Crop planting spatial layout method, device, computer equipment and storage medium

Abstract

The present invention relates to a method, device, computer equipment and storage medium for the spatial layout of crop planting. Among them, the method for the spatial layout of crop planting uses the random forest algorithm to train and estimate the development suitability of different crop types, introduces the neighborhood effect and land productivity constraints to simulate the interaction relationship between crops, and uses the roulette selection mechanism to depict the uncertainty in the transformation of crop types, so as to quickly and accurately simulate the spatial pattern of crop planting in future scenarios. This application solves the problems of excessive subjectivity and huge workload of traditional investigation methods, and it is difficult for remote sensing technology to simulate the spatial pattern of future crop planting structures, and promotes the refined expression of the spatial pattern of crop planting.

Description

Technical Field

[0001] The present invention relates to the field of agricultural information technology, and particularly to a method, device, computer device and storage medium for crop planting spatial layout. Background Art

[0002] The spatial pattern of crop planting is one of the core contents of the agricultural land use system. It can reflect the internal service functions of the agricultural land use system and the utilization status of human agricultural production resources, and is an important basis for optimizing and adjusting the crop structure. At present, the global food demand is increasing continuously, and the corresponding demands for land and water resources are also growing. However, there is often a problem of spatial mismatch between crop planting and the endowment of resources and environment, and there is a problem of low refinement of the crop planting spatial pattern. Summary of the Invention

[0003] Based on this, it is necessary to provide a method, device, computer device and storage medium for crop planting spatial layout that can promote the refined expression of the crop planting spatial pattern.

[0004] To achieve the above object, on the one hand, an embodiment of the present application provides a method for crop planting spatial layout, including:

[0005] Determine the land productivity and required area of the planting area;

[0006] Simulate the crop suitability probability of each pixel in the planting area;

[0007] Obtain the neighborhood effect value of each pixel;

[0008] Determine the constraint of limiting factors; wherein, the constraint of limiting factors is confirmed by land productivity, food yield demand and crop growth factor limitation;

[0009] According to the crop suitability probability, neighborhood effect value and constraint of limiting factors, determine the combined probability of a pixel turning into a specific crop type;

[0010] Perform roulette selection according to the combined probability to determine the crop type change result of each pixel, and obtain the allocated area of each crop type according to the crop type change result and complete the iteration of the current round;

[0011] Execute the iteration of the next round, and end the task when the allocated area is equal to the required area.

[0012] In one of the embodiments, the step of obtaining the neighborhood effect value of each pixel includes:

[0013] Based on the aggregation of the same type of crops, obtain the neighborhood effect value of a pixel turning into a specific crop type.

[0014] In one embodiment, in the step of obtaining the neighborhood effect value of a pixel converted into a specific crop type based on the aggregation of the same type of crops, the neighborhood effect value is obtained based on the following formula:

[0015]

[0016] where L is the planting type of pixel i, n is the radius of the neighborhood; Con is the objective function, whose value is 1 when L = q, and 0 otherwise.

[0017] In one embodiment, the step of obtaining the neighborhood effect value of each pixel includes:

[0018] Obtain the resource demand of each type of crop, and confirm the competition effect value according to the resource demand;

[0019] Obtain the symbiotic effect value, and obtain the neighborhood effect value based on the competition effect value and the symbiotic effect value.

[0020] In one embodiment, the step of determining the land productivity of the planting area includes:

[0021] Obtain the growth period parameters, heat zone zoning parameters, soil property parameters, land resource stock parameters and input level values of the planting area;

[0022] According to the growth period parameters and land resource stock parameters, obtain the photosynthetic production potential;

[0023] According to the photosynthetic production potential and heat zone zoning parameters, obtain the light and temperature production potential;

[0024] According to the light and temperature production potential and water use parameters, obtain the climate production potential;

[0025] According to the climate production potential and soil property parameters, confirm the land production potential;

[0026] According to the land production potential and input level value, confirm the land productivity.

[0027] In one embodiment, the limiting factor is confirmed according to the following formula:

[0028] C = con(L = Crop e )

[0029] where C represents the limiting factor of pixel i; con is the objective function, indicating that the pixel takes the value of 1 when it meets the conversion conditions such as production period, heat zone zoning, soil property, land resource stock and input level, and 0 otherwise; Crop e represents the e-th type of crop.

