CN114240163A - Crop state evaluation system and method based on satellite remote sensing image - Google Patents

Crop state evaluation system and method based on satellite remote sensing image Download PDF

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CN114240163A
CN114240163A CN202111553707.1A CN202111553707A CN114240163A CN 114240163 A CN114240163 A CN 114240163A CN 202111553707 A CN202111553707 A CN 202111553707A CN 114240163 A CN114240163 A CN 114240163A
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crop
area
planting
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remote sensing
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王娟
霍顺利
张鹏
韩双江
刘晓彤
宋玉良
张大鹏
闵淑琴
张亦玓
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Beijing Zhongyuruide Architectural Design Co ltd
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Beijing Zhongyuruide Architectural Design Co ltd
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Abstract

The invention discloses a crop state evaluation system and method based on satellite remote sensing images, which belong to the field of agricultural engineering and comprise a processing module, a storage module, a satellite remote sensing image acquisition module, a crop type and spectral characteristic parameter acquisition module, a planting region acquisition and characteristic color marking module, a true edge point extraction module, a planting region area calculation module and a planting region distribution evaluation module, wherein the processing module is used for providing data transmission and execution programs for each module. The planting area and the distribution evaluation index of the crops are obtained by acquiring or processing the planting area of the typical crops through the satellite remote sensing image, the problems that the traditional method for acquiring the planting area and the area of the crops and the time and labor consumption in the evaluation process are solved, and the result and the actual condition possibly have large deviation are solved, so that the planting area and the planting state of the crops are acquired in a standardized and standardized manner, the rapid programming processing of the typical crops by scientific research personnel is facilitated, the obtained result can be repeated, and the verification can be realized.

Description

Crop state evaluation system and method based on satellite remote sensing image
Technical Field
The invention relates to the technical field of agricultural engineering, in particular to a crop state evaluation system and method based on satellite remote sensing images.
Background
In agricultural engineering, the planting state of crops is an important aspect for evaluating the development of agriculture in a region. Generally, the crop planting state evaluation includes the type and area and distribution of the typical planted crops in the local area. For a certain area, the variety of crops is relatively fixed, and the variety of the typical planted crops, especially the agriculture leading industry, can not be changed greatly. However, the area and the distribution condition of crops may fluctuate greatly in different years, the planting area and the distribution condition of typical planted crops can be obtained quickly, and a basis can be provided for local agricultural departments in the aspects of guiding crop planting, promoting the agricultural modernization process, estimating production, making an agricultural policy, building water conservancy, allocating agricultural machinery and the like.
The traditional crop planting area and distribution condition are mainly obtained by field survey and grading statistics of investigators or by estimating the breeding condition of main crops. The method needs to consume large manpower and material resources, is slow in progress, and may have large deviation between the obtained result and the actual situation. In addition, the planting distribution state of crops has no uniform evaluation standard, the traditional evaluation indexes are various, and the distribution state of a planting area cannot be visually displayed macroscopically.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a crop state evaluation system and method based on a satellite remote sensing image.
In order to achieve the purpose, the invention adopts the following technical scheme:
a crop state evaluation system based on satellite remote sensing images comprises a processing module, a storage module, a satellite remote sensing image acquisition module, a crop type and spectral characteristic parameter acquisition module, a planting region acquisition and characteristic color marking module, a true edge point extraction module, a planting region area calculation module and a planting region distribution evaluation module, wherein the processing module is used for providing data transmission and execution programs for all modules.
Furthermore, the processing module is used for executing programs of each module and providing information processing for each module, and the storage module is used for storing information transmitted by each module and storing the execution programs of each module;
the satellite remote sensing image acquisition module is used for acquiring a recent satellite remote sensing image, determining the boundary of an area needing to be evaluated, and recording the area in the boundary as an evaluation area;
the crop type and spectral characteristic parameter acquisition module is used for selecting typical crop types and providing spectral characteristic parameters of corresponding remote sensing images of the selected crops at the current time.
