CN115496999A - Method and device for estimating field straw yield - Google Patents
Method and device for estimating field straw yield Download PDFInfo
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Abstract
The invention provides a field straw yield estimation method and device, wherein the method comprises the following steps: acquiring target data; acquiring the target crop yield of a target moment in a target area based on the target data; acquiring the yield of the vertical straw corresponding to the target moment in the target area based on the yield of the target crop; the target data comprise a target vegetation index of a target remote sensing image, and meteorological data and topographic data of a target area at a target moment; the target remote sensing image is a remote sensing image of an original region at a target moment; the target area is an area planted with target crops in the original area; the target time is within a period from when the target crop is planted in the target area to when the target crop is harvested. The field straw yield estimation method and device provided by the invention can be used for improving the field straw yield estimation accuracy by combining the influences of regional meteorological factors and topographic factors on the field straw yield.
Description
Technical Field
The invention relates to the technical field of remote sensing, in particular to a field straw yield estimation method and device.
Background
Straw is a generic term for the stem and leaf (ear) part of a mature crop, usually referring to the remainder of wheat, rice, corn, potatoes, oilseed rape, cotton, sugar cane and other crops (usually coarse grain) after harvesting the seeds. The field straw can be recycled as fertilizer, and can also be smashed and returned to the field to increase soil fertility, and the straw returning to the field can also play a role in carbon fixation, so that the method has important significance in estimating the yield of the field straw.
In the prior art, the yield of field straws on the site is estimated based on the planting area of the current season crops and the historical yield data of the field straws on the site by depending on experience. The traditional field on-site straw yield estimation method is susceptible to experience level, historical yield data volume and other uncontrollable factors, so that the accuracy of field on-site straw yield estimation based on the traditional field on-site straw yield estimation method is low. Therefore, how to estimate the field straw yield more accurately is a technical problem to be solved urgently in the field.
Disclosure of Invention
The invention provides a field straw yield estimation method and device, the method is used for solving the defect of low accuracy in field yield estimation in the prior art and realizing more accurate estimation of field straw yield.
The invention provides a field straw yield estimation method, which comprises the following steps:
acquiring target data;
obtaining the target crop yield of a target time in a target area based on the target data;
obtaining the field straw yield corresponding to the target moment in the target area based on the target crop yield;
the target data comprise a target vegetation index of a target remote sensing image, and meteorological data and topographic data of a target area at a target moment; the target remote sensing image is a remote sensing image of the original area at the target moment; the target area is an area where target crops are planted in the original area; the target time is within a period from the time the target crop is planted in the target area to the time the target crop is harvested for seed.
According to the field on-site straw yield estimation method provided by the invention, the target crop yield at the target moment in the target area is obtained based on the target data, and the method comprises the following steps:
inputting the target data into a crop yield estimation model to obtain the target crop yield output by the crop yield estimation model;
wherein the crop yield estimation model is constructed based on a hierarchical linear model; the model parameters of the crop yield estimation model are obtained by inverting based on the sample crop yield and the sample data corresponding to the sample time of the sample area; the sample data comprises a target vegetation index of a sample remote sensing image, and meteorological data and topographic data of an original sample area at a sample moment; the sample region is a region of the original sample region where the sample crop is planted; the sample remote sensing image is a remote sensing image of the original sample area at the sample moment; the sample time is within a period from when the sample crop is planted in the sample area to when the sample crop is harvested for seed; the sample crop is of the same species as the target crop.
According to the field straw yield estimation method provided by the invention, the crop yield estimation model comprises the following steps: a first crop yield estimator model and a second crop yield estimator model; the second crop yield estimator sub-model is nested within the first crop yield estimator sub-model;
accordingly, the inputting the target data into a crop yield estimation model, obtaining the target crop yield output by the crop yield estimation model, comprises:
inputting the meteorological data and the topographic data of the target area at the target moment into the second crop yield estimation submodel to obtain target parameters output by the second crop yield estimation submodel;
and inputting the target vegetation index of the target remote sensing image and the target parameter into the first crop yield estimation submodel to obtain the target crop yield output by the first crop yield estimation submodel.
According to the field on-site straw yield estimation method provided by the invention, the target area is obtained on the basis of the following modes:
inputting the target vegetation index of the target remote sensing image into a crop monitoring model, and acquiring the target area output by the crop monitoring model;
the crop monitoring model is constructed based on a random forest algorithm and is obtained based on a target vegetation index of the sample remote sensing image and the sample area training.
According to the field on-site straw yield estimation method provided by the invention, the target plant index comprises a normalized vegetation index; the target vegetation index is determined based on a correlation of vegetation index to crop yield.
According to the field on-site straw yield estimation method provided by the invention, under the condition that the number of the original areas is multiple, after the on-site straw yield corresponding to the target time in the target area is obtained based on the target crop yield, the method further comprises the following steps:
and generating a map of the yield of the stalks in the field based on the yield of the stalks in the field.
