectively.
The surface moisture index is sensitive to vegetation canopy water content and soil background changes. The surface moisture index is obtained by the following formula: LSWI = (B4-B5)/(B4+ B5)
Wherein, B4 and B5 are the earth surface reflectivity of Near Infrared (NIR) and short wave infrared 1 (SWIR 1) wave bands respectively.
The normalized differential senescence vegetation index is specifically responsive to the moisture content of different crop types. The normalized differential senescence vegetation index is obtained by the following formula: NDSVI = (B5-B3)/(B5+ B3)
Wherein, B3 and B5 are the earth surface reflectivity of red light and short wave infrared 1 (SWIR 1) wave bands respectively.
The normalized tillage index is related to stubble coverage of the crop. The normalized farming index is obtained by the following formula:
NDTI=(B5-B7)/(B5+B7)
wherein, B5 and B7 are the earth surface reflectances of short wave infrared 1 (SWIR 1) and short wave infrared 2 (SWIR 2) wave bands respectively.
S130, determining a crop planting area characteristic vector of the crop planting area to be detected according to the vegetation index statistical characteristic and the vegetation phenological characteristic.
S140, inputting the characteristic vector of the crop planting area into a pre-trained random forest-based crop planting area extraction model, and performing remote sensing extraction to determine a crop planting area distribution diagram of the crop planting area to be detected.
In a specific implementation process, training of a random forest classifier, namely training of a random forest extraction model based on a random forest requires a crop planting data set and a classification feature image set as input, and two parameters need to be defined: number of desired decision trees (k) And the number of features required for each node to splitm). Here, the number of decision trees (k) Set to 100, the number of features required per node for splitting: (m) The square root of the number of input features for the default setting is used.
The training method for the random forest based crop planting area extraction model comprises S141-S143.
S141, acquiring a preprocessed crop planting data set; wherein, 30% of sample points in the crop planting data set are used as a test set, and 70% of sample points in the crop planting data set are used as a training set.
In a specific implementation process, the sample set cannot meet the requirement of the time range, and the availability of the sample after time shift can be measured by a spectral angle method. 70% of the crop planting data set was used to train the classifier, and the remaining 30% was used to test the performance of the classifier.
Specifically, the method for preprocessing the crop planting data set includes, S1411, obtaining sample point data of the crop planting data set according to year; s1412, performing sample time migration on the sample point data; s1413, screening the sample point data after the sample time migration by using a spectrum angle, and acquiring sample point data with the spectrum angle meeting a set standard as sample data which does not change remarkably between the years; and S1414, forming the acquired sample data which does not change remarkably between the years into a crop planting sample library, and finishing the pretreatment of the crop planting data set.
And S142, training a random forest-based crop planting area extraction pre-model by utilizing the training set and the pre-acquired classification characteristic image set.
The pre-acquisition method of the classification characteristic image set comprises S1421, acquiring remote sensing image data in a set space and time. S1422, vegetation index acquisition is carried out on the remote sensing image data according to a set waveband, and vegetation index statistical characteristics are acquired according to the vegetation index; wherein the vegetation index comprises a normalized vegetation index, an enhanced vegetation index, a green chlorophyll vegetation index, a surface moisture index, a normalized differential senescence vegetation index, and a normalized tillage index. And S1423, fitting the time sequence of the enhanced vegetation index through a linear harmonic model and a double Logistic model respectively to obtain vegetation phenological characteristics.
That is, the Enhanced Vegetation Index (EVI) time series calculated in step S1422 is fitted by a phenological model: fitting was performed using a linear harmonic model, a dual Logistic model, respectively.
In particular, the time series of enhanced vegetation indices are fitted through a linear harmonic model that uses a series of sine and cosine wave combinations to fit complex signals, and there are complex variants of this model that can accommodate more diverse situations.
