Crop evapotranspiration prediction method, system, equipment, medium and product

Abstract

The invention discloses a crop evapotranspiration prediction method, system and device, a medium and a product, and relates to the field of crop evapotranspiration, and the method comprises the steps: obtaining the input data of crop evapotranspiration in a research area in real time; the input data comprises meteorological factors, crop factors and soil water potential; predicting crop evapotranspiration by adopting an optimized artificial neural network model based on the input data; the optimized artificial neural network model is obtained by optimizing the artificial neural network model through a food-feeding fish foraging optimization algorithm. According to the method, the soil water potential factor is considered, the crop evapotranspiration is predicted by adopting the optimized artificial neural network model, and the crop evapotranspiration prediction efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of crop evapotranspiration, and in particular to a crop evapotranspiration prediction method, system, equipment, medium and product. Background Art

[0002] Evapotranspiration is a complex and important process in the ecosystem, including transpiration of vegetation, evaporation of soil moisture, and evaporation of intercepted precipitation or dew. As the most important component of crop water requirement information, crop evaporation will greatly affect crop growth and yield. Accurate quantification of evaporation has important guiding significance for precision irrigation, drought prediction, and assessment of the impact of climate change on water cycle. Crop evaporation is affected by many factors, including meteorological factors, soil factors, crop factors, etc.

[0003] Evapotranspiration prediction models can be divided into models based on physical processes and models based on statistics. Models based on physical processes focus on using physical and mathematical equations to simulate evapotranspiration, parameterizing evapotranspiration through surface energy balance constraints and characterization of water vapor diffusion processes. Commonly used physical models include the Penman-Monteith formula. Models based on statistics can be divided into empirical models and machine learning models, which mainly simulate evapotranspiration by establishing linear or nonlinear relationships between measured evapotranspiration and various input variables. However, both usually select soil moisture as one of the influencing factors.

[0004] Compared with soil moisture, soil water potential not only considers the amount of water in the soil, but also the energy state of water, which can effectively describe water migration. Moreover, under abiotic stresses such as drought, salinity, and freeze-thaw, soil water potential can effectively reflect the stress conditions, which will show great advantages in predicting crop evapotranspiration under extreme weather conditions. Summary of the invention

[0005] The purpose of this application is to provide a crop evapotranspiration prediction method, system, equipment, medium and product, which takes into account soil water potential factors and uses an optimized artificial neural network model to predict crop evapotranspiration, thereby improving the efficiency and accuracy of crop evapotranspiration prediction.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a crop evapotranspiration prediction method, comprising:

[0008] Acquire input data of crop evapotranspiration in the study area in real time; the input data includes meteorological factors, crop factors and soil water potential;

[0009] Based on the input data, an optimized artificial neural network model is used to predict crop evapotranspiration; the optimized artificial neural network model is obtained by optimizing the artificial neural network model using a piranha foraging optimization algorithm.

[0010] In a second aspect, the present application provides a crop evapotranspiration prediction system, comprising:

[0011] An input data acquisition module is used to acquire input data of crop evapotranspiration in the study area in real time; the input data includes meteorological factors, crop factors and soil water potential;

[0012] The prediction module is connected to the input data acquisition module and is used to predict crop evapotranspiration based on the input data using an optimized artificial neural network model; the optimized artificial neural network model is obtained by optimizing the artificial neural network model using a piranha foraging optimization algorithm.

[0013] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned crop evapotranspiration prediction method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned crop evapotranspiration prediction method when executed by a processor.