Furthermore, the planting area obtaining and characteristic color marking module is used for obtaining a planting area of a typical crop and marking a characteristic color on the corresponding planting area;
the real edge point extraction module is used for traversing all edge points of the crop planting area, judging whether the edge points are real edge points or false edge points, eliminating the false edge points and forming a new set of the real edge points;
the planting area calculation module is used for obtaining the number of pixel points in the planting area through line-by-line scanning to obtain a planting area estimation value of each crop;
the planting area distribution evaluation module is used for evaluating indexes of the planting area distribution of the typical planted crops according to the real edge point set and the area estimation value of each typical crop to obtain the planting area distribution evaluation indexes of each crop.
Another object of the invention is: the crop state evaluation method based on the satellite remote sensing image, therefore, the invention simultaneously provides the following technical scheme on the basis of the technical scheme:
a crop state evaluation method based on satellite remote sensing images comprises the following steps:
step S1: acquiring a recent satellite remote sensing image and determining an evaluation area;
step S2: selecting typical crop species and obtaining spectral characteristic parameters;
step S3: acquiring a planting area of a typical crop and marking a characteristic color;
step S4: traversing edge points of the planting area to obtain a true edge point set;
step S5: scanning the planting area telemeasuring image line by line to obtain a planting area estimated value;
step S6: and evaluating the distribution condition of the planting area of the typical planted crops according to the real edge point set and the area estimation value to obtain an evaluation index.
Further, the method comprises the following steps:
step S10, acquiring a satellite remote sensing image of a local area when the recent weather is clear, recording the actual distance l corresponding to a single pixel of the image, determining the boundary of an area needing to be evaluated, recording the area in the boundary as an evaluation area, and setting the area image outside the boundary as an invalid value;
s20, selecting typical crop species according to the basic situation of the local crop planting species, recording the number of the selected typical crop species as N, and giving spectral characteristic parameters of the corresponding remote sensing images of the selected crop at the current time;
s30, acquiring a planting area corresponding to crops in the evaluation area according to the variety of the selected crops and the spectral characteristic parameters of the remote sensing image of the variety, marking characteristic colors on the corresponding planting area, and marking the area marked with the characteristic color of the ith typical crop as Ti;
step S40, traversing all edge points of the crop planting area according to the first method to obtain all edge point sets Qi of the ith typical crop characteristic color area, judging whether the edge points are real edge points or false edge points according to the second method, eliminating the false edge points, and forming the real edge points into a set Ui;
step S50, calculating an area estimation value Wi of the crop planting area by using a third method according to the area Ti of the ith typical crop marked with the characteristic color; traversing the number N of typical crop species to sequentially obtain a planting area estimated value of each crop;
step S60, according to the true edge point set Ui and the area estimation value Wi of each typical crop, index evaluation is carried out on the distribution condition of the planting area of the typical planted crop through a fourth method; and traversing the number N of the typical crop species to sequentially obtain the distribution condition evaluation indexes of the planting areas of the crops.
Further, the steps for the second method described in step S40 are:
step S41, taking the ith typical crop edge point set Qi, and obtaining RGB values of 8 pixels around the typical crop edge point set Qi, wherein if the RGB values of at least 6 edge points are consistent with the characteristic color, the typical crop edge point set Qi is a false edge point, otherwise, the typical crop edge point set Qi is a true edge point;
and step S42, traversing all edge points of the edge point set Qi, and eliminating the false edge points to obtain a set Ui of true edge points.
Further, the steps for the third method described in step S50 are:
step S51, scanning the remote sensing image evaluation area line by line, counting the number of pixel points with the color of the ith typical crop characteristic color during scanning each line, and obtaining the number accumulation sum si of the pixel points with the color of the characteristic color after all scanning is finished;
step S52, obtaining the area estimation value of the ith type of typical crop through the following formula
Wi＝si*l2
Wherein l is the actual distance corresponding to a single pixel of the remote sensing image.