The method is realized by the following formula:
wherein the content of the first and second substances,f(t)is as followstAn enhanced vegetation index value for the day fit,ais a constant term and is a constant number,bis the coefficient of the first-order term,Mthe number of the harmonic wave combinations is,c、dthe coefficients are respectively cosine function and sine function;ωis one year in the middle and topThe inverse of the number of the first and second,tthe number of days is the number of days,eis the residual value. It should be noted that, in particular implementations,M=2。ω=1/365。
fitting the time sequence of the enhanced vegetation index through a double Logistic model, wherein the double Logistic model has the following characteristics: 1. and (2) fitting the growth and aging stages of the vegetation by using different functions, wherein the physical significance of the extracted parameters is related to the growth and aging of the vegetation. The method is realized by the following formula:
wherein the content of the first and second substances,f(t)is as followstA day-fitted enhanced vegetation index value;v 1 andv 2 respectively an annual background value and an amplitude value of the enhanced vegetation index;m 1 、n 1 、m 2 andn 2 the parameters are pair parameters captured in the variation trend of vegetation growth period and aging period. In particular, the present invention relates to a method for producing,n 1 andn 2 the date of the s-shaped curve on which the growth-phase EVI increase rate was the greatest and the aging-phase EVI decrease rate was the greatest,m 1 andm 2 then it isn 1 Andn 2 the rate of change of (c).
By passingm 1 Andn 1 obtaining a phenological feature at the SOS node; wherein, the SOS (Start of search) node is a time point when the derivative of the enhanced vegetation index time sequence reaches the maximum value; by passingm 2 Andn 2 obtaining the phenological characteristics at an EOS (end of reason) node; and the EOS node is a time point when the derivative of the enhanced vegetation index time sequence reaches the minimum value.
Specifically, a threshold-based extraction method is used to extract the objective features from the linear harmonic model, and the amplitudes are defined as a two-dimensional vector [ c ]M, dM]While the phase passesComputing a two-dimensional vector [ c ]M, dM]The angle formed.
And extracting the phenological features from the double Logistic model by adopting a derivative-based extraction method. SOS is defined as the point in time at which the EVI time series derivative reaches a maximum value, and EOS is defined as the point in time at which the EVI time series derivative reaches a minimum value.m 1 Andn 1 、m 2 andn 2 these two pairs of parameters, used to capture the growth and senescence stages of the crop respectively, differ:m 1 andn 1 the phenological features at the SOS nodes are extracted and obtained,m 2 andn 2 extracting and obtaining the phenological characteristics of the EOS; whilem 1 Andm 2 as a slope at a time node, ofn 1 Andn 2 is used to represent the number of days of the date of the current time node.
And S1424, synthesizing the vegetation index statistical characteristics and the vegetation phenological characteristics to obtain a classification characteristic image set.
Specifically, the statistical features of the vegetation index are extracted from the vegetation index and the time sequence of the partial wave band, namely, the standard deviation, 5% quantile, 25% quantile, median, 75% quantile and 95% quantile of the year-round NDVI, EVI, GCVI, LSWI, NDSVI, NDTI, SWIR1, SWIR2 and NIR time sequence are calculated. And finally, superposing and synthesizing the statistical characteristics and the phenological characteristics to obtain a classification characteristic image set.
S143, stopping training until the precision evaluation index and the out-of-package error estimation value reach preset standards, and obtaining a crop planting area extraction model based on a random forest; the precision evaluation index is obtained by performing precision inspection on a random forest based crop planting area extraction pre-model by using a confusion matrix generated by a test set.
In a specific implementation, the precision testing process for the random forest based crop planting area extraction model includes, but is not limited to, a confusion matrix and an out-of-package error estimation value.
In particular toFirst, the accuracy of remote sensing extraction is evaluated based on a confusion matrix (also called an error matrix), which is a standard format for representing accuracy evaluation and is represented in a matrix form of n rows and n columns. The confusion matrix is generated by 30% test of the sample set, specific evaluation indexes comprise Overall Accuracy (OA), drawing Accuracy (Producer's Accuracy, PA), User Accuracy (UA), Kappa Coefficient (KC) and the like, and in addition, the Out-of-packet Error estimation (Out of bag Error) returned by the random forest classifier is provided, and the Accuracy indexes reflect the Accuracy of image classification from different sides. Secondly, the planting area is estimated on different scales by combining the sample and the drawing result, and is compared and verified with the statistical data (generally adopting official data) of the corresponding scale. The planting area is mainly obtained by multiplying the number of pixels in the drawing result by the area of the pixels, and the uncertainty can be adjusted and estimated through the proportion of each category in the sample set. And fitting a first-order function between the statistical data and the area extraction result by a least square method. Finally by evaluating R2And the size of the slope, and the reliability of the area extraction result is checked. R is2The closer to 1, the better the consistency of the area extraction results among all regions is represented; the closer the slope is to 1, the better the statistical data is consistent with the area extraction results. Finally, consistency comparison verification on spatial distribution is carried out with other classification charting data products.
