CN111598938A - Farmland land area measurement method and system based on scale configuration distortion correction - Google Patents
Farmland land area measurement method and system based on scale configuration distortion correction Download PDFInfo
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Abstract
The invention relates to a farmland land area measuring method and system based on scale configuration distortion correction, comprising the following steps: acquiring an image of a farmland to be detected, which is shot by a shooting terminal; segmenting the image of the farmland to be detected by using an image segmentation method, and determining a first calibration plate image, a second calibration plate image and an image of the target farmland to be detected; respectively processing the images by utilizing digital image morphology, and determining the number of pixel points in the first calibration plate image, the number of pixel points in the second calibration plate image and the number of pixel points in the target farmland image to be detected; determining an image tangential area distortion coefficient according to the number of pixel points in the first calibration plate image; determining an image radial area distortion coefficient according to the number of pixel points in the second calibration plate image; and determining the area of the target farmland to be detected according to the number of pixel points in the image of the target farmland to be detected, the image tangential area distortion coefficient and the image radial area distortion coefficient. The method can accurately measure the farmland area and reduce the measurement cost.
Description
Technical Field
The invention relates to the technical field of farmland area measurement, in particular to a farmland mu measuring method and system based on scale configuration distortion correction.
Background
The monitoring of the planting area of crops is highly valued from time to time, and information such as the planting area of grain crops can be known and accurately mastered through the monitoring of the planting area. Meanwhile, the timely acquisition of the planting information of the grain crops can provide scientific basis for formulating agricultural production policy, and the method has very important significance for ensuring grain safety. In agricultural production, the timely and accurate prediction of crop area and yield also has important significance for farmers to better implement crop management every year and next year, especially in aspects of crop insurance, harvest plans, storage requirements, cash flow budget, nutrition, pesticides, water investment and the like. The traditional crop area measuring method mainly depends on measuring by using a tape measure and the like, measures the lengths of all boundaries of a crop planting field in the field, and calculates by using a geometric area calculating method, so that the labor intensity is high, the cost is high, the subjectivity is strong, and the measuring accuracy is low.
With the rapid development of science and technology, further requirements are made on the cost of area measurement and the precision of an area measurement result, and therefore how to improve a farmland area measurement means is a problem which needs to be solved urgently at present.
Disclosure of Invention
The invention aims to provide a farmland land area measuring method and system based on scale configuration distortion correction, which can accurately measure farmland area and reduce measurement cost.
In order to achieve the purpose, the invention provides the following scheme:
a farmland mu measuring method based on scale configuration distortion correction comprises the following steps:
acquiring an image of a farmland to be detected, which is shot by a shooting terminal; the image of the farmland to be detected comprises a first calibration plate and a second calibration plate; the first calibration plate and the second calibration plate are respectively positioned at the positions of two end points of a diagonal line in a farmland area to be tested;
segmenting the farmland image to be detected by using an image segmentation method, and determining a first calibration plate image, a second calibration plate image and a target farmland image to be detected;
respectively processing the first calibration plate image, the second calibration plate image and the farmland image of the target to be detected by using digital image morphology, and determining the number of pixel points in the first calibration plate image, the number of pixel points in the second calibration plate image and the number of pixel points in the farmland image of the target to be detected;
determining an image tangential area distortion coefficient according to the number of pixel points in the first calibration plate image;
determining an image radial area distortion coefficient according to the number of pixel points in the second calibration plate image;
and determining the area of the target farmland to be detected according to the number of pixel points in the image of the target farmland to be detected, the image tangential area distortion coefficient and the image radial area distortion coefficient.
Optionally, obtaining the farmland image to be measured shot by the shooting terminal specifically includes:
acquiring images by adopting the shooting terminal according to a trigger signal at a fixed time interval to obtain a plurality of images; the shooting terminal is an unmanned aerial vehicle provided with a visible light camera;
and splicing the plurality of images by adopting three-dimensional model generation software to obtain the image of the farmland to be detected.
Optionally, the determining an image tangential area distortion coefficient according to the number of the pixels in the first calibration plate image specifically includes:
according to formula α ═ SIs justDetermining an image tangential area distortion coefficient by using the/M;
wherein S isIs justThe area of the first calibration plate, M is the number of pixels in the image of the first calibration plate, and α is the image tangential area distortion coefficient.