Further, the steps for the fourth method described in step S60 are:
step S61, calculating the reference radius ri of the i-th typical crop planting area distribution according to the following formula:
Figure BDA0003418506200000051
step S62, calculating the maximum distance d between every two pixel points in the ith typical crop true edge point set Uimax；
Step S63, calculating the evaluation index of the distribution situation of the planting area of the ith typical crop according to the following formula:
Figure BDA0003418506200000052
further, the maximum distance calculation formula used in step S62 is:
Figure BDA0003418506200000053
k∈Ui，j∈Uiand k ≠ j }
Wherein (x)k，yk) And (x)j，yj) And max represents the maximum value of all the values, wherein the values are the horizontal and vertical coordinate values of the kth pixel point and the jth pixel point.
Compared with the prior art, the invention has the beneficial effects that:
the planting area and the distribution evaluation index of the crops are obtained by acquiring or processing the planting area of the typical crops in the satellite remote sensing image, and the problems that the traditional method for acquiring the planting area and the area of the crops, the evaluation process consumes time and labor and the result possibly has large deviation with the actual condition are solved;
through the definite processing flow and the standard processing method, the crop planting area and the crop planting state are standardized and standardized, rapid programming processing of typical crops by scientific research personnel is facilitated, the obtained result can be repeated, and verification can be achieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a schematic flow chart of a crop state evaluation system based on satellite remote sensing images according to the present invention;
FIG. 2 is a flow chart illustrating the steps of a crop condition evaluation method based on satellite remote sensing images according to the present invention;
FIG. 3 is a schematic diagram of the steps of obtaining an evaluation area and selecting crops in the crop state evaluation method based on the satellite remote sensing image provided by the invention;
FIG. 4 is a schematic diagram of a crop planting region acquisition and marginalization step in the crop state evaluation method based on satellite remote sensing images according to the present invention;
fig. 5 is a schematic diagram of region labeling in the crop state evaluation method based on the satellite remote sensing image according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
According to the specific embodiment of the invention, a crop state evaluation system based on a satellite remote sensing image is provided.
Referring to fig. 1, a crop state evaluation system based on a satellite remote sensing image comprises a processing module, a storage module, a satellite remote sensing image acquisition module, a crop species and spectral characteristic parameter acquisition module, a planting region acquisition and characteristic color marking module, a true edge point extraction module, a planting region area calculation module and a planting region distribution evaluation module, wherein the processing module is used for providing data transmission and execution programs for the modules.
The processing module is used for executing programs of each module and providing information processing for each module, and the storage module is used for storing information transmitted by each module and storing the execution programs of each module;
the satellite remote sensing image acquisition module is used for acquiring a recent satellite remote sensing image, determining the boundary of an area needing to be evaluated, and recording the area in the boundary as an evaluation area;
the crop type and spectral characteristic parameter acquisition module is used for selecting typical crop types and giving spectral characteristic parameters of corresponding remote sensing images of the selected crops at the current time;
the planting area obtaining and characteristic color marking module is used for obtaining a planting area of a typical crop and marking a characteristic color on the corresponding planting area;
the real edge point extraction module is used for traversing all edge points of the crop planting area, judging whether the edge points are real edge points or false edge points, eliminating the false edge points and forming a new set of the real edge points;
the planting area calculation module is used for obtaining the number of pixel points in the planting area through line-by-line scanning to obtain a planting area estimation value of each crop;
the planting area distribution evaluation module is used for evaluating indexes of the planting area distribution of the typical planted crops according to the real edge point set and the area estimation value of each typical crop to obtain the planting area distribution evaluation indexes of each crop.
In this embodiment, the processing module adopts any one of an I7 processor or an airy cloud server, wherein any adaptive processor with the same performance or function may be used to perform information processing, and the storage module adopts any storage device matched with the processing module.