[0030] In one embodiment, in the step of determining the combined probability of a pixel transitioning to a specific crop type based on the crop suitability probability, neighborhood effect value, and constraint of limiting factors, the combined probability is obtained based on the following formula:

[0031]

[0032] Where is the combined probability; is the crop suitability probability; is the neighborhood effect value; C is the constraint of limiting factors.

[0033] On the one hand, an embodiment of the present invention further provides a crop planting space layout device, including:

[0034] A determination module for determining the land productivity and required area of the planting area;

[0035] A crop suitability probability acquisition module for simulating and obtaining the crop suitability probability of each pixel in the planting area by using the conditional random forest algorithm;

[0036] A neighborhood effect value acquisition module for obtaining the neighborhood effect value of each pixel;

[0037] A constraint module for determining the constraint of limiting factors; among them, the constraint of limiting factors is confirmed by land productivity, food yield demand, and crop growth factor constraints;

[0038] A combined probability acquisition module for determining the combined probability of a pixel transitioning to a specific crop type according to the crop suitability probability, neighborhood effect value, and constraint of limiting factors;

[0039] A roulette selection module for performing roulette selection according to the combined probability, determining the crop type change result of each pixel, and obtaining the allocated area of each crop type according to the crop type change result and completing the iteration of the current round;

[0040] An iteration module for performing the iteration of the next round and ending the task when the allocated area is equal to the required area.

[0041] On the one hand, an embodiment of the present application further provides a computer device, including a memory and a processor, the memory stores a computer program, and a computer program is stored in a computer-readable storage medium, wherein the computer program is set to execute the steps of the above method when running.

[0042] On the other hand, an embodiment of the present invention further provides a storage medium, on which a computer program is stored, and the computer program realizes the steps of the above method when executed by a processor.

[0043] One of the above technical solutions has the following advantages and beneficial effects:

[0044] The above method uses the random forest algorithm to train and estimate the development suitability of different crop types, introduces the neighborhood effect and land productivity constraints to simulate the interaction relationship between crops, and uses the roulette selection mechanism to characterize the uncertainty in the transformation of crop types, which is used to quickly and accurately simulate the spatial pattern of crop planting in future scenarios. This application solves the problems of overly strong subjectivity and huge workload in traditional survey methods, and the difficulty of remote sensing technology in simulating the spatial pattern of future crop planting structures, and promotes the refined expression of the spatial pattern of crop planting. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or in related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 It is a schematic flowchart of a method for the spatial layout of crop planting in an embodiment;

[0048] Figure 2 It is a schematic flowchart of the steps for determining the land productivity of a planting area in an embodiment;

[0049] Figure 3 It is a structural block diagram of a device for the spatial layout of crop planting in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] To facilitate the understanding of this application, the following will provide a more comprehensive description of this application with reference to the relevant drawings. Embodiments of this application are shown in the drawings. However, this application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of this application more thorough and comprehensive.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used in the specification of this application herein are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0052] In the following description, suffixes such as "module", "component", or "unit" used to represent elements are only for the convenience of describing the present application, and they do not have specific meanings themselves. Therefore, "module" and "component" can be used interchangeably.

[0053] As used herein, the singular forms "a", "an", and "the" may also include the plural forms unless the context clearly dictates otherwise. It should also be understood that the terms "comprising", "including", or "having", etc., specify the presence of the stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0054] Currently, the global food demand is increasing continuously, and the corresponding demand for land and water resources is growing. However, there is often a spatial mismatch between crop cultivation and the endowment of resources and the environment. Therefore, developing a method for simulating the spatial pattern of crop cultivation not only helps to provide an effective solution for optimizing the spatial pattern of crop cultivation, but also provides a solution for achieving the multi-dimensional collaborative goal of maximizing food production, reducing resource demand, and minimizing environmental impact, and provides refined guidance for optimizing the spatial pattern of crop cultivation. Currently, remote sensing technology is regarded as an effective method for extracting crop spatial distribution information, but it is difficult to simulate the spatial pattern of future crop planting structures. Traditional future land use models have good effects in land use simulation, but currently, they rarely involve the simulation and expression of the spatial pattern of crop cultivation and do not take into account factors such as climate, soil, and terrain related to crop growth. At the same time, due to the use of artificial neural networks (ANNs), there are overfitting problems.

[0055] The method for arranging the spatial layout of crop cultivation provided by the present application can effectively solve the above problems.

