CN115687850A - Method and device for calculating irrigation water demand of farmland - Google Patents

Abstract

The invention provides a method and device for calculating the irrigation water demand of farmland, belonging to the field of agricultural information technology. The method includes: performing image segmentation on the target image of the farmland according to the color feature, and determining where the green crops in the target image are located. Target area: Determine the coverage rate of green crops according to the ratio of the number of pixels in the target area to the total number of pixels in the target image; determine the irrigation water demand of the farmland according to the coverage rate and meteorological data. The invention uses the image segmentation method based on color features to calculate the crop coverage rate, calculates the crop coefficient, and then calculates the crop water demand according to the crop coefficient and reference crop evapotranspiration, changes the traditional calculation method of crop water demand, and realizes automatic crop water demand Calculate, increase productivity and save money.

Description

Calculation method and device for irrigation water demand of farmland

technical field

The invention relates to the field of agricultural information technology, in particular to a method and device for calculating irrigation water demand of farmland.

Background technique

At present, the waste of agricultural water is quite serious, and the utilization rate is low. The development of water-saving agriculture is one of the important measures taken by countries all over the world. Crop water demand is an important part of agricultural water use. Reasonable and accurate estimation of crop water demand is the basis for determining a scientific and reasonable crop irrigation system, regional irrigation water consumption, and implementation of precision irrigation.

Determining crop water demand is the basis for specifying reasonable irrigation. Generally, empirical parameters are selected. However, due to differences in planting varieties and agronomic measures, the accuracy of standard crop coefficients is not high, which in turn leads to low calculation accuracy of irrigation water demand.

Contents of the invention

The invention provides a calculation method and device for irrigation water demand of farmland, which is used to solve the defect of low calculation accuracy of irrigation water demand in the prior art.

In the first aspect, the present invention provides a method for calculating the irrigation water demand of farmland, which includes: performing image segmentation on the target image of the farmland according to the color features, and determining the target area where the green crops in the target image are located; according to the target The ratio of the number of pixels in the area to the total number of pixels in the target image determines the coverage of green crops; and determines the irrigation water demand of the farmland according to the coverage and meteorological data.

According to a method for calculating the irrigation water demand of farmland provided by the present invention, the determination of the irrigation water demand of the farmland based on the coverage rate combined with meteorological data includes: inputting the coverage rate into the crop coefficient target estimation model to obtain The crop coefficient of the green crop; according to the crop coefficient and meteorological data, determine the reference crop evapotranspiration of the farmland; determine the irrigation water demand of the farmland based on the crop coefficient and the reference crop evapotranspiration .

According to a method for calculating farmland irrigation water demand provided by the present invention, before performing image segmentation on the target image of the farmland according to the color features, and before determining the target area where the green crops in the target image are located, it also includes: using image acquisition The equipment collects the initial image of the farmland; the initial image is optimized using a noise reduction algorithm and a perspective transformation method to obtain a target image; the image acquisition device includes a mobile phone.

According to a method for calculating water demand for farmland irrigation provided by the present invention, the step of establishing the crop coefficient estimation model includes: obtaining a plurality of target image sample sets with the same number, and using one of the target image sample sets as a target image test set, Each of the remaining target image sample sets is used as a target image training set; according to the functional relationship between the coverage rate of the target image sample and the crop coefficient, a corresponding crop coefficient estimation model is established using each target image training set; using the target image test set , each of the crop coefficient estimation models is tested, and the optimal crop coefficient estimation model is used as the crop coefficient target estimation model.

According to a method for calculating the irrigation water demand of farmland provided by the present invention, the reference crop evapotranspiration of the farmland is determined according to the crop coefficient and meteorological data, and its calculation formula is:

R _n =R _ns -R _nl ;

G=0.38( _Td - _Td-1 );

γ=0.00163P/λ;

U ₂ =4.87·U _h /ln(67.8h-5.42);

ET ₀ is the reference crop evapotranspiration; T is the average temperature; Δ is the tangent slope of the temperature-saturated water vapor pressure relationship curve at T; R _n is the net solar radiation, R _ns is the static short-wave radiation, R _nl is the static long-wave radiation ; G is the soil heat flux, for the daily estimation of ET ₀ , calculate the soil heat flux on the d-th day, T _d and T _d-1 are the average temperature of the d-th day and d-1 day respectively; γ is the temperature table constant , λ is latent heat; U ₂ is the wind speed at a height of 2 meters, h is the height, U _h is the wind speed at height h; e _a is the saturated water vapor pressure; e _d is the actual water vapor pressure, where T _min is the daily minimum temperature, T _max is the daily maximum temperature, and P is the daily precipitation.