In conclusion, the accuracy of the extraction result is verified on the three aspects of precision index, area and spatial distribution, and a more solid data basis can be provided for subsequent analysis and decision.
S150, analyzing the distribution diagram of the crop planting area to determine the planting area data of the crop planting area to be detected.
In summary, the method for determining the crop planting area based on the phenological characteristics classifies the crop planting areas based on the remote sensing technology, establishes a random forest-based crop planting area extraction model based on the phenological characteristics, and fully excavates the difference between the crop types; the accuracy of the extraction result is verified on the three aspects of precision index, area and spatial distribution, so that a more solid data basis can be provided for subsequent analysis and decision; the method has the technical effects of low cost, high efficiency and capability of finely depicting the spatial distribution of the planting area.
As shown in fig. 3, the present invention provides a crop planting area determining system 300 based on the phenological characteristics, and the present invention may be installed in an electronic device. According to the implemented functions, the system 300 for determining the crop planting area based on the phenological characteristics may include an acquisition unit 310, a crop planting region characteristic vector acquisition unit 320, and a planting area data acquisition unit 330. The unit of the present invention, which may also be referred to as a module, refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform a fixed function, and are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the acquisition unit 310 is used for acquiring remote sensing image data of a crop planting area to be detected according to set time;
the crop planting area characteristic vector obtaining unit 320 is used for determining the vegetation index statistical characteristic and the vegetation phenological characteristic of the crop planting area to be detected according to the remote sensing image data; determining a crop planting area characteristic vector of the crop planting area to be detected according to the statistical feature of the vegetation index and the phenological feature of the vegetation;
a planting area data obtaining unit 330, configured to input the feature vector of the crop planting area into a pre-trained random forest-based crop planting area extraction model, and perform remote sensing extraction to determine a crop planting area distribution diagram of the to-be-detected crop planting area; and analyzing the distribution diagram of the crop planting area to determine the planting area data of the crop planting area to be detected.
The system 300 for determining the crop planting area based on the phenological characteristics classifies the crop planting areas based on the remote sensing technology, establishes a random forest-based crop planting area extraction model based on the phenological characteristics, and fully excavates the difference between the crop types; the accuracy of the extraction result is verified on the three aspects of precision index, area and spatial distribution, so that a more solid data basis can be provided for subsequent analysis and decision-making; the method has the technical effects of low cost, high efficiency and capability of finely depicting the spatial distribution of the planting area.
As shown in fig. 4, the present invention provides an electronic device 4 for a method of determining a crop planting area based on a climatic characteristic.
The electronic device 4 may comprise a processor 40, a memory 41 and a bus, and may further comprise a computer program stored in the memory 41 and operable on said processor 40, such as a crop planting area determination program 42 based on a climatic characteristic. The memory 41 may also include both an internal storage unit and an external storage device of the artificial intelligence phenological feature based crop planting area determination system. The memory 41 may be used not only to store application software installed in the artificial intelligence crop planting area determination apparatus and various types of data, such as a code of an artificial intelligence crop planting area determination assisting program, etc., but also to temporarily store data that has been output or is to be output.
The memory 41 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, and the like. The memory 41 may in some embodiments be an internal storage unit of the electronic device 4, such as a removable hard disk of the electronic device 4. The memory 41 may also be an external storage device of the electronic device 4 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 41 may be used not only to store application software installed in the electronic device 4 and various types of data, such as codes of a crop planting area determination program based on the climatic characteristics, etc., but also to temporarily store data that has been output or is to be output.