According to the field on-site straw yield estimation method provided by the invention, under the condition that the number of the target time is multiple, after the on-site straw yield corresponding to the target time in the target area is obtained based on the target crop yield, the method further comprises the following steps:
and generating a vertical straw yield time sequence distribution diagram based on the vertical straw yield.
The invention also provides a field straw yield estimation device, which comprises:
the target data acquisition module is used for acquiring target data;
the crop yield inversion module is used for acquiring the target crop yield at the target moment in the target area based on the target data;
the straw yield estimation module is used for acquiring the yield of the standing straws corresponding to the target moment in the target area based on the yield of the target crops;
the target data comprise a target vegetation index of a target remote sensing image, and meteorological data and topographic data of a target area at a target moment; the target remote sensing image is a remote sensing image of the original area at the target moment; the target area is an area in which a target crop is planted in the original area; the target time is within a period from the time the target crop is planted in the target area to the time the target crop is harvested for seed.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize any one of the field on-site straw yield estimation methods.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a field on-site straw yield estimation method as described in any of the above.
The invention also provides a computer program product comprising a computer program, wherein the computer program is executed by a processor to realize the field on-site straw yield estimation method.
According to the field on-site straw yield estimation method and device, after the target crop yield at the target moment in the target area is obtained based on the target data, the on-site straw yield corresponding to the target moment in the target area is obtained based on the target crop yield at the target moment in the target area, the target data comprise the target vegetation index of the target remote sensing image and the meteorological data and topographic data at the target moment in the target area, the target remote sensing image is the remote sensing image of the original area at the target moment, the target area is the area in which the target crop is planted in the original area, and the target moment is within the period from the time when the target crop is planted in the target area to the time when the target crop is harvested, the influence of regional meteorological factors and topographic factors on the on-site straw yield can be combined, and the field on-site straw yield estimation accuracy is improved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a field straw yield estimation method provided by the invention;
FIG. 2 is a second schematic flow chart of the field on-site straw yield estimation method provided by the present invention;
FIG. 3 is a schematic structural diagram of the field on-site straw yield estimation device provided by the invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
It should be noted that, as an important crop, corn has the advantages of wide planting range, high yield, etc. The field straw after the maize is harvested can be used as fertilizer for recycling, can also be smashed and returned to the field to increase soil fertility, and can also play a role in carbon fixation when returned to the field, and the returning rate of the straw is related to carbon balance of a field system. Therefore, the yield of the field on-site straws is estimated, and data support can be provided for straw recycling, subsequent retting and diversified utilization of straw resources.
The traditional field straw yield estimation method is generally based on the planting area of the current crop and the historical field straw yield data, and the field straw yield estimation method depends on experience. The traditional field on-site straw yield estimation method is susceptible to the influence of experience level, historical yield data volume and other uncontrollable factors, so that the accuracy of field on-site straw yield estimation based on the traditional field on-site straw yield estimation method is low. With the rapid development of remote sensing technology, remote sensing images are widely applied to the fields of environmental protection, national soil resource investigation, disaster monitoring and the like due to the advantages of high imaging definition, objective and rich information, timeliness, strong practicability and the like.
Therefore, the invention provides a field straw estimation method and device. The field on-site straw estimation method provided by the invention can comprehensively invert the corn yield based on the remote sensing data, the meteorological data and the topographic data, further estimate the on-site straw yield based on the corn yield according to the incidence relation between the on-site straw yield and the corn yield, combine the influence of regional meteorological factors and topographic factors on the on-site straw yield, and improve the accuracy of field on-site straw yield estimation.
FIG. 1 is a schematic flow chart of a field on-site straw yield estimation method provided by the invention. The field on-site straw yield estimation method of the present invention is described below with reference to fig. 1. As shown in fig. 1, the method includes: step 101, target data is obtained.
The target data comprise a target vegetation index of a target remote sensing image, and meteorological data and topographic data of a target region at a target moment; the target remote sensing image is a remote sensing image of an original area at a target moment; the target area is an area planted with target crops in the original area; the target time is within a period from when the target crop is planted in the target area to when the target crop is harvested.
It should be noted that the main execution body of the embodiment of the invention is a field straw monitoring device.
In addition, the field straw in the embodiment of the present invention is the remaining part of the mature crops after harvesting the seeds, and the crops may include, but are not limited to, wheat, rice, corn, potatoes, rape, cotton, sugarcane, etc. The embodiment of the invention does not limit the concrete types of the field standing straws. The method for estimating the yield of the field straws is described by taking the field straws as the rest parts of the mature corns after seeds are harvested as an example, namely the target crops and the sample crops are corns.
In the embodiment of the invention, a GEE (Google Earth Engine) platform can be utilized to obtain the remote sensing image of the target time in the original region as the target remote sensing image.