[0056] In one embodiment, as Figure 1 shown, a method for arranging the spatial layout of crop cultivation is provided, including:

[0057] S110, determining the land productivity and the required area of the planting area;

[0058] Among them, the required area refers to the total planting area of various crops in the planting area, which can be determined according to the estimated yield and market demand.

[0059] S120, simulating the crop suitability probability of each pixel in the planting area. This method has strong performance and can take into account the mutual influence between elements, and has good performance in the simulation of the spatial layout of crop cultivation, especially in large-scale simulations;

[0060] Specifically, the conditional random forest algorithm can be used for simulation. First, the stratified random sampling method is adopted to collect training samples from the crop planting structure data of the base year, and these samples are evenly distributed among various crops. 60% of the collected samples are used as training samples, 20% as validation samples, and 20% as test samples, which are used to train the random forest algorithm and verify the fitting accuracy. Then, a bootstrap sample set X is randomly drawn from the original training set X with replacement by the Bootstrap method. i Finally, for each bootstrap sample set X i , an unpruned decision tree is generated: Suppose there are M original variables in total, and a positive integer m is given, satisfying m << M. At each internal node, m predictive variables are randomly selected from the M original variables as candidate variables for this splitting node, and the best splitting method is selected from the m candidate variables to split this node. During the process of generating the entire forest, m remains unchanged. Repeat the above steps until M decision trees are generated (M is large enough). When predicting data of unknown classes, the output class label is determined by the majority vote of the M trees, that is:

[0061]

[0062] In the formula, H(x) represents the combined classification model, h(·) represents the decision tree model, Y represents the output variable (target variable), I(·) is the indicator function, and argmax Y represents the value of Y when making ∑I(h(X, Θ k ) = Y) take the maximum value.

[0063] The suitability probability is obtained using the above random forest method. When classifying, the random forest determines the predicted class based on the voting results of multiple decision trees. In addition, since randomness is introduced to both the original training set X and the original spatial variables during the process of training and generating the random forest, in many cases, the classification results of each decision tree are not exactly the same. For the same pixel, some decision trees may vote for a transformation to other crop types, while others may vote against it. The suitability probability of crop q in each pixel i is expressed as:

[0064]

[0065] In the formula, h m (i) represents the estimation result of a single decision tree m for pixel i; M represents the number of decision trees.

[0066] The steps of S120 can also be replaced by the GBDT algorithm (gradient boosting decision tree). Random forest and GBDT algorithms are two currently widely recognized effective methods for crop planting space classification. The random forest algorithm is a typical representative of the Bagging integration strategy, and the GBDT algorithm is a more common method in the Boosting integration strategy. The random forest improves performance by reducing the model variance, and the GBDT algorithm improves performance by reducing the model bias. The GBDT algorithm has higher accuracy than the random forest algorithm but is more sensitive to outliers.

[0067] Specifically, the GBDT algorithm is applied to generate a local correlation map to illustrate the potential non-linear relationship between crop suitability and crop planting space layout. The approximate function f(x) of a set of independent variables can be expressed as an additive expansion form of the basis function g(x; a n ) as follows:

[0068]

[0069] where N is the number of decision trees, a n is the splitting position and leaf node average value of the decision tree g(x; a n ), and ω n is the weight estimated by minimizing the loss function. The iterative steps for optimizing this loss function are as follows:

[0070] First, define the initial function:

[0071]

[0072] In each iteration process, the negative gradient function r in is as follows:

[0073]

[0074] According to r in in the above formula, set g(x; a n ) to calculate the optimal gradient:

[0075]

[0076] Based on the above function equation, add the learning rate μ(0 < μ < 1) for updating:

[0077] f n (x) = f n-1 (x) + μω n g(x; a n )

[0078] The GBDT algorithm can identify the relative importance of predictive variables. By accumulating all decision trees T, the squared importance of predictive variables can be obtained:

[0079]

[0080] The predictive variables x = {x1, x2, …, x p} are divided into two parts, including the set of interests z to be explained S and other predictive variables z c = x\z S . For z S , further estimate the partial dependence of f s on it:

[0081]

[0082] When the root mean square error reaches the minimum, the final model of the suitability probability of each crop type is obtained, and the suitability probability of the crop is obtained.

[0083] 3. Evaluation of the neighborhood effect of crop planting

[0084] S130, obtain the neighborhood effect value of each pixel;

[0085] Among them, the neighborhood effect value represents the influence degree of the adjacent plots around a certain plot on the selection of its planted crop type. Specifically, the neighborhood effect value of each pixel can be obtained by any means in this field.