The processor 40 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 40 is a Control Unit of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes or executes programs or modules (for example, a program for determining a crop planting area based on a characteristic of a climate, etc.) stored in the memory 41 and calls data stored in the memory 41 to perform various functions of the electronic device 4 and process the data.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 41 and at least one processor 40 or the like.
Fig. 4 only shows an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 4 does not constitute a limitation of the electronic device 4, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 4 may further include a power source (such as a battery) for supplying power to the components, and preferably, the power source may be logically connected to the at least one processor 40 through a power management system, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management system. The power supply may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 4 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 4 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used to establish a communication connection between the electronic device 4 and other electronic devices.
Optionally, the electronic device 4 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device 4 and for displaying a visualized user interface.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The phenological feature-based crop planting area determination program 42 stored in the memory 41 of the electronic device 4 is a combination of instructions that, when executed in the processor 40, may implement: collecting remote sensing image data of a crop planting area to be detected according to set time; determining the vegetation index statistical characteristics and the vegetation phenological characteristics of the crop planting area to be detected according to the remote sensing image data; determining a crop planting area characteristic vector of the crop planting area to be detected according to the statistical feature of the vegetation index and the phenological feature of the vegetation; inputting the characteristic vector of the crop planting area into a pre-trained crop planting area extraction model based on a random forest, and performing remote sensing extraction to determine a crop planting area distribution diagram of the crop planting area to be detected; and analyzing the distribution diagram of the crop planting area to determine the planting area data of the crop planting area to be detected.
Specifically, the specific implementation method of the instruction by the processor 40 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, which is not described herein again. It should be emphasized that, in order to further ensure the privacy and safety of the above-mentioned program for determining the crop planting area based on the phenological characteristics, the above-mentioned data for determining the crop planting area based on the phenological characteristics are stored in the nodes of the block chain where the server cluster is located.
Further, the integrated modules/units of the electronic device 4, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer-readable medium may include: any entity or system capable of carrying said computer program code, a recording medium, a usb-disk, a removable hard disk, a magnetic diskette, an optical disk, a computer Memory, a Read-Only Memory (ROM).
An embodiment of the present invention further provides a computer-readable storage medium, where the storage medium may be nonvolatile or volatile, and the storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements: collecting remote sensing image data of a crop planting area to be detected according to set time; determining the vegetation index statistical characteristics and the vegetation phenological characteristics of the crop planting area to be detected according to the remote sensing image data; determining a crop planting area characteristic vector of the crop planting area to be detected according to the statistical feature of the vegetation index and the phenological feature of the vegetation; inputting the characteristic vector of the crop planting area into a pre-trained crop planting area extraction model based on a random forest, and performing remote sensing extraction to determine a crop planting area distribution diagram of the crop planting area to be detected; and analyzing the distribution diagram of the crop planting area to determine the planting area data of the crop planting area to be detected.
Specifically, the specific implementation method of the computer program when being executed by the processor may refer to the description of the relevant steps in the method for determining the crop planting area based on the phenological characteristics in the embodiment, which is not repeated herein.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, system, and method may be implemented in other ways. For example, the system embodiments described above are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or systems recited in the system claims may also be implemented by one unit or system in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. A crop planting area determining method based on phenological characteristics is characterized by comprising the following steps:
collecting remote sensing image data of a crop planting area to be detected according to set time;
determining vegetation index statistical characteristics and vegetation phenological characteristics of the crop planting area to be detected according to the remote sensing image data; wherein the vegetation index statistical characteristics include a normalized vegetation index, an enhanced vegetation index, a green chlorophyll vegetation index, a surface moisture index, a normalized differential senescence vegetation index, and a normalized tillage index; the vegetation phenological characteristics are obtained by fitting the time sequence of the enhanced vegetation index respectively through a linear harmonic model and a double Logistic model;
determining a crop planting area characteristic vector of the crop planting area to be detected according to the vegetation index statistical characteristic and the vegetation phenological characteristic;
inputting the characteristic vector of the crop planting area into a pre-trained random forest-based crop planting area extraction model, and performing remote sensing extraction to determine a crop planting area distribution diagram of the crop planting area to be detected;
and analyzing the distribution diagram of the crop planting area to determine the planting area data of the crop planting area to be detected.