[0015] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the above-mentioned crop evapotranspiration prediction method when executed by a processor.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects:

[0017] The present application provides a crop evapotranspiration prediction method, system, equipment, medium and product, which realizes the real-time performance of the crop evapotranspiration prediction method by acquiring the input data of crop evapotranspiration in the study area in real time; optimizes the artificial neural network model by using the piranha foraging optimization algorithm to improve the efficiency and accuracy of the artificial neural network model prediction; takes factors such as soil water potential as input data and inputs them into the optimized artificial neural network model to predict crop evapotranspiration, taking into account the amount of water in the soil and the energy state of water, effectively describing the water migration, and the soil water potential can effectively reflect the stress situation under abiotic stresses such as drought, salinity and freeze-thaw, thereby improving the accuracy of crop evapotranspiration prediction under extreme weather conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 This is an application environment diagram of a crop evapotranspiration prediction method in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of a process flow of a crop evapotranspiration prediction method provided in one embodiment of the present application;

[0021] Figure 3 A schematic diagram of a process of optimizing an artificial neural network model using a piranha foraging optimization algorithm in a crop evapotranspiration prediction method provided in an embodiment of the present application;

[0022] Figure 4 A schematic diagram of a process for obtaining the preliminary position of each piranha at time t+1 based on the position of each piranha at time t, the optimal position of the piranha at time t, the hunger degree of the piranha population at time t, and the bloodiness degree of the piranha population at time t in a crop evapotranspiration prediction method provided in an embodiment of the present application;

[0023] Figure 5 A schematic diagram of functional modules of a crop evapotranspiration system provided in another embodiment of the present application;

[0024] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In order to make the purpose, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0027] The crop evapotranspiration prediction method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the input data to the server 104. After the server 104 receives the input data, for the input data, the server 104 uses the optimized artificial neural network model to predict the crop evapotranspiration based on the input data; the optimized artificial neural network model is obtained by optimizing the artificial neural network model by the piranha foraging optimization algorithm. The server 104 can feed back the obtained crop evapotranspiration prediction result to the terminal 102. In addition, in some embodiments, the crop evapotranspiration prediction method can also be implemented by the server 104 or the terminal 102 alone.

[0028] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0029] In an exemplary embodiment, Figure 2 As shown, a crop evapotranspiration prediction method is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 202. Among them:

[0030] Step 201, obtaining input data of crop evapotranspiration in a study area in real time; the input data includes meteorological factors, crop factors and soil water potential.

[0031] Step 202: Based on the input data, the crop evapotranspiration is predicted using an optimized artificial neural network model; the optimized artificial neural network model is obtained by optimizing the artificial neural network model using a piranha foraging optimization algorithm.

[0032] In another exemplary embodiment of the present application, before step 202, the input data is normalized.

[0033] In another exemplary embodiment of the present application, the meteorological factors include solar radiation, temperature, humidity and precipitation; and the crop factors include leaf area index and vegetation height.

[0034] The artificial neural network (ANN) model is initialized, specifically including: the input layer of the ANN is the input data; the number of hidden layers and nodes of the ANN is initialized, and the weights and bias of the network are randomly initialized; the output layer of the ANN is the predicted crop evapotranspiration.

[0035]

[0036] Among them, y p is the crop evapotranspiration predicted by ANN, x h is the input of the neuron, w h is the weight, h is the hth input layer, f is the activation function, and H is the number of input layers.

[0037] Initialize the Piranha Foraging Optimization Algorithm (PFOA), specifically including: taking the weights and biases of the ANN model as the piranha positions, randomly generating a group of initial populations, and one piranha position represents a group of weights and biases.

[0038] Define the fitness function to evaluate the MSE performance of Piranha: Among them, Y i is the position of the i-th piranha, is the optimal position of the i-th piranha, i is the i-th piranha, and n is the number of piranhas.

[0039] Define the predation intensity parameter F:

[0040]

[0041] d i =x prey -x i ;

[0042] Z i =[x i (t)-x i+1 (t)] 2 ;

[0043] Among them, F i is the predation intensity parameter of the location of the ith piranha, d i is the distance between the position of the ith piranha and the optimal position of the piranha (optimal solution), Z i is the source intensity perceived by the i-th piranha, β2 represents a random number between 0 and 1, and x i represents the position of the i-th piranha, x prey represents the optimal position of the piranha, t is the tth moment, xi (t) represents the position of the ith piranha at time t, x i+1 (t) represents the position of the i+1th piranha at time t.