Optionally, the determining the distortion coefficient of the radial area of the image according to the number of the pixels in the image of the second calibration plate specifically includes:
according to formula β ═ SIIIDetermining the distortion coefficient of the radial area of the image;
wherein S isIIIThe area of the second calibration plate, N is the number of pixels in the image of the second calibration plate, and β is the distortion coefficient of the radial area of the image.
Optionally, the area of the target farmland to be detected is determined according to the number of the pixel points in the target farmland image to be detected, the image tangential area distortion coefficient and the image radial area distortion coefficient, and the method specifically includes the following steps:
according to the formula SField (Tu)Determining the area of the target farmland to be detected as (α. L + β. L)/2;
wherein α is an image tangential area distortion coefficient, β is an image radial area distortion coefficient, L is the number of pixel points in a farmland image of a target to be detected, and SField (Tu)The area of the target farmland to be measured.
Optionally, the first calibration plate is square, and the second calibration plate is regular triangle.
A farmland mu-measuring system based on scale configuration distortion correction, the farmland mu-measuring system comprising:
the system comprises a to-be-detected farmland image acquisition module, a to-be-detected farmland image acquisition module and a to-be-detected farmland image acquisition module, wherein the to-be-detected farmland image acquisition module is used for acquiring a to-be-detected farmland image shot by a shooting terminal; the image of the farmland to be detected comprises a first calibration plate and a second calibration plate; the first calibration plate and the second calibration plate are respectively positioned at the positions of two end points of a diagonal line in a farmland area to be tested;
the image determining module is used for segmenting the farmland image to be detected by using an image segmentation method and determining a first calibration plate image, a second calibration plate image and a target farmland image to be detected;
the image pixel number determining module is used for respectively processing the first calibration plate image, the second calibration plate image and the target farmland image to be detected by utilizing digital image morphology, and determining the number of pixels in the first calibration plate image, the number of pixels in the second calibration plate image and the number of pixels in the target farmland image to be detected;
the image tangential area distortion coefficient determining module is used for determining an image tangential area distortion coefficient according to the number of pixel points in the first calibration plate image;
the image radial area distortion coefficient determining module is used for determining an image radial area distortion coefficient according to the number of pixel points in the second calibration plate image;
and the target farmland area determination module is used for determining the area of the target farmland to be measured according to the number of pixel points in the target farmland image to be measured, the image tangential area distortion coefficient and the image radial area distortion coefficient.
Optionally, the farmland image acquisition module to be tested specifically includes:
the shooting terminal is used for shooting images to obtain a plurality of images; the shooting terminal is an unmanned aerial vehicle provided with a visible light camera;
and the to-be-detected farmland image acquisition unit is used for splicing the multiple images by adopting three-dimensional model generation software to acquire the to-be-detected farmland image.
Optionally, the image tangential area distortion coefficient determining module and the image radial area distortion coefficient determining module specifically include:
an image tangential area distortion coefficient determining unit for determining S according to the formula αIs justDetermining an image tangential area distortion coefficient by using the/M;
wherein S isIs justThe area of the first calibration plate, M is the number of pixel points in the image of the first calibration plate, and α is the image tangential area distortion coefficient;
an image radial area distortion coefficient determining unit for determining S according to the formula βIIIDetermining the distortion coefficient of the radial area of the image;
wherein S isIIIThe area of the second calibration plate, N is the number of pixels in the image of the second calibration plate, and β is the distortion coefficient of the radial area of the image.
Optionally, the module for determining the farmland area of the target to be measured specifically includes:
a farmland area determination unit of the target to be measured for determining the farmland area according to the formula SField (Tu)Determining the area of the target farmland to be detected as (α. L + β. L)/2;
wherein α is an image tangential area distortion coefficient, β is an image radial area distortion coefficient, L is the number of pixel points in a farmland image of a target to be detected, and SField (Tu)The area of the target farmland to be measured.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a farmland area measurement method and system based on scale configuration distortion correction, which are characterized in that a shooting terminal is used for acquiring an image of a farmland to be measured, a first calibration plate and a second calibration plate which are placed are used for correcting tangential area distortion and radial area distortion of the area of a target farmland to be measured, and finally the area of the target farmland to be measured is determined by using an image tangential area distortion coefficient and an image radial area distortion coefficient.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flow chart of a farmland mu measuring method based on scale configuration distortion correction provided by the embodiment of the invention;
FIG. 2 is a schematic diagram of a positioning position of a calibration board according to an embodiment of the present invention;
FIG. 3 is a first calibration plate image provided by an embodiment of the present invention;
FIG. 4 is a second calibration plate image provided by an embodiment of the present invention;
fig. 5 is a first calibration board image extracted by threshold segmentation according to an embodiment of the present invention;
fig. 6 is a second calibration board image extracted by threshold segmentation according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a farmland mu measuring system based on scale configuration distortion correction provided by an embodiment of the 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. 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.