According to the embodiment of the invention, the crop state evaluation method based on the satellite remote sensing image is also provided.
Example one
Referring to fig. 2, a crop state evaluation method based on satellite remote sensing images includes the following steps:
step S1: acquiring a recent satellite remote sensing image and determining an evaluation area;
step S2: selecting typical crop species and obtaining spectral characteristic parameters;
step S3: acquiring a planting area of a typical crop and marking a characteristic color;
step S4: traversing edge points of the planting area to obtain a true edge point set;
step S5: scanning the planting area telemeasuring image line by line to obtain a planting area estimated value;
step S6: and evaluating the distribution condition of the planting area of the typical planted crops according to the real edge point set and the area estimation value to obtain an evaluation index.
Example two
Referring to fig. 3-5, on the basis of the first embodiment, a crop state evaluation method based on satellite remote sensing images includes the following steps:
step S10, acquiring a satellite remote sensing image of a local area when the recent weather is clear, recording the actual distance l corresponding to a single pixel of the image, determining the boundary of an area needing to be evaluated, recording the area in the boundary as an evaluation area, and setting the area image outside the boundary as an invalid value;
s20, selecting typical crop species according to the basic situation of the local crop planting species, recording the number of the selected typical crop species as N, and giving spectral characteristic parameters of the corresponding remote sensing images of the selected crop at the current time;
s30, acquiring a planting area corresponding to crops in the evaluation area according to the variety of the selected crops and the spectral characteristic parameters of the remote sensing image of the variety, marking characteristic colors on the corresponding planting area, and marking the area marked with the characteristic color of the ith typical crop as Ti;
step S40, traversing all edge points of the crop planting area according to the first method to obtain all edge point sets Qi of the ith typical crop characteristic color area, judging whether the edge points are real edge points or false edge points according to the second method, eliminating the false edge points, and forming the real edge points into a set Ui;
specifically, the first method is a Prewitt difference method or a sobel difference method.
More specifically, the second method comprises the following steps:
step S41, taking the ith typical crop edge point set Qi, and obtaining RGB values of 8 pixels around the typical crop edge point set Qi, wherein if the RGB values of at least 6 edge points are consistent with the characteristic color, the typical crop edge point set Qi is a false edge point, otherwise, the typical crop edge point set Qi is a true edge point;
and step S42, traversing all edge points of the edge point set Qi, and eliminating the false edge points to obtain a set Ui of true edge points.
Step S50, calculating an area estimation value Wi of the crop planting area by using a third method according to the area Ti of the ith typical crop marked with the characteristic color; traversing the number N of typical crop species to sequentially obtain a planting area estimated value of each crop;
specifically, the third method comprises the following steps:
step S51, scanning the remote sensing image evaluation area line by line, counting the number of pixel points with the color of the ith typical crop characteristic color during scanning each line, and obtaining the number accumulation sum si of the pixel points with the color of the characteristic color after all scanning is finished;
step S52, obtaining the area estimation value of the ith type of typical crop through the following formula
Wi＝si*l2
Wherein l is the actual distance corresponding to a single pixel of the remote sensing image.
Step S60, according to the true edge point set Ui and the area estimation value Wi of each typical crop, index evaluation is carried out on the distribution condition of the planting area of the typical planted crop through a fourth method; and traversing the number N of the typical crop species to sequentially obtain the distribution condition evaluation indexes of the planting areas of the crops.
Specifically, the fourth method comprises the following steps:
step S61, calculating the reference radius ri of the i-th typical crop planting area distribution according to the following formula:
Figure BDA0003418506200000101
step S62, calculating the maximum distance d between every two pixel points in the ith typical crop true edge point set Uimax；
Step S63, calculating the evaluation index of the distribution situation of the planting area of the ith typical crop according to the following formula:
Figure BDA0003418506200000111
the minimum value of the index is 1, and the closer the value is to 1, the more concentrated the distribution of the crop planting area is, thus being beneficial to large-scale concentrated operation and convenient for concentrated management; larger values represent more distributed distributions, which is not conducive to centralized management.