[0044] Define a nonlinear parameter control strategy to use S parameters to control the time-varying random process and prevent the population from converging prematurely: Among them, S is the control parameter, Max_iter represents the maximum number of iterations, and C is a constant (the default is 5). represents the product of value and variable, and T is the number of iterations.

[0045] Define a reverse escape search strategy and use E to change the search direction of the population to avoid falling into a local solution.

[0046] Among them, E is a parameter that changes the search direction of the population, and β3 is a random number between 0 and 1.

[0047] In another exemplary embodiment of the present application, the piranha foraging optimization algorithm and the artificial neural network model are initialized respectively, such as Figure 3 As shown in the figure, the piranha foraging optimization algorithm optimizes the artificial neural network model, specifically including:

[0048] A group of piranha populations are randomly generated, and the initial parameters of the artificial neural network model are used as the position of each piranha at time t; the initial parameters include weights and biases.

[0049] According to the position of each piranha at time t, the fitness function value of each piranha at time t is calculated, and based on the fitness function value of each piranha at time t, the optimal position of the piranha at time t is obtained.

[0050] For the iteration at time t, the hunger degree of the piranha population at time t and the bloodiness degree of the piranha population at time t are randomly generated.

[0051] Based on the position of each piranha at time t, the optimal position of the piranha at time t, the hunger degree of the piranha population at time t, and the bloodiness degree of the piranha population at time t, the preliminary position of each piranha at time t+1 is obtained.

[0052] The initial fitness function value of each piranha at time t+1 is calculated according to the initial position of each piranha at time t+1; and based on the initial fitness function value of each piranha at time t+1, the initial optimal position of the piranha at time t+1 and the survival rate of the piranha at time t+1 are obtained.

[0053] When the survival rate is higher than or equal to the set threshold, the initial position of each piranha at time t+1 is used as the position of each piranha at time t+1, and the initial optimal position of the piranha at time t+1 is used as the optimal position of the piranha at time t+1.

[0054] When the survival rate is lower than a set threshold, based on the optimal position of the piranha at time t, the survival strategy of the piranha population is adopted to obtain the position of each piranha at time t+1; and according to the position of each piranha at time t+1, the fitness function value of each piranha in the piranha population at time t+1 is calculated; based on the fitness function value of each piranha in the piranha population at time t+1, the optimal position of the piranha at time t+1 is obtained.

[0055] Determine whether time t reaches the set time. If not, iterate at time t+1; if so, optimize the artificial neural network model according to the optimal position of the piranha at time t+1.

[0056] The optimized artificial neural network model is trained using historical input data and measured values ​​of crop evapotranspiration. Specifically, 80% of the historical input data is used for training, 10% for verification, and 10% for testing.

[0057] Specifically, the calculation formula of the piranha survival rate SR is:

[0058]

[0059] fitness(i) is the fitness function value of the i-th piranha, fitness Max is the maximum value of the fitness function during the traversal process, fitness Min It is the minimum value of the fitness function during the traversal process.

[0060] The calculation formula for the survival strategy of the piranha population is:

[0061]

[0062] Among them, x i (t+1) represents the position of the i-th piranha at time t+1, x prey (t) represents the optimal position of the piranha at time t, where represents the position of the C1th piranha randomly selected from the piranha population at time t, represents the position of the C2th piranha randomly selected from the piranha population at time t, Represents the position of the C3th piranha randomly selected from the piranha population at time t, C1≠C2≠C3.

[0063] In another exemplary embodiment of the present application, based on the position of each piranha at time t, the optimal position of the piranha at time t, the hunger degree of the piranha population at time t, and the bloodiness degree of the piranha population at time t, the preliminary position of each piranha at time t+1 is obtained, such as Figure 4 As shown, specifically including:

[0064] It is determined whether the hunger degree of the piranha population at time t is less than the critical probability of the piranha being in a hungry state. The critical probability of the piranha being in a hungry state is M=0.5.