According to a method for calculating the irrigation water demand of farmland provided by the present invention, the irrigation water demand of the farmland is determined based on the crop coefficient and the reference crop evapotranspiration, and its specific formula is:

ET _c =K _c *ET ₀ ;

Among them, ET _c is the irrigation water demand, and K _c is the crop coefficient.

In a second aspect, the present invention also provides a calculation device for irrigation water demand of farmland, comprising:

The first processing module is used to perform image segmentation on the target image of the farmland according to the color feature, and determine the target area where the green crops in the target image are located;

The second processing module is used to determine the coverage rate of green crops according to the ratio of the number of pixels in the target area to the total number of pixels in the target image;

The third processing module is used to determine the irrigation water demand of the farmland according to the coverage rate and meteorological data.

In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the program, any of the above-mentioned The steps of the method for calculating the irrigation water demand of farmland.

In the fourth aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for calculating the irrigation water demand of farmland as described in any one of the above-mentioned methods is realized. step.

In the fifth aspect, the present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method for calculating the irrigation water demand of farmland as described in any one of the above are realized

The invention uses the image segmentation method based on color features to calculate the crop coverage rate, calculates the crop coefficient, and then calculates the crop water demand according to the crop coefficient and reference crop evapotranspiration, changes the traditional calculation method of crop water demand, and realizes automatic crop water demand Calculate, increase productivity and save money.

Description of drawings

In order to more clearly illustrate the present invention or the technical solutions in the prior art, the accompanying drawings that need to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings in the following description are the present invention. For some embodiments of the invention, those skilled in the art can also obtain other drawings based on these drawings without creative effort.

Fig. 1 is one of the schematic flow charts of the irrigation water demand calculation method of the farmland provided by the present invention;

Fig. 2 is the second schematic flow chart of the irrigation water demand calculation method of the farmland provided by the present invention;

Fig. 3 is the structural representation of the calculation device of the irrigation water demand of farmland provided by the present invention;

Fig. 4 is a schematic structural diagram of an electronic device provided by the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention , but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

It should be noted that, in the description of the embodiments of the present invention, the terms "comprising", "comprising" or any other variant thereof are intended to cover a non-exclusive inclusion, so that a process, method, article or device comprising a series of elements Not only those elements are included, but also other elements not expressly listed or inherent in such process, method, article or apparatus. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element. The orientation or positional relationship indicated by the terms "upper", "lower", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must Having a particular orientation, being constructed and operating in a particular orientation, and therefore not to be construed as limiting the invention. Unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" should be interpreted in a broad sense, for example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection, It can also be an electrical connection; it can be a direct connection, or an indirect connection through an intermediary, or an internal communication between two components. Those of ordinary skill in the art can understand the specific meanings of the above terms in the present invention according to specific situations.

The terms "first", "second" and the like in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application can be practiced in sequences other than those illustrated or described herein, and that references to "first," "second," etc. distinguish Objects are generally of one type, and the number of objects is not limited. For example, there may be one or more first objects.

In recent years, computer vision technology has been widely used in agricultural engineering. Computer vision includes target detection, image processing, etc., using cameras instead of human eyes to identify and measure targets, and further image processing. Studies have shown that suitable computer vision algorithms can be used for many tasks, such as fruit grading and monitoring, grain germplasm inspection and evaluation, and plant growth status monitoring. The advent of computer vision provides new avenues for non-destructive analysis of crop parameters. It has realized the recognition of the color and biological characteristics of crops through machine vision technology, and can accurately detect and calculate the trait parameters of crops such as bald rate, ear row number, and row grain number. Based on computer vision technology, the invention provides a calculation method for irrigation water demand of farmland, which is used to guide rational irrigation, and has the advantages of online non-destructive, low cost and high implementability.

The method and device for calculating the irrigation water demand of farmland provided by the embodiments of the present invention will be described below with reference to FIGS. 1-4 .

Fig. 1 is one of the flow diagrams of the irrigation water demand calculation method of farmland provided by the present invention, as shown in Fig. 1, including but not limited to the following steps:

Step 101: Segment the target image of the farmland according to the color features, and determine the target area where the green crops in the target image are located.