[0086] S140, determine the limiting factor constraints; among them, the limiting factor constraints are confirmed by land productivity, food yield demand, and crop growth factor limitations;

[0087] Among them, the crop growth factor limitations are the limitations of irrigation water and fertilizers. Specifically, in the actual development process of crop planting space, due to the constraint of land productivity, the crop planting space does not change disorderly. Therefore, the constraint condition of limiting factor constraints is introduced. Furthermore, the layout of the crop planting space is constrained not to exceed the land productivity limit of this type of crop locally, and at the same time, it can meet the food yield and the nutritional needs of residents. Since the evaluation of land productivity considers natural, social, and economic factors including production period, heat zone zoning, soil properties, land resource stock, and input level, the limiting factors can be expressed as:

[0088] C = con(L = Crop e )

[0089] In the formula, C represents the limiting factor of pixel i; con is the objective function, indicating that the value is 1 when the pixel meets the conversion conditions such as production period, heat zone zoning, soil properties, land resource stock, and input level, otherwise it is 0; Crop eRepresents the e-th type of crop.

[0090] S150. Determine the combined probability of a pixel transitioning to a specific crop type based on the crop suitability probability, neighborhood effect value, and constraint of limiting factors;

[0091] Among them, the combined probability of a pixel transitioning to a specific crop type is the comprehensive probability of growing the specific crop type in this pixel.

[0092] Specifically, the combined probability is obtained based on the following formula:

[0093]

[0094] Among them, is the combined probability; is the crop suitability probability; is the neighborhood effect value; C is the constraint of limiting factors.

[0095] S160. Perform roulette selection according to the combined probability to determine the crop type change result of each pixel, and obtain the allocated area of each crop type based on the crop type change result and complete the iteration of the current round;

[0096] Specifically, introduce the roulette selection mechanism. The dominant crop type with the highest combined probability is more likely to undergo conversion, but the remaining types with relatively low combined probabilities still have the opportunity to be allocated to the current pixel. The core of the roulette selection mechanism is adaptive inertia (inertia coefficient), that is, the inertia coefficient of each crop type is determined by the difference between the existing quantity and the demand, and is adaptively adjusted during the iteration process, so that the quantity of each crop type develops towards the predetermined goal. If the macroscopic demand of a certain crop type conflicts with the development trend, the inertia coefficient will increase the inheritance of this type, so as to correct the utilization trajectory of this crop in the next iteration, promote the mutual conversion of other crop types to this type, narrow the gap between the quantity of this crop type and the predetermined goal, and ultimately meet the various crop demands of macroscopic development to the greatest extent. The formula is:

[0097]

[0098] In the formula, represents the inertia coefficient of crop type q at time t; and respectively represent the differences between the macroscopic demand and the allocated quantity of crop type q at time t - 1 and t - 2. The inertia coefficient is determined by the crop type occupying the current grid. In each grid, if it is not currently occupied by the demanded crop type, the inertia coefficient of this crop will be set to 1, and it will not affect the overall combined probability of this crop in this grid cell.

[0099] According to the above probability coefficient equation, the definition of the adaptive inertia coefficient is divided into three cases: when the development trend of the target crop type meets the macro demand and the gap between the two continues to narrow, the inertia coefficient remains unchanged; when the development trend of the crop type conflicts with the macro demand trend, if the macro demand is less than the current supply, the inertia coefficient is decreased; if the current supply does not meet the macro demand, the inertia coefficient is increased.

[0100] According to the roulette wheel selection mechanism, the change results of the crop types of each pixel can be confirmed, and finally the crop type of each pixel can be confirmed and the allocated areas of each crop type can be counted.

[0101] S170, perform the next round of iteration and end the task when the allocated area is equal to the required area.

[0102] This application uses the random forest algorithm to train and estimate the development suitability of different crop types, introduces the neighborhood effect and land productivity constraints to simulate the interaction relationship between crops, and uses the roulette wheel selection mechanism to characterize the uncertainty in the transformation of crop types, so as to quickly and accurately simulate the crop planting spatial pattern in the future scenario. This application solves the problems of overly strong subjectivity and huge workload in traditional survey methods, and it is difficult for remote sensing technology to simulate the spatial pattern of future crop planting structures, and promotes the refined expression of the crop planting spatial pattern.