It should be noted that, in the embodiment of the present invention, an area planted with corn in an original area may be used as a target area. The target area may be obtained in advance, or may be obtained based on a target remote sensing image, and the obtaining manner of the target area in the embodiment of the present invention is not particularly limited.
It can be understood that the yield of the straw in the field corresponding to the target time in the target area is the yield of the straw in the field corresponding to the target time in the original area.
The GEE platform is a remote sensing cloud computing platform, integrates massive geospatial data, image data, meteorological and weather data, geophysical data and the like, has corresponding visualization and analysis computing capacity, and can be used for exchanging information and command standard sets (API) with a computer operating system. The image data comprises Landsat series, sentinel series, MODIS and local area high-resolution images; the weather and meteorological data comprise surface temperature and emissivity, surface variables of long-term climate prediction and historical difference, atmosphere data inverted by satellite observation and weather data predicted and observed in short time; the geophysical data comprise topographic and landform data, land cover data, farmland distribution data, noctilucent data and the like.
The target time is within a period from the time when the corn is planted in the target area to the time when the corn is harvested to complete the seed. For example: the target time may be within 4 months of the year to 10 months of the year.
In the embodiment of the invention, any time from the time when the corn is planted in the target area to the time when the corn is harvested and seeds are not harvested can be determined as the target time; and determining the specific time from the planting of the corn in the target area to the ending of the harvesting of the corn seeds as the target time according to the actual situation and/or the prior knowledge. The target time is not particularly limited in the embodiment of the present invention.
It should be noted that the number of target time instants may be one or more.
Optionally, the corn growing season (7-9 months) is a cloudy and rainy season, so that the situation of cloud cover is very easy to occur, optical images in part of important periods cannot be used, and the SAR images have the characteristics of strong penetrability and no influence by cloud cover, so that the defects of the optical images can be well compensated, therefore, the target remote sensing image in the embodiment of the invention can be the combination of the Sentinel-2 optical image and the Sentinel-1SAR image, the cloud cover shielding problem can be solved in the combination of the optical images and the SAR images, the defects of the optical images are compensated, and the influence on the accuracy of field site straw yield estimation can be further avoided.
It should be noted that the Sentinel-2 optical image is L2A-level data, which has undergone radiation correction and geometric correction, and does not need to be corrected, and the Sentinel-2 optical image can be used directly after being acquired and then appropriately mosaiced and cropped as required.
After the target remote sensing image is obtained, the target vegetation index of the target remote sensing image can be obtained through numerical calculation, mathematical statistics and other modes.
Based on the disclosure of the various embodiments above, the target vegetation index comprises a normalized vegetation index; the target vegetation index is determined based on a correlation of the vegetation index with crop yield.
Optionally, in the embodiment of the present invention, a Normalized Vegetation Index (Normalized Difference Vegetation Index, NDVI), an Enhanced Vegetation Index (Enhanced Vegetation Index, EVI), a Difference Vegetation Index (Difference Vegetation Index, DVI), a Green chlorophyllin Index (CIgreen), a Structure Insensitive Pigment Index (SIPI), a Normalized Water Index (Normalized Difference Water Index, NDWI), and a Normalized Area Vegetation Index (Normalized Area Vegetation Index NAVI) of the target remote sensing image may also be obtained as the original Vegetation Index of the target remote sensing image through a numerical calculation method.
The normalized vegetation index NDVI may be calculated based on the following formula:
the enhanced vegetation index EVI may be calculated based on the following formula:
the differential vegetation index DVI may be calculated based on the following formula:
DVI＝ρ nir -ρ r (3)
the ratio vegetation index RVI may be calculated based on the following formula:
the green chlorophyll index CIgreen can be calculated based on the following formula:
the NAVI may be calculated based on the following equation:
the structure insensitive pigment index SIPI can be calculated based on the following formula:
the normalized water index NDWI may be calculated based on the following equation:
wherein ρ r Representing the red waveband reflectivity of the remote sensing image; rho b Representing the blue-band reflectivity of the remote sensing image; ρ is a unit of a gradient g Representing the green band reflectivity of the remote sensing image; ρ is a unit of a gradient nir And the reflectivity of the remote sensing image in the near infrared band is represented.
In order to avoid the characteristic redundancy image classification efficiency, after the original vegetation index of the target remote sensing image is obtained, the original vegetation index of the target remote sensing image can be used as an independent variable, the correlation between the original vegetation index and the corn yield is analyzed, and the normalized vegetation index NDVI which has the highest correlation with the corn yield in the original vegetation index is determined as the target vegetation index.
In the embodiment of the present invention, the meteorological data of the target time of the target area may be acquired in various ways, for example: the meteorological data of the target time of the target area can be acquired in a data query mode.
Alternatively, the above-mentioned meteorological data may include, but is not limited to, at least one of daily minimum air temperature (Tmin, ° c), daily maximum air temperature (Tmax, ° c), sunshine duration (RAD), and rainfall (PRE, mm).