The green crops in the farmland are clearly distinguished from the background land in color, and the segmentation of different regions can be achieved through the color features of different regions in the target image. For example, an area with a large proportion of green components in the target image is taken as the target area. The background can be removed from the target image and the green crop image can be preserved by image segmentation technology.

Step 102: Determine the coverage rate of green crops according to the ratio of the number of pixels in the target area to the total number of pixels in the target image.

After image segmentation based on color features is applied, the ratio of the number of pixels in the target area (that is, the number of green crop pixels) to the total number of pixels in the target image is expressed as:

PGC = px_crop/px_img;

Among them, PGC is the coverage rate of green crops, px_crop is the number of pixels in the target area, and px_img is the total number of pixels in the target image.

Step 103: Determine the irrigation water demand of the farmland according to the coverage rate and meteorological data.

Among them, meteorological data include temperature, solar radiation, average temperature, wind speed, air pressure and other data. The crop coefficient of the green crop can be deduced based on the coverage rate, and the reference crop evapotranspiration is calculated according to the PM (Penman-Monteith) formula in combination with the meteorological data.

Further, the crop water requirement is calculated according to the crop coefficient and the reference crop evapotranspiration, so as to determine the irrigation water requirement.

The invention uses the image segmentation method based on color features to calculate the crop coverage rate, calculates the crop coefficient, and then calculates the crop water demand according to the crop coefficient and reference crop evapotranspiration, changes the traditional calculation method of crop water demand, and realizes automatic crop water demand Calculate, increase productivity and save money.

Fig. 2 is the second schematic flow chart of the calculation method of the irrigation water demand of the farmland provided by the present invention, as shown in Fig. 2, the calculation method of the irrigation water demand of the farmland provided by the invention comprises: initial image acquisition unit, image processing unit, crop coefficient Calculation unit, reference crop evapotranspiration calculation unit and irrigation water demand calculation unit. In order to describe the technical solution of the present invention more clearly, the technical solution of the present invention will be described below in conjunction with specific embodiments.

Based on the content of the above-mentioned embodiments, as an optional embodiment, the method for calculating the irrigation water demand of the farmland provided by the present invention is to perform image segmentation on the target image of the farmland according to the color features, and determine the green crops in the target image Before the target area, it also includes: using an image acquisition device to collect an initial image of the farmland; using a noise reduction algorithm and a perspective transformation method to optimize the initial image to obtain a target image.

Wherein, the image acquisition device in the initial image acquisition unit includes a mobile phone. In the present invention, the mobile phone is used to take photos as the source of the initial image, and the initial image is collected multiple times during the entire growth cycle of the crop.

The present invention proposes a method for calculating the water demand for irrigation of farmland, using low-cost mobile phone photography as the main image acquisition device, realizing that the crop coefficient can be automatically calculated only by hand-held photography, reducing the cost of photography equipment, and increasing the way to obtain data flexibility.

Optionally, the image processing unit of the present invention optimizes the initial image by using a noise reduction algorithm and a perspective transformation method to obtain a target image. The steps of optimizing the initial image by the image processing unit of the present invention will be described below.

The noise of the initial image collected by the present invention mainly comes from the collection process and the transmission process. The noise source of the present invention is mainly Gaussian noise. For the Gaussian noise existing in the initial image, the present invention will use a Gaussian filter to process the existing Gaussian noise. As a widely used smoothing algorithm, the Gaussian filter can reduce image noise very well, and at the same time, the filter will not eliminate image details due to noise removal.

For the image T (x) with noise collected in the present invention, its additive noise is represented by following formula:

T(x)=S(x)+η(x), x∈Ω

T(x) represents the initial image with noise, S(x) represents the initial image without noise, η(x) is the added noise term; Ω is the total number of pixels contained in the entire initial image. After removing the noise term, the initial image without noise can be obtained.

Use exponential smoothing instead of smoothing coefficients in a Gaussian filter:

Among them, α is the smoothing coefficient, and t represents time.

The present invention uses a perspective transformation method to solve three-dimensional distortion. Perspective transformation is the process of transforming three-dimensional images, and affine transformation is the process of transforming two-dimensional images. After obtaining the coordinates of a set of four points in the distorted image and a set of four points in the target image, the perspective transformation is applied to correct the distorted image. The transformation matrix of perspective transformation is calculated through two sets of coordinate points, and then the entire original image is transformed to realize image correction.