[0103] In one embodiment, the steps of obtaining the neighborhood effect value of each pixel include:

[0104] Based on the aggregation of the same type of crops, obtain the neighborhood effect value of the pixel converted into a specific crop type.

[0105] Specifically, in the step of obtaining the neighborhood effect value of the pixel converted into a specific crop type based on the aggregation of the same type of crops, the neighborhood effect value is obtained based on the following formula:

[0106]

[0107] Among them, L is the planting type of pixel i, n is the radius of the domain; Con is the objective function, and its value is 1 when L = q, otherwise it is 0.

[0108] In one embodiment, the steps of obtaining the neighborhood effect value of each pixel include:

[0109] Obtain the resource demand of each type of crop, and confirm the competition effect value according to the resource demand;

[0110] Obtain the symbiotic effect value, and obtain the neighborhood effect value based on the competition effect value and the symbiotic effect value.

[0111] Specifically, the resource demand is the demand for resources (such as water, light, and nutrients) by various types of crops during their growth cycle. The crop type with the highest crop suitability probability is selected and confirmed as the current crop type to be planted. Then, geographical location data (such as longitude and latitude, grid data, etc.) of the crops of each current crop type to be planted is obtained, and clustering algorithms such as DBSCAN and K-means are applied to identify the clustering regions of different crop types in space. Based on the clustering regions, neighborhoods are defined, and then the competition and symbiotic relationships are calculated. The demand for resources by different crops will lead to competition, which can be quantified using a competition index (such as C-score). There may be a mutually beneficial relationship between some crops, such as the relationship between nitrogen-fixing plants and other crops, which can be determined through ecological literature or experimental data. For each pair of neighboring crops, the competition effect value is calculated. For example, if both crop A and crop B are high-demand crops, the competition effect between them will be relatively high, and the competition effect value can range from 0 to 0.5. For crop pairs with a symbiotic relationship, the symbiotic effect value is calculated, and the range can be from 0 to 0.5. Total neighborhood effect value: NE = Σ(competition effect value) + Σ(symbiotic effect value), where NE is the neighborhood effect value, and Σ represents the summation of all relevant crop pairs within the neighborhood. Further, the symbiotic effect value and the competition effect value can also be preset, that is, the competition effect value and the symbiotic effect value of two crops are preset. The stronger the competition effect between two crops, the smaller the value, and the stronger the symbiotic effect between two crops, the larger the value.

[0112] In one embodiment, as Figure 2 shown, the steps for determining the land productivity of the planting area include:

[0113] S210, obtaining the growth period parameters, heat zone zoning parameters, soil property parameters, land resource stock parameters, and input level value of the planting area;

[0114] S220, obtaining the photosynthetic production potential based on the growth period parameters and the land resource stock parameters;

[0115] Among them, the growth period parameters refer to various parameters of the crop during the time period from sowing to maturity; the land resource stock parameter can be the area coefficient; specifically, the photosynthetic production potential is obtained by the following formula:

[0116] Y1 = Cf(Q) = KΩεφ(1 - α)(1 - β)(1 - ρ)(1 - γ)(1 - ω)·(1 - d)sf(L)(1 - η) -1 (1 - δ) - 1 q -1 ∑Q j

[0117] In the formula, Y1 is the photosynthetic production potential; C is the unit conversion coefficient; K is the area coefficient; ∑Qj R is the total solar radiation during the crop growth period; Ω is the efficiency of the crop using light intensity; ε is the proportion of photosynthetically active radiation in the total radiation; φ is the light quantum conversion efficiency; α is the reflectance of the plant population; β is the transmittance of the lush plant population; ρ is the proportion of radiation intercepted by non-photosynthetic organs of the crop; γ is the proportion of light exceeding the light saturation point; ω is the proportion of respiratory consumption in photosynthetic products; d is the leaf and stem shedding rate of the crop; s is the economic coefficient of the crop; f(L) is the correction value of the dynamic change of the crop leaf area; η is the moisture content of mature grains; δ is the ash content rate; q is the calorific value per unit dry matter.

[0118] S230. Obtain the light and temperature production potential according to the photosynthetic production potential and the heat zone partition parameters.

[0119] Specifically, the heat zone partition parameter is the temperature correction function for crop photosynthesis; the light and temperature production potential is obtained by the following formula:

[0120] Y2 = f(T)·Y1

[0121] In the formula, Y2 is the light and temperature production potential; f(T) is the temperature correction function for crop photosynthesis.