In the embodiment of the present invention, the topographic data of the target time in the target area may be obtained in various ways, for example: the terrain data of the target area at the target moment can be acquired in a data query mode.
Alternatively, the terrain data may include, but is not limited to, elevation (E), slope (S), and heading (A).
And 102, acquiring the target crop yield at the target moment in the target area based on the target data.
Specifically, after the target data is acquired, the corn yield at the target time in the target area can be acquired in a numerical calculation manner.
And 103, acquiring the yield of the standing straws corresponding to the target moment in the target area based on the yield of the target crops.
Specifically, after the corn yield at the target moment in the target area is obtained, the corn straw yield corresponding to the target moment in the target area can be obtained through a numerical calculation mode.
It is to be noted that the calculation of the dry weight of the corn stalks is divided into an overground part and a root part. The dry weight of the above-ground parts of the corn stalks can be calculated based on the following formula:
Ps＝AMP×MSI (9)
wherein Ps represents the dry weight of the aerial parts of the corn stalks; AMP represents annual average corn yield; MSI represents the corn stover index.
The maize straw index MSI can be calculated based on the following formula:
wherein HI denotes harvest index.
The harvest index HI can be calculated based on the following formula:
wherein, grain dry weight represents the dry weight of corn; and an above coarse dry matter.
The dry weight of the corn stover root portion may be calculated based on the following formula:
wherein Pr represents the dry weight of the aerial parts of the corn stalks; R/S represents the root-crown ratio of the mature period of the corn.
The dry weight of the corn stover can be calculated based on the following formula:
P＝P s +P r (13)
taking northern corn growing area as an example, HI =0.52, msi =0.98, and r/S =0.063, the formula for northern corn stalks is P = AMP × 0.98+ (AMP/0.52) × 0.063.
According to the embodiment of the invention, after the target crop yield at the target moment in the target area is obtained based on the target data, the field straw yield corresponding to the target moment in the target area is obtained based on the target crop yield at the target moment in the target area, the target data comprises the target vegetation index of the target remote sensing image and the meteorological data and topographic data at the target moment in the target area, the target remote sensing image is the remote sensing image of the original area at the target moment, the target area is the area in which the target crop is planted in the original area, and the target moment is in the period from the time when the target crop is planted in the target area to the time when the target crop is harvested to be planted, so that the influence of regional meteorological factors and topographic factors on the field straw yield can be combined, and the field straw yield estimation accuracy is improved.
Based on the content of the above embodiments, obtaining the target crop yield at the target time in the target area based on the target data includes: and inputting the target data into the crop yield estimation model to obtain the target crop yield output by the crop yield estimation model.
Wherein the crop yield estimation model is constructed based on a layered linear model; model parameters of the crop yield estimation model are obtained by inverting based on sample crop yield and sample data corresponding to the sample time of the sample area; the sample data comprises a target vegetation index of the sample remote sensing image, and meteorological data and topographic data of an original sample area at a sample moment; the sample area is an area where a sample crop is planted in the original sample area; the sample remote sensing image is a remote sensing image of an original sample area at a sample time; the sample time is in the period from the sample crop planting in the sample area to the sample crop harvesting seed; the sample crop is of the same species as the target crop.
Specifically, a Hierarchical Linear Model (HLM) is one of multivariate statistical analysis, is a least squares regression analysis model considering a data nesting structure, and can be used for layering data according to the interactivity of the data, so that the data variation on the same layer can be considered as a whole, and the data variation between different layers can also be considered.
The embodiment of the invention can construct the crop yield estimation model based on the hierarchical linear model, and can obtain the model parameters of the crop yield estimation model by inversion based on the sample crop yield and the sample data corresponding to the original sample area sample time.
Optionally, HLM7.03 Student software can be used to implement the construction of the crop yield estimation model in the embodiment of the present invention.
It should be noted that the manner of obtaining the sample data may be the same as the manner of obtaining the target data, and the specific process of obtaining the sample data may refer to the contents of the foregoing embodiments, which is not described in detail in the embodiments of the present invention.
Alternatively, a plurality of sample moments are determined between 7 months, 8 months and 9 months, a remote sensing image of an original sample area can be obtained at each sample moment, and the sample remote sensing image can be obtained by combining the VV and VH polarization characteristics of the SAR image in the optical image missing time period.
After the target data is obtained, the target data may be input into the crop yield estimation model, and the corn yield at the target time in the target area output by the crop yield estimation model may be obtained.
According to the embodiment of the invention, the target data is input into the crop yield estimation model constructed on the basis of the hierarchical linear model, the yield of the target crops in the target area output by the crop yield estimation model is obtained, and the crop yield can be comprehensively inverted on the basis of the remote sensing data, the meteorological data and the topographic data, so that the crop yield can be estimated more accurately and efficiently, and the accuracy of field site straw yield estimation can be further improved.
