Method and system for screening crop rotation systems for different soil types

Abstract

The invention discloses a crop rotation system screening method and system for different soil types, and relates to the technical field of agricultural intelligent decision making, and the method comprises the steps: dividing a target land parcel into a plurality of sub land parcels; a plurality of crop rotation schemes are constructed, each crop rotation scheme comprises a plurality of rounds, each round comprises a plurality of crops to be planted at the same time, and each crop is distributed to one sub-plot in one round; defining an action value of each crop on a plurality of soil function factors for each crop; calculating a complementary value between any two crops based on the promotion effect value; calculating a complementary value score sum of any rotation scheme based on the complementary value; and selecting the rotation scheme with the highest complementary value score sum from the plurality of rotation schemes as output. The method can always ensure that at least one optimal sub-plot of any crop is suitable for planting at any stage in a multi-round planting sequence, and solves the problem of poor overall coordination of a traditional crop rotation scheme.

Description

Technical Field

[0001] The present invention relates to the field of agricultural intelligent decision-making technology, and more specifically, to a method and system for screening crop rotation systems for different soil types. Background Art

[0002] In modern agricultural practices, the practice of repeatedly planting a single crop on the same plot of land for extended periods (known as "continuous cropping") is common. While this practice facilitates management and equipment utilization in the short term, it can easily lead to a series of agricultural ecological and soil degradation issues in the medium and long term.

[0003] Continuous cropping of a single crop repeatedly depletes certain soil functional factors (such as nitrogen, phosphorus, and potassium), leading to a serious nutrient imbalance. Simultaneously, due to the lack of alternating root systems, the soil structure gradually becomes compacted, permeability decreases, and the microbial community becomes unbalanced, resulting in a decrease in soil fertility. Long-term continuous cropping also easily accumulates pests and diseases and toxic metabolites, causing crops to gradually develop a "physiological rejection" of the land, manifesting as reduced germination rates, unstable yields, and frequent disease outbreaks. This phenomenon is known as "continuous cropping disorder."

[0004] To address these issues, traditional crop rotation systems have been proposed, alternating between different crops to alleviate soil stress and restore ecological balance. However, these systems are often based on empirical judgment or static planning, overlooking several key issues: a failure to quantify the relationship between the contribution and consumption of soil functional factors by different crops, an inability to systematically assess the synergistic effects of crop combinations across multiple plots, and a lack of optimal mechanisms to ensure overall land use sustainability across complex, multi-crop, multi-site cropping cycles.

[0005] Especially in areas with limited land resources and significant soil heterogeneity, traditional crop rotation strategies can hardly guarantee that all crops can still find suitable land for cultivation in future rotations, which can easily lead to situations where some crops have no land to grow and some plots of land become functionally exhausted.

[0006] Therefore, a more systematic, intelligent and long-term sustainable method for crop rotation system screening is urgently needed. Achieving a sustainable, optimizable, and quantifiable intelligent crop rotation mechanism. Crucially, ensuring that at any stage in a multi-crop cropping sequence, there is always at least one optimal sub-plot suitable for planting any crop, building an agro-ecological cycle system with full-cycle adaptability, effectively avoiding continuous cropping obstacles and improving the overall utilization efficiency of land resources. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a method and system for screening crop rotation systems for different soil types, so as to solve the problems mentioned in the background technology.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions: A method for screening crop rotation systems for different soil types comprises the following steps: Split the target plot into multiple sub-plots; Constructing a plurality of crop rotation schemes, wherein each crop rotation scheme comprises a plurality of rounds, each round comprises a plurality of crops to be planted simultaneously, and each crop is allocated to a sub-plot in a round; Define the effect value of each crop on multiple soil functional factors, where positive and negative values ​​represent promoting and consuming effects respectively; Calculating the complementarity value between any two crops based on the promotion value; Calculating the total complementary value score of any crop rotation scheme based on the complementary value, wherein when two crops with high complementarity are allocated to physically adjacent sub-plots, a positive enhancement factor is applied to the total complementary value score; A crop rotation scheme with the highest total complementary value score is selected from the plurality of crop rotation schemes as output.

[0009] Preferably, for any two crops i and j , and calculate their complementarity score based on the following formula: ; in For the k The weight coefficient of each functional factor, m is the total number of functional factors; Indicates crops i For the first k The degree of promotion or consumption of each soil functional factor, where promotion is positive and consumption is negative.

[0010] Preferably, the soil functional factors include any one or more of the following: nitrogen content, phosphorus content, potassium content, organic matter content and pH.

[0011] Preferably, the total complementary value score corresponding to the crop rotation scheme P is The expression is: ; in, T It is expressed as the number of crop rotations, n is the number of crops per rotation; Indicates the t mid-rotation crops i With crops j Complementarity score between them; Indicates the t mid-rotation crops i With crops j The physical distance between the sub-plots; is the adjacency enhancement function, which is used to increase the weight of the complementarity scores between adjacent sub-plots.

[0012] Preferably, The expression is: ; in, ∈(0.5, 3.0) is the first adjustment coefficient; ∈(0.1, 1.0) is the second adjustment coefficient.