[0122] S240. Obtain the climate production potential according to the light and temperature production potential and the water use parameters.

[0123] Specifically, the water use parameter is the water correction function; the climate production potential further performs water correction on the basis of the light and temperature production potential, considering not only the natural precipitation but also the irrigation water of economic input. Specifically:

[0124] Y w = Y1·f(W)(1 - I r ) + Y2·I r

[0125] In the formula, I r is the irrigation coefficient; Y2 is the light and temperature production potential; Y w is the climate production potential; F(W) is the water correction function.

[0126] S250. Confirm the land production potential according to the climate production potential and the soil property parameters.

[0127] Specifically, the soil property parameter is the soil availability coefficient, and the land production potential is obtained by the following formula:

[0128] Y L = f(s)·Y w

[0129] In the formula, Y L is the land production potential; f(s) is the soil availability coefficient.

[0130] S260. Determine the land productivity according to the land production potential and the input level value.

[0131] Specifically, the input level value is the total socio-economic input. Calculate the land productivity for each pixel through a multi-objective analysis method.

[0132] Y = f(I0, Y L )

[0133] Where Y is the land productivity; I0 is the total socio-economic input, satisfying the condition of maximizing the profit:

[0134] f(I, Y L )·P e -I < f(I0, Y L )·P e -I0,

[0135] Where Pe is the expected price.

[0136] In one embodiment, as Figure 3 shown, the embodiment of the present invention further provides a device for crop planting spatial layout, including:

[0137] A determination module for determining the land productivity and the required area of the planting area;

[0138] A crop suitability probability acquisition module for simulating and obtaining the crop suitability probability of each pixel in the planting area;

[0139] A neighborhood effect value acquisition module for acquiring the neighborhood effect value of each pixel;

[0140] A constraint module for determining the constraint of limiting factors; wherein, the constraint of limiting factors is confirmed by the land productivity, the food production demand and the crop growth factor constraints;

[0141] A combined probability acquisition module for determining the combined probability of a pixel changing to a specific crop type according to the crop suitability probability, the neighborhood effect value and the constraint of limiting factors;

[0142] A roulette wheel selection module for performing roulette wheel selection according to the combined probability, determining the crop type change result of each pixel, and obtaining the allocated area of each crop type according to the crop type change result and completing the iteration of the current round;

[0143] An iteration module for performing the iteration of the next round and ending the task when the allocated area is equal to the required area.

[0144] For the specific limitations of the crop planting space layout device, reference may be made to the limitations of the crop planting space layout method in the foregoing text, which will not be elaborated herein. Each module in the above crop planting space layout device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules. It should be noted that the division of modules in the embodiments of the present application is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0145] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0146] Determine the land productivity and required area of the planting area;

[0147] Simulate to obtain the crop suitability probability of each pixel in the planting area;

[0148] Obtain the neighborhood effect value of each pixel;

[0149] Determine the constraint of limiting factors; wherein, the constraint of limiting factors is confirmed by the land planting potential characteristics, food yield requirements, and crop growth factor limitations;

[0150] According to the crop suitability probability, neighborhood effect value, and constraint of limiting factors, determine the combined probability of a pixel changing to a specific crop type;

[0151] Perform roulette selection according to the combined probability to determine the crop type change result of each pixel, and obtain the allocated area of each crop type according to the crop type change result and complete the iteration of the current round;

[0152] Execute the iteration of the next round, and end the task when the allocated area is equal to the required area.

[0153] In one embodiment, the embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented.

[0154] Determine the land productivity and required area of the planting area;

[0155] Simulate to obtain the crop suitability probability of each pixel in the planting area;

[0156] Obtain the neighborhood effect value of each pixel;

[0157] Determine the limiting factor constraints; among them, the limiting factor constraints are confirmed by the land planting potential characteristics, food production demand, and crop growth factor limitations;

[0158] According to the crop suitability probability, neighborhood effect value, and limiting factor constraints, determine the combined probability of a pixel transitioning to a specific crop type;

[0159] Perform roulette selection based on the combined probability to determine the crop type change result of each pixel, and obtain the allocated area of each crop type according to the crop type change result and complete the iteration of the current round;

[0160] Execute the iteration of the next round and end the task when the allocated area is equal to the required area.

[0161] In the specific implementation of the embodiments of the present application, reference may be made to the above-mentioned various embodiments, and corresponding technical effects are achieved.