Based on the disclosure of the above embodiments, the crop yield estimation model comprises: a first crop yield estimator sub-model and a second crop yield estimator sub-model; the second crop yield estimator sub-model is nested within the first crop yield estimator sub-model.
Correspondingly, the target data is input into the crop yield estimation model, and the yield of the target crop output by the crop yield estimation model is obtained, wherein the method comprises the following steps: and inputting the meteorological data and the topographic data of the target time of the target area into a second crop yield estimation submodel, and acquiring target parameters output by the second crop yield estimation submodel.
Specifically, the crop yield estimator sub-model in the embodiment of the present invention includes a first crop yield estimator sub-model and a second crop yield estimator sub-model. The first crop yield estimator model may invert the yield of corn based on the vegetation index; the second crop yield estimator model may invert model parameters (intercept and efficiency) in the first crop product estimator model based on the meteorological data and the topographical data
After the meteorological data and the topographic data of the target time of the target area are input into the second crop yield estimation submodel, the second crop yield estimation submodel can acquire and output target parameters in a numerical calculation mode based on the meteorological data of the target time of the target area and the topographic data of the original area.
The specific calculation formula of the second crop yield estimation submodel is as follows:
β 0 ＝γ 00 +γ 01 ×RAD+γ 02 ×T max +γ 03 ×T min +γ 04 ×PRE+γ 05 ×E+γ 06 ×S+γ 07 ×S+μ 0 (14)
β 1 ＝γ 10 +γ 11 ×RAD+γ 12 ×T max +γ 13 ×T min +γ 14 ×PRE+γ 15 ×E+γ 16 ×S+γ 17 ×S+μ 1 (15)
wherein, beta 0 And beta 1 All represent a target parameter, beta 0 Representing the intercept, beta, of a first crop yield estimator sub-model 1 Representing a slope of the first crop yield estimate submodel; gamma ray 00 To gamma 10 Representing an intercept of the second crop yield estimate submodel; gamma ray 11 -γ 17 Representing the slope corresponding to the meteorological data and topographic data of the target area target moment in the second crop yield estimation submodel; mu.s 0 And mu 1 Representing random errors of the second crop yield estimation submodel.
In addition, γ is 00 To gamma 07 And gamma 11 To gamma 17 Parameters of the submodel are estimated for the second crop yield. Wherein, γ 00 Representing the 0 th parameter in equation 14, the first bit in the subscript "0" may be associated with β 0 Corresponding; gamma ray 10 Representing the 0 th parameter in equation 15, the first digit in the subscript "1" may be associated with β 1 And (7) corresponding.
And inputting the target parameters and the target vegetation index of the target remote sensing image into the first crop yield estimation submodel, and acquiring the target crop yield output by the first crop yield estimation submodel.
Specifically, after the target parameter is obtained, the target parameter and the target vegetation index of the target remote sensing image may be input into a first crop yield estimation submodel, and the first crop yield estimation submodel may obtain and output the target crop yield of the target area at the target time based on the target parameter and the target vegetation index of the target remote sensing image, where the specific calculation formula is as follows:
Yield＝β 0 +β 1 ×NDVI+e (16)
wherein Yield represents the crop Yield of the original area; the NDVI represents a normalized vegetation index of the target remote sensing image.
The crop yield estimator model in the embodiment of the invention comprises a first crop yield estimator model and a second crop yield estimator model, wherein the first crop yield estimator model can invert the yield of corn based on a vegetation index; the second crop yield estimation submodel can invert the model parameters in the first crop product estimation submodel based on meteorological data and topographic data, and can acquire the yield of the target crop in the target area more accurately and efficiently.
Based on the content of the above embodiments, the target area is acquired based on the following manner: and inputting the target vegetation index of the target remote sensing image into the crop monitoring model, and acquiring a target area output by the crop monitoring model.
The crop monitoring model is constructed based on a random forest algorithm and is obtained based on a target vegetation index of a sample remote sensing image and sample area training.
Optionally, in the embodiment of the present invention, the number of the sample remote sensing images is 700, where 60% is used for training and 40% is used for verification.
After the sample remote sensing images are obtained, the target vegetation index of each sample remote sensing image can be obtained based on a numerical calculation mode.
The method comprises the steps of carrying out field investigation on an original sample region, recording position information of a corn field by adopting portable GPS positioning equipment, leading collected point location information into ArcGIS within an error of 5m, and determining the sample region corresponding to each sample remote sensing image.
Wherein the primary artifacts in the raw sample area may include: corn, grassland, villages and towns, roads (bare land) and water bodies.
By taking the target vegetation index of each sample remote sensing image as a sample and taking the sample area corresponding to each sample remote sensing image as a sample label, the crop monitoring model constructed based on the random forest algorithm can be trained, and the trained crop monitoring model can be obtained.