[0065] If so, based on the position of each piranha at time t, the scavenging foraging mode is adopted to obtain the initial position of each piranha at time t+1.

[0066] The calculation formula of the detritivorous foraging mode is:

[0067] Among them, x i (t+1) represents the initial position of the i-th piranha at time t+1, x i (t) represents the position of the i-th piranha at time t, γ2 is a random number uniformly distributed in [-1,1], and E is a parameter that changes the search direction of the population. Represents the position of the C1th piranha randomly selected from the piranha population at time t, C1≠i.

[0068] If not, it is determined whether the bloodiness of the piranha population at time t is less than the critical probability of the piranha attacking bloodily. The critical probability of the piranha attacking bloodily is N=0.75.

[0069] If so, based on the position of each piranha at time t and the optimal position of the piranha at time t, a local group attack mode is adopted to obtain the initial position of each piranha at time t+1.

[0070] The calculation formula of the local group attack mode is:

[0071] Among them, x i (t+1) represents the initial position of the i-th piranha at time t+1, x i (t) represents the position of the i-th piranha at time t, pc represents a randomly generated integer, which is the total number of piranhas / 2; k is the number of piranhas participating in the attack, L k (t) represents the proportion of attacks by the local population that first attacks at time t, L∈X, X is the number of randomly generated piranhas, x prey (t) represents the optimal position of the piranha at time t, and γ1 is a random number uniformly distributed in [-2,2].

[0072] If not, based on the optimal position of the piranhas at time t, a bloodthirsty cluster attack mode is adopted to obtain the initial position of each piranha at time t+1.

[0073] The calculation formula of the bloodthirsty cluster attack mode is:

[0074]

[0075] Among them, x i (t+1) represents the initial position of the i-th piranha at time t+1, x prey (t) represents the optimal position of the piranha at time t, γ1 is a random number uniformly distributed in [-2,2], γ2 is a random number uniformly distributed in [-1 / 2,1 / 2], β4 is a random number between 0 and 1, G is the piranha foraging coefficient, G≥5.

[0076] In another exemplary embodiment of the present application, the crop evapotranspiration prediction method further includes: using a model interpretability algorithm (SHapley Additive exPlanations, SHAP) to obtain the contribution of each input data to crop evapotranspiration. The calculation formula of SHAP is:

[0077] Among them, f(a) is the original model, g(a') is the simplified explanation model of input a', a'∈{0,1} U , a and a' through a=h a (a'), U represents the number of input features, is the characteristic attribute of feature u, is the output value of the model when all features are 0.

[0078] Based on the same inventive concept, the present application embodiment also provides a crop evapotranspiration prediction system, such as Figure 5 As shown, including:

[0079] The input data acquisition module 301 is used to acquire the input data of crop evapotranspiration in the study area in real time; the input data includes meteorological factors, crop factors and soil water potential.

[0080] The prediction module 302 is connected to the input data acquisition module 301 and is used to predict crop evapotranspiration based on the input data using an optimized artificial neural network model; the optimized artificial neural network model is obtained by optimizing the artificial neural network model using a piranha foraging optimization algorithm.

[0081] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store video tag processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a crop evapotranspiration prediction method is implemented.

[0082] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0083] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above-mentioned crop evapotranspiration prediction method is implemented.

[0084] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above-mentioned crop evapotranspiration prediction method when executed by a processor.

[0085] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0086] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0087] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0088] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A crop evapotranspiration prediction method, characterized in that: The crop evapotranspiration prediction method comprises: Acquire input data of crop evapotranspiration in the study area in real time; the input data includes meteorological factors, crop factors and soil water potential; Based on the input data, an optimized artificial neural network model is used to predict crop evapotranspiration; the optimized artificial neural network model is obtained by optimizing the artificial neural network model using a piranha foraging optimization algorithm.