[0162] It can be understood that these embodiments described herein can be implemented by hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application, or a combination thereof.

[0163] For software implementation, the techniques described herein can be implemented by units that execute the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented inside or outside the processor.

[0164] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0165] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0166] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0167] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0169] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes. It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. Without further limitations, an element defined by the statement "including an..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0170] The above are only specific implementation manners of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. A crop planting space layout method, characterized in that: include: Determine land productivity and area required in the planting area; Simulating and obtaining the crop suitability probability of each pixel in the planting area; Obtaining the neighborhood effect value of each pixel; Determining limiting factor constraints; wherein the limiting factor constraints are confirmed by the land productivity, food production requirements and crop growth factor constraints; Determining a combined probability of a pixel being transformed into a specific crop type based on the crop suitability probability, the neighborhood effect value, and the limiting factor constraint; Performing roulette selection according to the combined probability, determining the crop type change result of each pixel, and obtaining the allocated area of ​​each crop type according to the crop type change result and completing the current round of iteration; The next round of iterations is performed, and the task is terminated when the allocated area is equal to the required area.

2. The crop planting space layout method according to claim 1, characterized in that: The step of obtaining the neighborhood effect value of each pixel comprises: Based on the clustering of crops of the same type, the neighborhood effect value of pixels converted into specific crop types is obtained.

3. The crop planting space layout method according to claim 2, characterized in that: In the step of converting pixels into neighborhood effect values ​​of specific crop types based on the aggregation of crops of the same type, the neighborhood effect value is obtained based on the following formula: Where L is the planting type of pixel i, n is the radius of the field; Con is the objective function, whose value is 1 when L = q, otherwise it is 0; q is the target planting category.

4. The crop planting space layout method according to claim 1, characterized in that: The step of obtaining the neighborhood effect value of each pixel comprises: Obtaining resource requirements of various types of crops, and determining competition effect values ​​according to the resource requirements; A symbiotic effect value is obtained, and the neighborhood effect value is obtained based on the competition effect value and the symbiotic effect value.

5. The crop planting space layout method according to claim 1, characterized in that: The steps to determine land productivity in a cropping area include: Obtain growth period parameters, thermal zone zoning parameters, soil property parameters, land resource stock parameters and input level values ​​of the planting area; Obtaining photosynthetic production potential according to the growth period parameters and the land resource stock parameters; According to the photosynthetic production potential and the thermal zone zoning parameters, obtaining the light and temperature production potential; According to the light and temperature production potential and water use parameters, the climate production potential is obtained; Determine the land production potential based on the climate production potential and the soil property parameters; The land productivity is determined based on the land production potential and the input level value.

6. The crop planting space layout method according to claim 1, characterized in that: The limiting factor is determined according to the following formula: C=con(L=Crop e ) Where C represents the limiting factor of pixel i; L is the planting type of pixel i; con is the objective function, which means that the pixel takes the value of 1 when it meets the conversion conditions of production period, thermal zone division, soil properties, land resource stock and input level, and takes the value of 0 otherwise; Crop e Indicates crop category e.

7. The crop planting space layout method according to claim 1, characterized in that: In the step of determining the combined probability of a pixel being transformed into a specific crop type according to the crop suitability probability, the neighborhood effect value and the limiting factor constraint, the combined probability is obtained based on the following formula: in, is the combined probability; is the suitability probability of the crop; is the neighborhood effect value; C is the limiting factor constraint.

8. A crop planting space layout device, characterized in that: include: Determination module to determine land productivity and required area in the planting area; A crop suitability probability acquisition module, used to simulate and obtain the crop suitability probability of each pixel in the planting area using a conditional random forest algorithm; A neighborhood effect value acquisition module, used to acquire the neighborhood effect value of each pixel; A constraint module, used for determining limiting factor constraints; wherein the limiting factor constraints are confirmed by the land productivity, food production demand and crop growth factor constraints; A combined probability acquisition module, used for determining the combined probability of a pixel being transformed into a specific crop type according to the crop suitability probability, the neighborhood effect value and the limiting factor constraint; A roulette wheel selection module, configured to perform roulette wheel selection according to the combined probability, determine the crop type change result of each pixel, obtain the allocated area of ​​each crop type according to the crop type change result and complete the current round of iteration; The iteration module is used to execute the next round of iterations and end the task when the allocated area is equal to the required area.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: The computer program is configured to execute the steps of the method according to any one of claims 1 to 7 when executed.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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