The invention aims to provide a farmland land area measuring method and system based on scale configuration distortion correction, which can accurately measure farmland area and reduce measurement cost.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a farmland mu measuring method based on scale configuration distortion correction provided by an embodiment of the present invention, and as shown in fig. 1, the farmland mu measuring method of the present invention includes:
s1, obtaining an image of the farmland to be detected, which is shot by the shooting terminal; the image of the farmland to be detected comprises a first calibration plate and a second calibration plate; the first calibration plate and the second calibration plate are respectively positioned at the positions of two end points of a diagonal line in a farmland area to be measured. In the embodiment of the invention, the first calibration plate is square, the second calibration plate is regular triangle, and the specific placement position is shown in fig. 2. S1 specifically includes:
1-1) acquiring images by adopting the shooting terminal according to a trigger signal at a fixed time interval to obtain a plurality of images, wherein the shooting terminal is an unmanned aerial vehicle provided with a visible light camera. Specifically, the visible light camera is arranged towards the course direction of the unmanned aerial vehicle and is set to follow the course, the flight speed of the unmanned aerial vehicle is not more than 5m/s, the course repetition rate of the unmanned aerial vehicle is not less than 60%, the sidewise repetition rate of the unmanned aerial vehicle is not less than 55%, a flight control system of the unmanned aerial vehicle provides a fixed time interval, a trigger signal is given at every fixed time interval, the visible light camera carries out image acquisition on the ground after receiving the trigger signal, a position posture recorder on the unmanned aerial vehicle carries out recording of the position and the posture, the frequency of the trigger signal is not more than 1.5HZ, information is acquired once when each trigger signal is received by the unmanned aerial vehicle and the position posture recorder, and a.
And 1-2) splicing the multiple images by adopting three-dimensional model generation software to obtain an image of the farmland to be detected. Specifically, three-dimensional model generation software (Agisosoft Photoshop Professional) of Russian Agisosoft LLC company is used for image splicing, all images of a field collected by an unmanned aerial vehicle are spliced into a complete image of a farmland to be tested, and longitude and latitude coordinates, a pitch angle, a roll angle and a course angle of the unmanned aerial vehicle in a position posture recorder, altitude and illumination information data are imported into the Agisosoft Photoshop Professional software for orthographic projection correction, image distortion is eliminated, and the image of the farmland to be tested is finally obtained.
And S2, segmenting the farmland image to be detected by using an image segmentation method, and determining a first calibration plate image, a second calibration plate image and a farmland image to be detected.
And S3, respectively processing the first calibration plate image, the second calibration plate image and the farmland image of the target to be detected by using digital image morphology, and determining the number of pixel points in the first calibration plate image, the number of pixel points in the second calibration plate image and the number of pixel points in the farmland image of the target to be detected.
Specifically, the threshold segmentation is adopted to extract the boundary of the first calibration plate and the second calibration plate, namely, the difference between the color and the gray value of the calibration plate in the image of the farmland to be detected and the surrounding area is utilized to segment, then the digital image morphology is utilized to carry out expansion operation on the first calibration plate and the second calibration plate, the internal area of the whole scale is filled, and the number M of pixels contained in the first calibration plate and the number N of pixels contained in the second calibration plate are respectively calculated. And (3) dividing (threshold dividing) the colors and gray values of the crop planting field blocks in the aerial photography image by the difference (DN value difference) of the surrounding areas to extract field block boundaries, performing dilation operation on the field block images by using digital image morphology, filling the internal areas of the whole farmland, and calculating the number L of pixel points contained in the field blocks.
S4, determining the image tangential area distortion coefficient according to the number of the pixel points in the first calibration plate image, specifically, according to the formula α, SIs justDetermining an image tangential area distortion coefficient by using the/M; wherein S isIs justThe area of the first calibration plate, M is the number of pixels in the image of the first calibration plate, and α is the image tangential area distortion coefficient.
S5, determining the distortion coefficient of the radial area of the image according to the number of the pixel points in the image of the second calibration plate, specifically, according to the formula β, SIIIDetermining the distortion coefficient of the radial area of the image; wherein S isIIIThe area of the second calibration plate, N is the number of pixels in the image of the second calibration plate, and β is the distortion coefficient of the radial area of the image.