2. The crop evapotranspiration prediction method according to claim 1, characterized in that: The crop evapotranspiration prediction method further includes: normalizing the input data.

3. The crop evapotranspiration prediction method according to claim 1, characterized in that: The meteorological factors include solar radiation, temperature, humidity and precipitation; the crop factors include leaf area index and vegetation height.

4. The crop evapotranspiration prediction method according to claim 1, characterized in that: The process of optimizing the artificial neural network model by the piranha foraging optimization algorithm includes: A group of piranha populations are randomly generated, and the initial parameters of the artificial neural network model are used as the position of each piranha at time t; the initial parameters include weights and biases; Calculate the fitness function value of each piranha at time t according to the position of each piranha at time t, and obtain the optimal position of the piranha at time t based on the fitness function value of each piranha at time t; For the iteration at time t, randomly generate the hunger degree of the piranha population at time t and the bloodiness degree of the piranha population at time t; Based on the position of each piranha at time t, the optimal position of the piranha at time t, the hunger degree of the piranha population at time t, and the bloodiness degree of the piranha population at time t, the preliminary position of each piranha at time t+1 is obtained; Calculate the initial fitness function value of each piranha at time t+1 according to the initial position of each piranha at time t+1; and obtain the initial optimal position of the piranha at time t+1 and the survival rate of the piranha at time t+1 based on the initial fitness function value of each piranha at time t+1; When the survival rate is higher than or equal to the set threshold, the initial position of each piranha at time t+1 is used as the position of each piranha at time t+1, and the initial optimal position of the piranha at time t+1 is used as the optimal position of the piranha at time t+1; When the survival rate is lower than the set threshold, based on the optimal position of the piranha at time t, the survival strategy of the piranha population is adopted to obtain the position of each piranha at time t+1; and the fitness function value of each piranha in the piranha population at time t+1 is calculated according to the position of each piranha at time t+1; based on the fitness function value of each piranha in the piranha population at time t+1, the optimal position of the piranha at time t+1 is obtained; Determine whether time t reaches the set time. If not, iterate at time t+1; if so, optimize the artificial neural network model according to the optimal position of the piranha at time t+1.

5. The crop evapotranspiration prediction method according to claim 4, characterized in that: Based on the position of each piranha at time t, the optimal position of the piranha at time t, the hunger degree of the piranha population at time t, and the bloodiness degree of the piranha population at time t, the preliminary position of each piranha at time t+1 is obtained, including: Determine whether the hunger level of the piranha population at time t is less than the critical probability of the piranha being in a hungry state; If so, based on the position of each piranha at time t, the scavenging foraging mode is adopted to obtain the initial position of each piranha at time t+1; If not, then determine whether the bloodiness of the piranha population at time t is less than the critical probability of a bloody attack by piranhas; If so, based on the position of each piranha at time t and the optimal position of the piranha at time t, a local group attack mode is adopted to obtain the initial position of each piranha at time t+1; If not, based on the optimal position of the piranhas at time t, a bloodthirsty cluster attack mode is adopted to obtain the initial position of each piranha at time t+1.

6. The crop evapotranspiration prediction method according to claim 1, characterized in that: The crop evapotranspiration prediction method further includes: using a model interpretability algorithm to obtain the contribution of each input data to the crop evapotranspiration.

7. A crop evapotranspiration prediction system, using the crop evapotranspiration prediction method according to any one of claims 1 to 6, characterized in that: The crop evapotranspiration prediction system comprises: An input data acquisition module is used to acquire input data of crop evapotranspiration in the study area in real time; the input data includes meteorological factors, crop factors and soil water potential; The prediction module is connected to the input data acquisition module and is used to predict crop evapotranspiration based on the input data using an optimized artificial neural network model; the optimized artificial neural network model is obtained by optimizing the artificial neural network model using a piranha foraging optimization algorithm.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the crop evapotranspiration prediction method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the crop evapotranspiration prediction method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the crop evapotranspiration prediction method according to any one of claims 1 to 6 is implemented.

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