Transform the world coordinate system and the camera coordinate system, the world coordinates are (X, Y, Z), the camera coordinates are (X _C , Y _C , Z _C ), there is a rotation transformation matrix R and The displacement transformation matrix T.

The conversion formula from the applied world coordinate system to the camera coordinate system is:

The coordinates of the four points in the distorted image of the present invention are the four vertices of the standard frame of the reference object, and the coordinates of the four points in the target image are the four vertices of the image. Since the viewing angle is larger and the transformed image is stretched in the height direction, the corrected image has reduced contrast, but its basic color characteristics do not change much.

Due to the complex natural conditions of the image collection environment, it is easy to generate image noise and distortion in the process of image collection. The present invention well weakens the influence of environmental factors on the subsequent image segmentation through the denoising algorithm and perspective distortion correction.

Since the light intensity changes and shadows in the image will affect the image segmentation quality and have a great impact on the subsequent crop recognition, the accurate extraction of crop color features is the key to image segmentation. The color distribution characteristics of crops are analyzed to accurately segment green crops from soil background images. The colors of crop stems and leaves are mainly green and yellow in different degrees, so the present invention selects a plurality of color images under RGB space conditions for image segmentation.

By comparing the crop color models, the green G component accounts for the largest proportion, the red R component takes the second place, and the blue B component is the smallest. Therefore, the component operators 2G-B and 2G-R are used as color operators for image processing. In order to preserve the color information of the original image as much as possible, and the component operator 0.299*R+0.587*G+0.114*B of the default color image converted to grayscale image is also in line with the proportion of each component. In summary, the operator fusion formula for applying RGB space is:

The 2G-B, 2G-R and 0.299*R+0.587*G+0.114*B operators alone or pairwise fusion operators cannot distinguish between green crops and background. In the image fused by the three operators, the difference between the gray value of the green crop and the background is the largest, and the grass and the background can be distinguished to achieve the segmentation effect. Because the histogram distribution of grayscale images has a peak trend, the three color difference features of green crop images are used as image operators to solve the problem of converting the three-dimensional processing of color images into one-dimensional grayscale images.

Based on the content of the above embodiment, as an optional embodiment, the present invention also provides a method for calculating the irrigation water demand of farmland, wherein the step of establishing the crop coefficient estimation model includes: acquiring multiple target images with the same number Sample set, one of the target image sample sets is used as the target image test set, and each of the remaining target image sample sets is used as the target image training set; according to the functional relationship between the coverage rate of the target image sample and the crop coefficient, each target image is used to train Set up a corresponding crop coefficient estimation model; use the target image test set to test each of the crop coefficient estimation models, and use the optimal crop coefficient estimation model as the crop coefficient target estimation model.

Specifically, in the present invention, 800 target images are obtained, which are equally divided into 4 target image sample sets, namely S1, S2, S3 and S4. Among them, S1, S2, and S3 are the target image training set, and S4 is the target image test set.

Establish a crop coefficient estimation model for each target image training set S1, S2, S3, and use the target image test set S4 for testing, and select the crop coefficient estimation model with the highest accuracy as the crop coefficient target estimation model.

Optionally, the crop coefficient target estimation model is a nonlinear relationship model between coverage rate and crop coefficient, and the specific formula is:

K _C ＝2.137PGC ² -1.24PGC+0.2091

Among them, K _C is the crop coefficient of green crops, and PGC is the coverage rate of green crops.

In view of the noise and distortion in the image data obtained by mobile phones, the present invention uses denoising and perspective transformation methods to optimize the picture, and finally uses an image segmentation algorithm based on color space features to calculate the crop coverage rate, establishes a crop coefficient estimation model, and realizes The inversion of crop coefficient, after calculating the reference crop evapotranspiration, further calculates the crop water requirement.

Based on the content of the above-mentioned embodiments, as an optional embodiment, the method for calculating the irrigation water demand of the farmland provided by the present invention, the determination of the irrigation water demand of the farmland based on the coverage rate combined with meteorological data includes: Input the coverage rate into the crop coefficient target estimation model to obtain the crop coefficient of the green crop; determine the reference crop evapotranspiration of the farmland according to the crop coefficient and meteorological data; based on the crop coefficient and the reference Crop evapotranspiration, which determines the irrigation water requirement of the field in question.