And S6, determining the area of the target farmland to be detected according to the number of pixel points in the image of the target farmland to be detected, the image tangential area distortion coefficient and the image radial area distortion coefficient. In particular, according to the formula SField (Tu)Determining the area of the target farmland to be detected (α. L + β. L)/2, wherein α is an image tangential area distortion coefficient, β is an image radial area distortion coefficient, L is the number of pixel points in the image of the target farmland to be detected, and SField (Tu)The area of the target farmland to be measured.
For example, the following steps are carried out:
under the condition of sunny and cloudy weather illumination, firstly, before the unmanned aerial vehicle takes an aerial photograph to collect information, a square and a regular triangle calibration plate are respectively placed on two end points of a diagonal line in a target farmland area to be measured, and the side lengths of the square and the regular triangle are both formed by scales of 5 m.
An SH-8-px-RS-02 type eight-rotor unmanned aerial vehicle designed and produced by Zhejiang university is used, a visible light camera is carried on the unmanned aerial vehicle, farmland image spectrum information is collected, the flying height of the unmanned aerial vehicle is set to be 25 m, the flying speed is 2.5m/s, the shooting angle is always vertical downwards, and the image collection covers the whole target farmland area to be measured.
The visible light cameras are arranged towards the heading direction of the unmanned aerial vehicle and are set to follow the heading, the heading repetition rate of the unmanned aerial vehicle is set to be 60%, the sidewise repetition rate of the unmanned aerial vehicle is set to be 55%, and the flying speed is 3 m/s. When image information is collected, the self-stabilization cradle head ensures that the direction of a lens is kept vertical to the ground, a trigger signal is given out by a flight control system of the unmanned aerial vehicle at a fixed time interval of 0.91s, the visible light camera takes pictures to collect information, image splicing is completed according to the same characteristics of forward and backward repeated images and side-to-side repeated images of the pictures, and then orthoimage correction is carried out.
As shown in fig. 3-6, the boundaries of squares and triangles are extracted by using the difference between the color and gray value of the scale in the aerial photograph and the surrounding area, the squares and triangles are subjected to expansion operation by using digital image morphology, the internal area of the whole scale is filled, and the numbers of pixel points 161002 and 70696 contained in the squares and the triangles are respectively calculated.
Calculating to obtain the areas of a square calibration plate and a regular triangle calibration plate with side lengths of 5m respectively as SIs just＝25m2、
According to the formula:
α＝Sis just/M
β＝SIII/N
The image tangential area distortion coefficient α was determined to be 1.55 × 10-4Image radial area distortion coefficient β ═ 1.53 × 10-4。
Extracting field boundaries by utilizing the difference segmentation of the colors and gray values of the crop planting fields in the aerial photography graph and the surrounding areas, performing expansion operation on the field images by utilizing digital image morphology, filling the inner area of the whole ruler, and calculating to obtain the number 9.678 x 10 of pixel points contained in the fields7。
Correcting the tangential area distortion and the radial area distortion of a measurement target region by using the placed square and regular triangle calibration plates, and calculating the field area S by using the image tangential area distortion coefficient, the image radial area distortion coefficient and the field pixel point number LField (Tu)：
SField (Tu)＝(1.55*10-4·9.678*107+1.53*10-4·9.678*107)/2＝14903m2。
The invention also provides a farmland mu measuring system based on scale configuration distortion correction, as shown in fig. 7, the farmland mu measuring system comprises:
the system comprises a to-be-detected farmland image acquisition module 1, a to-be-detected farmland image acquisition module and a to-be-detected farmland image acquisition module, wherein the to-be-detected farmland image acquisition module is used for acquiring a to-be-detected farmland image shot by a shooting terminal; the image of the farmland to be detected comprises a first calibration plate and a second calibration plate; the first calibration plate and the second calibration plate are respectively positioned at the positions of two end points of a diagonal line in a farmland area to be measured.
And the image determining module 2 is used for segmenting the farmland image to be detected by using an image segmentation method, and determining a first calibration plate image, a second calibration plate image and a target farmland image to be detected.
And the image pixel number determining module 3 is used for respectively processing the first calibration plate image, the second calibration plate image and the target farmland image to be detected by utilizing digital image morphology, and determining the number of pixels in the first calibration plate image, the number of pixels in the second calibration plate image and the number of pixels in the target farmland image to be detected.
And the image tangential area distortion coefficient determining module 4 is used for determining the image tangential area distortion coefficient according to the number of the pixel points in the first calibration plate image.