More specifically, the maximum distance calculation formula is:
Figure BDA0003418506200000112
k∈Ui，j∈Uiand k ≠ j }
Wherein (x)k，yk) And (x)j，yj) And max represents the maximum value of all the values, wherein the values are the horizontal and vertical coordinate values of the kth pixel point and the jth pixel point.
EXAMPLE III
On the basis of the second embodiment, the method comprises the steps of S10-S60:
step S101, acquiring a satellite remote sensing image of a local area in recent clear weather, acquiring an actual distance l corresponding to a single pixel of the image to be 5km through a telemetering data acquisition way, and recording the proportion of the telemetering image to be 5 km/pixel; determining the boundary of the area needing to be evaluated, recording the area in the boundary as an evaluation area, and setting the area image outside the boundary as an invalid value, wherein the evaluation area can be a certain city or a certain area.
Step S201, selecting typical crop types as soybean and corn according to the basic situation of the local crop planting types, recording the number of the selected typical crop types as 2, and providing spectral characteristic parameters of the soybean and the corn in a remote sensing image, wherein the common spectral characteristic parameters comprise Red Edge (RE), Blue Edge (BE), Yellow Edge (YE), vegetation Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), red edge first-stage number maximum value (DRE), leaf surface water content index (WI), leaf surface chlorophyll index (LCI) and the like.
S301, acquiring a planting area corresponding to crops in an evaluation area according to the variety of the selected crops and the spectral characteristic parameters of the remote sensing image of the selected crops, marking the corresponding planting area with a characteristic color, wherein the area marked with the characteristic color of soybeans is T1, and the area marked with the characteristic color of corns is T2;
step S401, acquiring all edge point sets Q1 and Q2 of the characteristic color areas of the soybeans and the corns according to the first method, traversing all edge points of the soybeans and the corns, judging whether the edge points are true edge points or false edge points according to the second method, eliminating the false edge points, and forming the true edge points into sets U1 and U2, wherein the specific description is as follows:
the first method can be a Prewitt difference method or a sobel difference method, and can also be independently processed by PS;
it should be noted that, taking Prewitt difference method as an example, the difference method performs gray difference on eight pixel points near the center pixel point to determine whether the pixel points are edge points, and the formula is expressed as follows:
Figure BDA0003418506200000121
if it is
Figure BDA0003418506200000122
Then (x, y) is an edge point; where F is a preset threshold, F (x, y) is a gray value at coordinates (x, y),
Figure BDA0003418506200000123
representing gradient values, and the edge points are schematically shown in 2-4;
further, the second method comprises the steps of:
taking a certain pixel point of the sets Q1 and Q2, calculating RGB values of 8 pixels around the certain pixel point, wherein if the RGB values of at least 6 edge points are consistent with the characteristic color of the soybeans/corns, the certain pixel point is a false edge point, and otherwise, the certain pixel point is a true edge point;
traversing all edge points of the edge point sets Q1 and Q2, and eliminating false edge points to obtain sets U1 and U2 of true edge points;
calculating area estimation values W1 and W2 of the crop planting area according to the coloring areas T1 and T2 of the soybeans and the corns by using a third method;
specifically, the third method comprises the following steps:
scanning the remote sensing image evaluation area line by line, counting the number of pixel points with the color of soybean characteristic color when scanning each line, obtaining the number sum s1 of the pixel points with the color of the characteristic color as 228 after all scanning is finished, scanning the remote sensing image evaluation area line by line, counting the number of the pixel points with the color of corn characteristic color when scanning each line, and obtaining the number sum s2 of the pixel points with the color of the characteristic color as 190 after all scanning is finished;
the area estimation values W of soybean and corn are obtained by the following formula1And W2
W1＝si*l2＝228*52＝5700km2
W2＝si*l2＝190*52＝4750km2
Wherein l is the actual distance corresponding to a single pixel of the remote sensing image, and is 5km in the process;
step S601, according to the true edge point sets U1 and U2 of the soybeans and the corns and the area estimation value W1And W2Evaluating indexes of the distribution conditions of the two planting areas by a fourth method;
specifically, the fourth method comprises the steps of:
calculating reference radiuses r1 and r2 of the distribution of the soybean and corn planting areas according to the following formula:
Figure BDA0003418506200000141
Figure BDA0003418506200000142
respectively calculating the maximum distance d between every two pixel points of the true edge point sets U1 and U2 of the soybeans and the cornsmax；
The maximum distance calculation formula is:
Figure BDA0003418506200000143
k∈Ui，j∈Uiand k ≠ j }
Wherein (x)k，yk) And (x)j，yj) The horizontal and vertical coordinate values of the kth pixel point and the jth pixel point are adopted, and max represents the maximum value of all the values;
according to calculation, the maximum distance of the soybean true edge point pixel is 85, and the maximum distance of the corn true edge point pixel is 39;
calculating the evaluation indexes of the distribution conditions of the planting areas of the soybeans and the corns according to the following formula:
Figure BDA0003418506200000144
Figure BDA0003418506200000145
the minimum value of the index is 1, and the closer the value is to 1, the more concentrated the distribution of the crop planting area is, the large-scale concentrated operation is facilitated, and the centralized management is facilitated; larger values represent more distributed distributions, which is not conducive to centralized management.
In this embodiment, the above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold in the formula are set by those skilled in the art according to the actual situation or obtained by simulating a large amount of data.
The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto.

Claims (9)

1. A crop state evaluation system based on a satellite remote sensing image is characterized by comprising a processing module, a storage module, a satellite remote sensing image acquisition module, a crop type and spectral characteristic parameter acquisition module, a planting region acquisition and characteristic color marking module, a true edge point extraction module, a planting region area calculation module and a planting region distribution evaluation module, wherein the processing module is used for providing data transmission and execution programs for all modules.
2. The crop state evaluation system based on satellite remote sensing images according to claim 1, wherein the processing module is used for executing programs of each module and providing information processing for each module, and the storage module is used for storing information transmitted by each module and storing execution programs of each module;
the satellite remote sensing image acquisition module is used for acquiring a recent satellite remote sensing image, determining the boundary of an area needing to be evaluated, and recording the area in the boundary as an evaluation area;
the crop type and spectral characteristic parameter acquisition module is used for selecting typical crop types and providing spectral characteristic parameters of corresponding remote sensing images of the selected crops at the current time.
3. The crop state evaluation system based on the satellite remote sensing image according to claim 2, wherein the planting region obtaining and characteristic color marking module is used for obtaining a planting region of a typical crop and marking a characteristic color on the corresponding planting region;
the real edge point extraction module is used for traversing all edge points of the crop planting area, judging whether the edge points are real edge points or false edge points, eliminating the false edge points and forming a new set of the real edge points;
the planting area calculation module is used for obtaining the number of pixel points in the planting area through line-by-line scanning to obtain a planting area estimation value of each crop;
the planting area distribution evaluation module is used for evaluating indexes of the planting area distribution of the typical planted crops according to the real edge point set and the area estimation value of each typical crop to obtain the planting area distribution evaluation indexes of each crop.
4. A crop state evaluation method based on satellite remote sensing images, which is used for the crop state evaluation system based on satellite remote sensing images as claimed in any one of claims 1 to 3, and is characterized in that: the crop state evaluation method based on the satellite remote sensing image comprises the following steps:
step S1: acquiring a recent satellite remote sensing image and determining an evaluation area;
step S2: selecting typical crop species and obtaining spectral characteristic parameters;
step S3: acquiring a planting area of a typical crop and marking a characteristic color;
step S4: traversing edge points of the planting area to obtain a true edge point set;
step S5: scanning the planting area telemeasuring image line by line to obtain a planting area estimated value;
step S6: and evaluating the distribution condition of the planting area of the typical planted crops according to the real edge point set and the area estimation value to obtain an evaluation index.