After the trained crop monitoring model is obtained, the target vegetation index of the target remote sensing image can be input into the trained crop monitoring model, and then the target area output by the trained crop monitoring model can be obtained.
Optionally, in the embodiment of the present invention, the estimation of the corn yield in the target area may be realized based on BandMath and layerstack modules in the ENVI software.
According to the embodiment of the invention, the target vegetation index of the target remote sensing image is input into the crop monitoring model to obtain the target area output by the crop monitoring model, so that the target area can be obtained more accurately and efficiently.
Based on the content of each embodiment, when the number of the original areas is multiple, after obtaining the yield of the straw in the site corresponding to the target time in the target area based on the yield of the target crop, the method further includes: and generating a field straw yield distribution graph based on the field straw yield.
Specifically, under the condition that the number of the original regions is multiple, after the corn straw yield corresponding to the target time of each target region is obtained, a local straw yield distribution map can be generated based on the corn straw yield corresponding to the target time of each target region, and the local straw yield distribution map is used for describing the corn straw yield distribution situation corresponding to the target time in each target region.
Optionally, a map of the yield of the stalks of the land can be generated based on the ArcGIS, wherein the ArcGIS can provide a scalable and comprehensive GIS platform for the user.
Optionally, the amount of the corn stalk output can be represented by the shade of the color in the corn stalk output distribution diagram, and the darker the color in the corn stalk output distribution diagram indicates that the corn stalk output in the region is higher; the darker the region in the corn stover yield profile, the lower the corn stover yield for that region.
Optionally, when the number of the original regions is multiple, after the corn yield at the target time in each target region is obtained, a corn yield distribution map may be generated based on the corn yield at the target time in each target region, so as to describe the corn yield distribution situation at the target time in each target region, and band calculation may be performed in the ENVI software according to the model parameters and the independent variable parameters in the crop monitoring model, so as to obtain a crop yield spatial distribution map.
According to the embodiment of the invention, the yield distribution map of the local straw is generated based on the yield of the local straw corresponding to the target time in each target area under the condition that the number of the original areas is multiple, so that the yield distribution situation of the local straw corresponding to the target time in each target area can be presented more intuitively and accurately, and the user perception can be improved.
Based on the content of the foregoing embodiments, when there are a plurality of target times, after obtaining the yield of the local straw corresponding to the target time in the target area based on the yield of the target crop in the target area, the method further includes: and generating a vertical straw yield time sequence distribution diagram based on the vertical straw yield.
Specifically, when the number of the original regions is multiple and the target time is multiple, after the corn straw yield corresponding to each target time of each target region is obtained, a temporal distribution map of the corn straw yield can be generated based on the corn straw yield corresponding to each target time of each target region, and the temporal distribution map is used for describing the temporal distribution of the corn straw yield in each target region.
Optionally, when the number of the original regions is multiple and each target time corresponds to each other, after the corn yield corresponding to each target time in the target region is obtained, a crop yield time sequence distribution map may be generated based on the corn yield corresponding to each target time in the target region, and the crop yield time sequence distribution map is used for describing the time sequence distribution situation of the corn yield in each target region.
According to the embodiment of the invention, the time sequence distribution map of the yield of the local straw is generated based on the yield of the local straw corresponding to each target moment in each target area under the condition that the number of the original areas is multiple and the number of the target moments is multiple, so that the time sequence distribution situation of the yield of the local straw in each target area can be presented more intuitively and accurately, and the user perception can be improved.
In order to facilitate understanding of the field on-site straw yield estimation method provided by the present invention, the field on-site straw yield estimation method provided by the present invention is described below by way of an example. FIG. 2 is a second schematic flow chart of the field on-site straw yield estimation method provided by the present invention. As shown in fig. 2, when the field on-site straw yield estimation method is used for on-site straw yield estimation, firstly, optical and SAR remote sensing images of an original region at a target moment can be obtained, and further a target remote sensing image can be obtained;
secondly, a target vegetation index of the target remote sensing image can be obtained, and a target area can be determined in the original area based on the target vegetation index of the target remote sensing image;
thirdly, meteorological data and topographic data of a target time of the target area can be obtained, so that a target vegetation index of the target remote sensing image, the meteorological data and the topographic data of the target time of the target area can be input into the crop yield estimation model, and the crop yield of the target time of the target area is inverted by the crop yield estimation model;
thirdly, on the basis of the target crop yield of the target area at the target moment, the site straw yield corresponding to the target area at the target moment can be obtained;
finally, a map of the yield of the stalks in the field can be generated based on the yield of the stalks in the field corresponding to the target time of the target area.
FIG. 3 is a schematic structural diagram of the field straw yield estimation device provided by the invention. The field on-site straw yield estimation device provided by the invention is described below with reference to fig. 3, and the field on-site straw yield estimation device described below and the field on-site straw yield estimation method provided by the invention described above can be referred to correspondingly. As shown in fig. 3, the apparatus includes: a target data acquisition module 301, a crop yield inversion module 302 and a straw yield estimation module 303.