And the image radial area distortion coefficient determining module 5 is used for determining the image radial area distortion coefficient according to the number of the pixel points in the second calibration plate image.
And the target farmland area determination module 6 is used for determining the area of the target farmland to be detected according to the number of pixel points in the target farmland image to be detected, the image tangential area distortion coefficient and the image radial area distortion coefficient.
Preferably, the farmland image acquisition module 1 to be tested specifically comprises:
the shooting terminal is used for shooting images to obtain a plurality of images; wherein, shoot the unmanned aerial vehicle of terminal for being provided with the visible light camera.
And the to-be-detected farmland image acquisition unit is used for splicing the multiple images by adopting three-dimensional model generation software to acquire the to-be-detected farmland image.
Preferably, the image tangential area distortion coefficient determining module 4 and the image radial area distortion coefficient determining module 5 specifically include:
an image tangential area distortion coefficient determining unit for determining S according to the formula αIs justDetermining an image tangential area distortion coefficient by using the/M; wherein S isIs justThe area of the first calibration plate, M is the number of pixels in the image of the first calibration plate, and α is the image tangential area distortion coefficient.
An image radial area distortion coefficient determining unit for determining S according to the formula βIIIDetermining the distortion coefficient of the radial area of the image; wherein S isIIIThe area of the second calibration plate, N is the number of pixels in the image of the second calibration plate, and β is the distortion coefficient of the radial area of the image.
Preferably, the module 6 for determining the farmland area of the target to be measured specifically includes:
a farmland area determination unit of the target to be measured for determining the farmland area according to the formula SField (Tu)Determining the area of the target farmland to be detected (α. L + β. L)/2, wherein α is an image tangential area distortion coefficient, β is an image radial area distortion coefficient, L is the number of pixel points in the image of the target farmland to be detected, and SField (Tu)The area of the target farmland to be measured.
The traditional crop planting area measurement needs measuring tools such as measuring tapes and the like to measure the lengths of all boundaries of a field block on the spot, and then calculates the area by using a geometric method, so that the labor intensity is high, the cost is high, and the consumed time is long.
The traditional crop planting area measuring method is strong in subjectivity, mainly depends on the professional knowledge of operators, and the measurement accuracy is different due to the fact that the professional knowledge and experience of the operators are different, the area of a crop planting field with an irregular shape is difficult to calculate by a geometric method, and the stability and the reliability are difficult to maintain. The device is mature and stable, the measurement process is fixed and streamlined, the artificial influence is eliminated, the precision can reach more than 98 percent through the verification of actual production, and the stability can reach more than 99 percent.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A farmland mu measuring method based on scale configuration distortion correction is characterized by comprising the following steps:
acquiring an image of a farmland to be detected, which is shot by a shooting terminal; the image of the farmland to be detected comprises a first calibration plate and a second calibration plate; the first calibration plate and the second calibration plate are respectively positioned at the positions of two end points of a diagonal line in a farmland area to be tested;
segmenting the farmland image to be detected by using an image segmentation method, and determining a first calibration plate image, a second calibration plate image and a target farmland image to be detected;
respectively processing the first calibration plate image, the second calibration plate image and the farmland image of the target to be detected by using digital image morphology, and determining the number of pixel points in the first calibration plate image, the number of pixel points in the second calibration plate image and the number of pixel points in the farmland image of the target to be detected;
determining an image tangential area distortion coefficient according to the number of pixel points in the first calibration plate image;
determining an image radial area distortion coefficient according to the number of pixel points in the second calibration plate image;
and determining the area of the target farmland to be detected according to the number of pixel points in the image of the target farmland to be detected, the image tangential area distortion coefficient and the image radial area distortion coefficient.
2. The farmland mu measuring method based on the ruler configuration distortion correction according to claim 1, wherein the obtaining of the farmland image to be measured shot by the shooting terminal specifically comprises:
acquiring images by adopting the shooting terminal according to a trigger signal at a fixed time interval to obtain a plurality of images; the shooting terminal is an unmanned aerial vehicle provided with a visible light camera;
and splicing the plurality of images by adopting three-dimensional model generation software to obtain the image of the farmland to be detected.
3. The farmland mu measuring method based on scale configuration distortion correction according to claim 1, wherein the determining of the image tangential area distortion coefficient according to the number of the pixel points in the first calibration plate image specifically comprises:
according to formula α ═ SIs justDetermining an image tangential area distortion coefficient by using the/M;
wherein S isIs justThe area of the first calibration plate, M is the number of pixels in the image of the first calibration plate, and α is the image tangential area distortion coefficient.