Wherein, according to the crop coefficient and meteorological data, the specific formula for determining the reference crop evapotranspiration of the farmland is:

R _n =R _ns -R _nl ;

G=0.38( _Td - _Td-1 );

γ=0.00163P/λ;

U ₂ =4.87·U _h /ln(67.8h-5.42);

ET ₀ is the reference crop evapotranspiration; T is the average temperature; Δ is the tangent slope of the temperature-saturated water vapor pressure relationship curve at T; R _n is the net solar radiation, R _ns is the static short-wave radiation, R _nl is the static long-wave radiation ; G is the soil heat flux, for the daily estimation of ET ₀ , calculate the soil heat flux on the d-th day, T _d and T _d-1 are the average temperature of the d-th day and d-1 day respectively; γ is the temperature table constant , λ is latent heat; U ₂ is the wind speed at a height of 2 meters, h is the height, U _h is the wind speed at height h; e _a is the saturated water vapor pressure; e _d is the actual water vapor pressure, where T _min is the daily minimum temperature, T _max is the daily maximum temperature, and P is the daily precipitation.

ET _c can be calculated from ET _o and K _C , the calculation formula is as follows:

ET _c ＝K _c *ET ₀

Among them, ET ₀ is the crop water requirement.

FIG. 3 is a schematic structural diagram of a calculation device for irrigation water demand provided by the present invention. As shown in FIG. 3 , the device includes: a first processing module 301 , a second processing module 302 and a third processing module 303 .

Wherein, the first processing module 301 is used to perform image segmentation on the target image of the farmland according to the color feature, and determine the target area where the green crops in the target image are located;

The second processing module 302 is used to determine the coverage rate of green crops according to the ratio of the number of pixels in the target area to the total number of pixels in the target image;

The third processing module 303 is configured to determine the irrigation water demand of the farmland according to the coverage rate and meteorological data.

The invention uses the image segmentation method based on color features to calculate the crop coverage rate, calculates the crop coefficient, and then calculates the crop water demand according to the crop coefficient and reference crop evapotranspiration, changes the traditional calculation method of crop water demand, and realizes automatic crop water demand Calculate, increase productivity and save money.

It should be noted that, the device for calculating the water demand for farmland irrigation provided by the embodiments of the present invention may execute the method for calculating the water demand for farmland irrigation described in any of the above-mentioned embodiments during specific operation, which will not be described in detail in this embodiment.

FIG. 4 is a schematic structural diagram of an electronic device provided by the present invention. As shown in FIG. 4, the electronic device may include: a processor (processor) 410, a communication interface (Communications Interface) 420, a memory (memory) 430 and a communication bus 440, Wherein, the processor 410 , the communication interface 420 , and the memory 430 communicate with each other through the communication bus 440 . The processor 410 can call the logic instructions in the memory 430 to execute the method for calculating the irrigation water demand of the farmland. The method includes: performing image segmentation on the target image of the farmland according to the color features, and determining where the green crops in the target image are located. Target area: Determine the coverage rate of green crops according to the ratio of the number of pixels in the target area to the total number of pixels in the target image; determine the irrigation water demand of the farmland according to the coverage rate and meteorological data.

In addition, the above logic instructions in the memory 430 may be implemented in the form of software function units and be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes. .

On the other hand, the present invention also provides a computer program product, the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer When executing, the computer can execute the method for calculating the irrigation water demand of the farmland provided by the above-mentioned embodiments, the method includes: performing image segmentation on the target image of the farmland according to the color features, and determining the target where the green crops in the target image are located area; determine the coverage rate of green crops according to the ratio of the number of pixels in the target area to the total number of pixels in the target image; determine the irrigation water demand of the farmland according to the coverage rate and meteorological data.

In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it is implemented to perform the method for calculating the irrigation water demand of farmland provided by the above-mentioned embodiments , the method includes: performing image segmentation on a target image of farmland according to color features, and determining a target area where green crops in the target image are located; The ratio of the numbers determines the coverage of green crops; according to the coverage and meteorological data, the water demand for irrigation of the farmland is determined.

The device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network elements. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. It can be understood and implemented by those skilled in the art without any creative effort.