A target data obtaining module 301, configured to obtain target data.
And a crop yield inversion module 302, configured to obtain a target crop yield at a target time in the target area based on the target data.
And the straw yield estimation module 303 is used for acquiring the site straw yield corresponding to the target moment in the target area based on the target crop yield.
The target data comprise a target vegetation index of a target remote sensing image, and meteorological data and topographic data of a target area at a target moment; the target remote sensing image is a remote sensing image of an original region at a target moment; the target area is an area planted with target crops in the original area; the target time is within a period from when the target crop is planted in the target area to when the target crop is harvested.
Specifically, the target data acquisition module 301, the crop yield inversion module 302 and the straw yield estimation module 303 are electrically connected.
The target data acquisition module 301 may be configured to acquire a remote sensing image of a target time in an original region as a target remote sensing image by using a GEE (Google Earth Engine) platform; the method can also be used for obtaining the target vegetation index of the target remote sensing image in the modes of numerical calculation, mathematical statistics and the like; and the method can be used for acquiring meteorological data and topographic data of the target area at the target moment in various ways.
Crop yield inversion module 302 may be configured to obtain the corn yield at a target time within a target area by way of numerical calculations.
The straw yield estimation module 303 may be configured to obtain the corn straw yield corresponding to the target time in the target area by a numerical calculation.
Optionally, the crop yield inversion module 302 may be further specifically configured to input the target data into the crop yield estimation model, and obtain the target crop yield output by the crop yield estimation model; wherein the crop yield estimation model is constructed based on a layered linear model; model parameters of the crop yield estimation model are obtained by inverting based on sample crop yield and sample data corresponding to the sample time of the sample area; the sample data comprises a target vegetation index of the sample remote sensing image, and meteorological data and topographic data of an original sample area at a sample moment; the sample area is an area where a sample crop is planted in the original sample area; the sample remote sensing image is a remote sensing image of an original sample area at a sample moment; the sample time is in the period from the sample crop planting in the sample area to the sample crop harvesting seed; the sample crop is of the same species as the target crop.
Optionally, the crop yield inversion module 302 may be further specifically configured to input the meteorological data and the topographic data at the target time of the target area into the second crop yield estimation submodel, and obtain a target parameter output by the second crop yield estimation submodel; and inputting the target vegetation index and the target parameters of the target remote sensing image into the first crop yield estimation submodel, and acquiring the target crop yield output by the first crop yield estimation submodel.
Optionally, the field on-site straw yield estimation device may further comprise a crop monitoring module.
The crop monitoring module can be used for inputting the target vegetation index of the target remote sensing image into the crop monitoring model and acquiring a target area output by the crop monitoring model; the crop monitoring model is constructed based on a random forest algorithm and is obtained based on target vegetation indexes of sample remote sensing images and sample area training.
Optionally, the field on-site straw yield estimation device may further include an image generation module.
The image generation module can be used for generating a field straw yield distribution map based on the field straw yield.
The image generation module can also be used for generating a time sequence distribution map of the yield of the straw in the field based on the yield of the straw in the field.
According to the field on-site straw yield estimation device disclosed by the embodiment of the invention, after the target crop yield at the target moment in the target area is obtained based on the target data, the on-site straw yield corresponding to the target moment in the target area is obtained based on the target crop yield at the target moment in the target area, the target data comprises the target vegetation index of the target remote sensing image and the meteorological data and topographic data at the target moment in the target area, the target remote sensing image is the remote sensing image of the original area at the target moment, the target area is the area in which the target crop is planted in the original area, and the target moment is in the period from the time when the target crop is planted in the target area to the time when the target crop is harvested to be harvested, the influence of regional meteorological factors and topographic factors on the on-site straw yield can be combined, and the field on-site straw yield estimation accuracy is improved.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor) 410, a communication Interface 420, a memory (memory) 430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may invoke logic instructions in the memory 430 to perform a field on-site straw production estimation method comprising: acquiring target data; acquiring the target crop yield of a target moment in a target area based on the target data; acquiring the yield of the vertical straw corresponding to the target moment in the target area based on the yield of the target crop; the target data comprise a target vegetation index of a target remote sensing image, and meteorological data and topographic data of a target region at a target moment; the target remote sensing image is a remote sensing image of an original region at a target moment; the target area is an area planted with target crops in the original area; the target time is within a period from when the target crop is planted in the target area to when the target crop is harvested.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being stored on a non-transitory computer readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing the field on-site straw production estimation method provided by the above methods, the method comprising: acquiring target data; acquiring the target crop yield of a target moment in a target area based on the target data; on the basis of the yield of the target crop, the yield of the on-site straw corresponding to the target moment in the target area is obtained; the target data comprise a target vegetation index of a target remote sensing image, and meteorological data and topographic data of a target area at a target moment; the target remote sensing image is a remote sensing image of an original region at a target moment; the target area is an area planted with target crops in the original area; the target time is within a period from when the target crop is planted in the target area to when the target crop is harvested.