5. The crop state evaluation method based on the satellite remote sensing image according to claim 4, characterized by comprising the following steps:
step S10, acquiring a satellite remote sensing image of a local area when the recent weather is clear, recording the actual distance l corresponding to a single pixel of the image, determining the boundary of an area needing to be evaluated, recording the area in the boundary as an evaluation area, and setting the area image outside the boundary as an invalid value;
s20, selecting typical crop species according to the basic situation of the local crop planting species, recording the number of the selected typical crop species as N, and giving spectral characteristic parameters of the corresponding remote sensing images of the selected crop at the current time;
s30, acquiring a planting area corresponding to crops in the evaluation area according to the variety of the selected crops and the spectral characteristic parameters of the remote sensing image of the variety, marking characteristic colors on the corresponding planting area, and marking the area marked with the characteristic color of the ith typical crop as Ti;
step S40, traversing all edge points of the crop planting area according to the first method to obtain all edge point sets Qi of the ith typical crop characteristic color area, judging whether the edge points are real edge points or false edge points according to the second method, eliminating the false edge points, and forming the real edge points into a set Ui;
step S50, calculating an area estimation value Wi of the crop planting area by using a third method according to the area Ti of the ith typical crop marked with the characteristic color; traversing the number N of typical crop species to sequentially obtain a planting area estimated value of each crop;
step S60, according to the true edge point set Ui and the area estimation value Wi of each typical crop, index evaluation is carried out on the distribution condition of the planting area of the typical planted crop through a fourth method; and traversing the number N of the typical crop species to sequentially obtain the distribution condition evaluation indexes of the planting areas of the crops.
6. The method for evaluating the status of a crop according to claim 5, wherein the second method used in step S40 comprises the steps of:
step S41, taking the ith typical crop edge point set Qi, and obtaining RGB values of 8 pixels around the typical crop edge point set Qi, wherein if the RGB values of at least 6 edge points are consistent with the characteristic color, the typical crop edge point set Qi is a false edge point, otherwise, the typical crop edge point set Qi is a true edge point;
and step S42, traversing all edge points of the edge point set Qi, and eliminating the false edge points to obtain a set Ui of true edge points.
7. The method for evaluating the status of a crop according to claim 5, wherein the third method used in step S50 comprises the steps of:
step S51, scanning the remote sensing image evaluation area line by line, counting the number of pixel points with the color of the ith typical crop characteristic color during scanning each line, and obtaining the number accumulation sum si of the pixel points with the color of the characteristic color after all scanning is finished;
step S52, obtaining the area estimation value of the ith type of typical crop through the following formula
Wi＝si*l2
Wherein l is the actual distance corresponding to a single pixel of the remote sensing image.
8. The method for evaluating the status of a crop according to claim 5, wherein the fourth method used in step S60 comprises the steps of:
step S61, calculating the reference radius ri of the i-th typical crop planting area distribution according to the following formula:
Figure FDA0003418506190000041
step S62, calculating the maximum distance d between every two pixel points in the ith typical crop true edge point set Uimax；
Step S63, calculating the evaluation index of the distribution situation of the planting area of the ith typical crop according to the following formula:
Figure FDA0003418506190000042
9. the method for evaluating the status of a crop according to claim 8, wherein the maximum distance calculation formula used in step S62 is:
Figure FDA0003418506190000043
wherein (x)k，yk) And (x)j，yj) And max represents the maximum value of all the values, wherein the values are the horizontal and vertical coordinate values of the kth pixel point and the jth pixel point.
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