Through the above description of the implementations, those skilled in the art can clearly understand that each implementation can be implemented by means of software plus a necessary general hardware platform, and of course also by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic discs, optical discs, etc., including several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent replacements are made to some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims (10)

1. A method for calculating the irrigation water demand of farmland, characterized in that, comprising: Carry out image segmentation to the target image of the farmland according to the color feature, and determine the target area where the green crops in the target image are located; Determine the coverage rate of green crops according to the ratio of the number of pixels in the target area to the total number of pixels in the target image; According to the coverage rate and meteorological data, the water demand for irrigation of the farmland is determined. 2. The method for calculating the water demand for irrigation of farmland according to claim 1, wherein the determination of the water demand for irrigation of the farmland based on the coverage rate in combination with meteorological data includes: Input the coverage rate into the crop coefficient target estimation model to obtain the crop coefficient of the green crop; Determine the reference crop evapotranspiration of the farmland according to the crop coefficient and meteorological data; Based on the crop coefficient and the reference crop evapotranspiration, the irrigation water demand of the farmland is determined. 3. the irrigation water demand calculation method of farmland according to claim 1, is characterized in that, before the target image of farmland is image-segmented according to color feature, before the target area where the green crop in the target image is determined, Also includes: Using an image acquisition device to acquire an initial image of the farmland; Optimizing the initial image by using a noise reduction algorithm and a perspective transformation method to obtain a target image; The image acquisition device includes a mobile phone. 4. the irrigation water demand calculation method of farmland according to claim 2, is characterized in that, the step of setting up described crop coefficient estimation model comprises: Obtain multiple target image sample sets with the same number, use one of the target image sample sets as a target image test set, and each of the remaining target image sample sets as a target image training set; According to the functional relationship between the coverage rate of the target image sample and the crop coefficient, use each target image training set to establish a corresponding crop coefficient estimation model; Each of the crop coefficient estimation models is tested by using the target image test set, and the optimal crop coefficient estimation model is used as the crop coefficient target estimation model. 5. the irrigation water demand calculation method of farmland according to claim 2, is characterized in that, described according to described crop coefficient and meteorological data, determines the reference crop evapotranspiration of described farmland, and its computing formula is:

R _n =R _ns -R _nl ; G=0.38( _Td - _Td-1 ); γ=0.00163P/λ; U ₂ =4.87·U _h /ln(67.8h-5.42);

ET ₀ is the reference crop evapotranspiration; T is the average temperature; Δ is the tangent slope of the temperature-saturated water vapor pressure relationship curve at T; R _n is the net solar radiation, R _ns is the static short-wave radiation, R _nl is the static long-wave radiation ; G is the soil heat flux, for the daily estimation of ET ₀ , calculate the soil heat flux on the d-th day, T _d and T _d-1 are the average temperature of the d-th day and d-1 day respectively; γ is the temperature table constant , λ is latent heat; U ₂ is the wind speed at a height of 2 meters, h is the height, U _h is the wind speed at height h; e _a is the saturated water vapor pressure; e _d is the actual water vapor pressure, where T _min is the daily minimum temperature, T _max is the daily maximum temperature, and P is the daily precipitation. 6. The irrigation water demand calculation method of farmland according to claim 2, characterized in that, based on the crop coefficient and the reference crop evapotranspiration, determine the irrigation water demand of the farmland, and its specific formula is : ET _c =K _c *ET ₀ ; Among them, ET _c is the irrigation water demand, and K _c is the crop coefficient. 7. An irrigation water demand calculation device for farmland, characterized in that it comprises: The first processing module is used to perform image segmentation on the target image of the farmland according to the color feature, and determine the target area where the green crops in the target image are located; The second processing module is used to determine the coverage rate of green crops according to the ratio of the number of pixels in the target area to the total number of pixels in the target image; The third processing module is used to determine the irrigation water demand of the farmland according to the coverage rate and meteorological data. 8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, characterized in that, when the processor executes the computer program, the computer program according to claim 1 is realized. The steps of the method for calculating the irrigation water demand of farmland described in any one of 1 to 6. 9. A non-transitory computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the irrigation water demand of the farmland according to any one of claims 1 to 6 is realized Calculation method steps. 10. A computer program product, comprising a computer program, characterized in that, when the computer program is executed by a processor, the steps of the method for calculating the irrigation water demand of the farmland according to any one of claims 1 to 6 are realized.

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