4. The farmland mu measuring method based on the ruler configuration distortion correction as claimed in claim 1, wherein the determining of the image radial area distortion coefficient according to the number of the pixel points in the second calibration plate image specifically comprises:
according to formula β ═ SIIIDetermining the distortion coefficient of the radial area of the image;
wherein S isIIIThe area of the second calibration plate, N is the number of pixels in the image of the second calibration plate, and β is the distortion coefficient of the radial area of the image.
5. The farmland mu measuring method based on the ruler configuration distortion correction as claimed in claim 1, wherein the determining of the area of the target farmland to be measured according to the number of pixel points in the image of the target farmland to be measured, the image tangential area distortion coefficient and the image radial area distortion coefficient specifically comprises:
according to the formula SField (Tu)Determining the area of the target farmland to be detected as (α. L + β. L)/2;
wherein α is an image tangential area distortion coefficient, β is an image radial area distortion coefficient, L is the number of pixel points in a farmland image of a target to be detected, and SField (Tu)The area of the target farmland to be measured.
6. The method of claim 1, wherein the first calibration plate is square and the second calibration plate is regular triangle.
7. The utility model provides a mu system is surveyed in farmland based on scale configuration distortion correction which characterized in that, mu system is surveyed in farmland includes:
the system comprises a to-be-detected farmland image acquisition module, a to-be-detected farmland image acquisition module and a to-be-detected farmland image acquisition module, wherein the to-be-detected farmland image acquisition module is used for acquiring a to-be-detected farmland image shot by a shooting terminal; the image of the farmland to be detected comprises a first calibration plate and a second calibration plate; the first calibration plate and the second calibration plate are respectively positioned at the positions of two end points of a diagonal line in a farmland area to be tested;
the image determining module is used for segmenting the farmland image to be detected by using an image segmentation method and determining a first calibration plate image, a second calibration plate image and a target farmland image to be detected;
the image pixel number determining module is used for respectively processing the first calibration plate image, the second calibration plate image and the target farmland image to be detected by utilizing digital image morphology, and determining the number of pixels in the first calibration plate image, the number of pixels in the second calibration plate image and the number of pixels in the target farmland image to be detected;
the image tangential area distortion coefficient determining module is used for determining an image tangential area distortion coefficient according to the number of pixel points in the first calibration plate image;
the image radial area distortion coefficient determining module is used for determining an image radial area distortion coefficient according to the number of pixel points in the second calibration plate image;
and the target farmland area determination module is used for determining the area of the target farmland to be measured according to the number of pixel points in the target farmland image to be measured, the image tangential area distortion coefficient and the image radial area distortion coefficient.
8. The farmland mu measuring system based on the ruler configuration distortion correction of claim 7, wherein the farmland image acquisition module to be measured specifically comprises:
the shooting terminal is used for shooting images to obtain a plurality of images; the shooting terminal is an unmanned aerial vehicle provided with a visible light camera;
and the to-be-detected farmland image acquisition unit is used for splicing the multiple images by adopting three-dimensional model generation software to acquire the to-be-detected farmland image.
9. The farmland mu measurement system based on scale configuration distortion correction according to claim 7, wherein the image tangential area distortion coefficient determining module and the image radial area distortion coefficient determining module specifically comprise:
an image tangential area distortion coefficient determining unit for determining S according to the formula αIs justDetermining an image tangential area distortion coefficient by using the/M;
wherein S isIs justThe area of the first calibration plate, M is the number of pixel points in the image of the first calibration plate, and α is the image tangential area distortion coefficient;
an image radial area distortion coefficient determining unit for determining S according to the formula βIIIDetermining the distortion coefficient of the radial area of the image;
wherein S isIIIThe area of the second calibration plate, N is the number of pixels in the image of the second calibration plate, and β is the distortion coefficient of the radial area of the image.
10. The farmland mu measuring system based on the ruler configuration distortion correction as claimed in claim 7, wherein the target farmland area determination module to be measured specifically comprises:
a farmland area determination unit of the target to be measured for determining the farmland area according to the formula SField (Tu)Determining the area of the target farmland to be detected as (α. L + β. L)/2;
wherein α is an image tangential area distortion coefficient, β is an image radial area distortion coefficient, L is the number of pixel points in a farmland image of a target to be detected, and SField (Tu)The area of the target farmland to be measured.
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