2. The method of determining a crop planting area based on phenological features of claim 1, wherein the training method of the random forest based crop planting region extraction model comprises,
acquiring a preprocessed crop planting data set; taking 30% of sample points in the crop planting data set as a test set, and taking 70% of sample points in the crop planting data set as a training set;
training a random forest-based crop planting area extraction pre-model by using the training set and the pre-acquired classification characteristic image set;
stopping training until the precision evaluation index and the out-of-package error estimation value reach preset standards, and obtaining a crop planting area extraction model based on random forests;
and the precision evaluation index is obtained by performing precision inspection on the random forest based crop planting area extraction pre-model by using a confusion matrix generated by the test set.
3. The method for determining a crop planting area based on phenological characteristics as claimed in claim 2, wherein the method for preprocessing the crop planting data set comprises,
acquiring sample point data of the crop planting data set according to the year;
performing sample time migration on the sample point data;
screening the sample point data after the sample time migration by using a spectrum angle, and acquiring the sample point data of which the spectrum angle meets a set standard as sample data which does not change remarkably between the years;
and forming the acquired sample data which does not change remarkably between the years into a crop planting sample library, and finishing the pretreatment of the crop planting data set.
4. The method of determining a crop planting area based on climatic features of claim 1, wherein the time series of enhanced vegetation indices is fitted through a linear harmonic model by:
wherein the content of the first and second substances,f(t)is as followstAn enhanced vegetation index value for the day fit,ais a constant term and is a constant number,bis the coefficient of the first-order term,Mthe number of the harmonic wave combinations is,c、dthe coefficients are respectively a cosine function and a sine function;ωis the reciprocal of the number of days in a year,tthe number of days is the number of days,eis the residual value.
5. The method of determining a crop planting area based on phenological characteristics of claim 1, wherein said time series of enhanced vegetation indices is fitted by a dual Logistic model, by the following formula:
wherein the content of the first and second substances,f(t)is as followstA day-fitted enhanced vegetation index value;v 1 andv 2 respectively an annual background value and an amplitude value of the enhanced vegetation index;m 1 、n 1 、m 2 andn 2 the parameters are pair parameters captured in the variation trend of vegetation growth period and aging period.
6. The method of determining a crop planting area based on phenological characteristics of claim 5,
by passingm 1 Andn 1 obtaining a phenological feature at the SOS node; wherein the SOS node is a time point at which the derivative of the enhanced vegetation index time series reaches a maximum value;
by passingm 2 Andn 2 obtaining a phenological characteristic at an EOS node; wherein the EOS node is a time point at which the derivative of the enhanced vegetation index time series reaches a minimum value.
7. A crop planting area determination system based on phenological characteristics, comprising:
the acquisition unit is used for acquiring remote sensing image data of a crop planting area to be detected according to set time;
the crop planting area characteristic vector acquisition unit is used for determining the vegetation index statistical characteristic and the vegetation phenological characteristic of the crop planting area to be detected according to the remote sensing image data; determining a crop planting area characteristic vector of the crop planting area to be detected according to the vegetation index statistical characteristic and the vegetation phenological characteristic; wherein the vegetation index statistical features include a normalized vegetation index, an enhanced vegetation index, a green chlorophyll vegetation index, a surface moisture index, a normalized differential senescence vegetation index, and a normalized tillage index; the vegetation phenological characteristics are obtained by fitting the time sequence of the enhanced vegetation index respectively through a linear harmonic model and a double Logistic model;
a planting area data acquisition unit, configured to input the feature vector of the crop planting area into a pre-trained random forest-based crop planting area extraction model, and perform remote sensing extraction to determine a crop planting area distribution diagram of the to-be-detected crop planting area; and analyzing the distribution diagram of the crop planting area to determine the planting area data of the crop planting area to be detected.
8. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method of determining crop planting area based on climatic characteristics of any one of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for determining a crop planting area based on a phenological feature of any one of claims 1 to 6.
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