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements a method for field on-site straw production estimation provided by the methods described above, the method comprising: acquiring target data; acquiring the target crop yield at a target moment in a target area based on the target data; on the basis of the yield of the target crop, the yield of the on-site straw corresponding to the target moment in the target area is obtained; the target data comprise a target vegetation index of a target remote sensing image, and meteorological data and topographic data of a target area at a target moment; the target remote sensing image is a remote sensing image of an original region at a target moment; the target area is an area planted with target crops in the original area; the target time is within a period from when the target crop is planted in the target area to when the target crop is harvested.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple 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. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. Based on the understanding, the above technical solutions substantially or otherwise contributing to the prior art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A field straw yield estimation method is characterized by comprising the following steps:
acquiring target data;
obtaining the target crop yield of a target time in a target area based on the target data;
obtaining the yield of the standing straws corresponding to the target moment in the target area based on the yield of the target crops;
the target data comprise a target vegetation index of a target remote sensing image, and meteorological data and topographic data of a target area at a target moment; the target remote sensing image is a remote sensing image of the original area at the target moment; the target area is an area where target crops are planted in the original area; the target time is within a period from the time the target crop is planted in the target area to the time the target crop is harvested for seed.
2. The field on-site straw yield estimation method as claimed in claim 1, wherein the obtaining of the target crop yield at the target moment in the target area based on the target data comprises:
inputting the target data into a crop yield estimation model to obtain the target crop yield output by the crop yield estimation model;
wherein the crop yield estimation model is constructed based on a hierarchical linear model; the model parameters of the crop yield estimation model are obtained by inverting based on the sample crop yield and the sample data corresponding to the sample time of the sample area; the sample data comprises a target vegetation index of a sample remote sensing image, and meteorological data and topographic data of an original sample area at a sample moment; the sample region is a region of the original sample region where the sample crop is planted; the sample remote sensing image is a remote sensing image of the original sample area at the sample moment; the sample time is within a period from when the sample crop is planted in the sample area to when the sample crop is harvested; the sample crop is of the same species as the target crop.
3. The field on-site straw yield estimation method of claim 2, wherein the crop yield estimation model comprises: a first crop yield estimator sub-model and a second crop yield estimator sub-model; the second crop yield estimator sub-model is nested within the first crop yield estimator sub-model;
accordingly, the inputting the target data into a crop yield estimation model, obtaining the target crop yield output by the crop yield estimation model, comprises:
inputting the meteorological data and the topographic data of the target area at the target moment into the second crop yield estimation submodel to obtain target parameters output by the second crop yield estimation submodel;
and inputting the target vegetation index of the target remote sensing image and the target parameter into the first crop yield estimation submodel to obtain the target crop yield output by the first crop yield estimation submodel.
4. The field on-site straw production estimation method according to claim 3, wherein the target area is obtained based on:
inputting the target vegetation index of the target remote sensing image into a crop monitoring model, and acquiring the target area output by the crop monitoring model;
the crop monitoring model is constructed based on a random forest algorithm and is obtained based on the target vegetation index of the sample remote sensing image and the sample area training.
5. The field on-site straw yield estimation method of claim 1, wherein the target plant index comprises a normalized vegetation index; the target vegetation index is determined based on a correlation of vegetation index to crop yield.
6. The field on-site straw yield estimation method as claimed in any one of claims 1 to 5, wherein when the number of the original areas is plural, after obtaining the on-site straw yield corresponding to the target time in the target area based on the target crop yield, the method further comprises:
and generating a map of the yield of the stalks in the field based on the yield of the stalks in the field.
7. The field on-site straw yield estimation method according to claim 6, wherein when the number of the target time is multiple, after the on-site straw yield corresponding to the target time in the target area is obtained based on the target crop yield, the method further comprises:
and generating a time sequence distribution map of the yield of the stalks in the field based on the yield of the stalks in the field.
8. A field standing straw yield estimation device is characterized by comprising:
the target data acquisition module is used for acquiring target data;
the crop yield inversion module is used for acquiring the target crop yield at the target moment in the target area based on the target data;
the straw yield estimation module is used for acquiring the yield of the standing straws corresponding to the target moment in the target area based on the yield of the target crops;
the target data comprise a target vegetation index of a target remote sensing image, and meteorological data and topographic data of a target area at a target moment; the target remote sensing image is a remote sensing image of the original area at the target moment; the target area is an area where target crops are planted in the original area; the target time is within a period from the time the target crop is planted in the target area to the time the target crop is harvested for seed.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the field on-site straw production estimation method of any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the field on-site straw yield estimation method of any one of claims 1 to 7.
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