[0013] Preferably, an optimization algorithm is used to construct and find the crop rotation scheme with the highest total complementary value score, and the objective function of the optimization algorithm is the total complementary value score.

[0014] Preferably, the optimization algorithm is a genetic algorithm, which specifically includes the following steps: Each candidate crop rotation scheme is encoded as a chromosome, and each gene position in the chromosome represents the crop allocated to a sub-plot in a certain round; Initialize the population, including multiple randomly generated crop rotation schemes; In each generation, do the following: The sum of the complementary value scores is used as fitness to evaluate the quality of each chromosome; Select chromosomes with high fitness for crossover operation to generate the next generation of crop rotation plan; Perform mutation operations on some chromosomes to adjust crop distribution on individual sub-plots; Repeat the iteration until the preset number of generations or fitness convergence threshold is reached; The crop rotation plan with the highest fitness is output as the final recommended plan.

[0015] The present invention also discloses a crop rotation system screening system for different soil types, comprising: The plot division module is used to divide the target plot into multiple sub-plots; A crop rotation scheme construction module, used for constructing multiple crop rotation schemes, wherein each crop rotation scheme includes a plurality of rounds, each round includes a plurality of crops to be planted simultaneously, and each crop is allocated to a sub-plot in a round; The crop function modeling module is used to define the effect value of each crop on multiple soil function factors, where positive and negative values ​​represent promotion and consumption effects respectively; a crop complementarity calculation module, configured to calculate a complementarity value between any two crops based on the promotion effect value; a crop rotation scheme scoring module, configured to calculate a total complementary value score of any crop rotation scheme based on the complementary value, wherein when two crops with high complementarity are allocated to physically adjacent sub-plots, a positive enhancement factor is applied to the total complementary value score; The preferred output module is used to select a crop rotation scheme with the highest total complementary value score from the multiple crop rotation schemes as output.

[0016] Preferably, for any two crops i and j , and calculate their complementarity score based on the following formula: ; in For the k The weight coefficient of each functional factor, m is the total number of functional factors; Indicates crops i For the first k The degree of promotion or consumption of each soil functional factor, where promotion is positive and consumption is negative.

[0017] Preferably, the total complementary value score corresponding to the crop rotation scheme P is The expression is: ; in, T It is expressed as the number of crop rotations, n is the number of crops per rotation; Indicates the t mid-rotation crops i With crops j Complementarity score between them; Indicates the t mid-rotation crops i With crops j The physical distance between the sub-plots; is the adjacency enhancement function, which is used to increase the weight of the complementarity scores between adjacent sub-plots.

[0018] The advantages of the present invention over the prior art are: 1. This invention breaks down a target plot into multiple sub-plots and models the positive and negative effects of crops on multidimensional soil functional factors. During the rotation plan construction process, it systematically evaluates the matching relationships among multiple rotations, multiple crops, and multiple plots. The resulting rotation plan ensures that any crop has at least one suitable sub-plot in each planting cycle, fundamentally addressing the issue of land failure caused by continuous cropping problems and establishing a stable and sustainable farming mechanism over the long term.

[0019] 2. By defining the complementarity of soil functional factors between crops and formulaically calculating a complementarity score, this invention accurately measures the ecological compatibility of different crops in a crop rotation. In particular, the introduction of a "neighborhood enhancement factor" allows for additional weighted optimization of highly complementary crop combinations in adjacent plots, thereby achieving more efficient nutrient complementarity (adjacent areas are more likely to complement each other) and rotation sustainability, further enhancing the collaborative resilience of the overall soil ecology.

[0020] 3. This invention uses an optimization algorithm (such as a genetic algorithm) to perform a combined scoring of all candidate crop rotation schemes. The objective function is the sum of the complementarity scores over the entire cycle. This can quickly screen the optimal planting deployment from a vast space of options, thus avoiding the blindness and local optimality risks brought about by relying on manual configuration.

[0021] 4. This invention can adapt to various types of soil functional factor indicators (such as nitrogen, phosphorus, potassium, organic matter, pH, etc.), allowing for flexible modeling of different soil types and is suitable for a variety of agricultural production environments such as plains, hills, and terraces.

[0022] 5. This invention effectively reduces soil function depletion and microecological imbalance by optimizing crop rotation paths and crop configuration combinations, thereby improving land use efficiency and crop yield stability, providing intelligent tool support for the green and sustainable development of modern agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is an overall flow chart of the method of the present invention; Figure 2 is a flow chart of crop function modeling of the present invention; Figure 3 This is a flow chart of crop complementarity calculation and crop rotation scheme scoring of the present invention; Figure 4 This is a flow chart of the genetic algorithm optimization crop rotation plan of the present invention. DETAILED DESCRIPTION

[0024] The specific embodiments of the present invention will be described below with reference to the accompanying drawings.

[0025] The present invention provides a method and system for screening crop rotation systems for different soil types, aiming to optimize soil resource utilization and improve the sustainability of agricultural production through scientific crop rotation arrangements.

[0026] As shown in FIG1 , the method of the present invention comprises the following steps: Split the target plot into multiple sub-plots; Constructing a plurality of crop rotation schemes, wherein each crop rotation scheme comprises a plurality of rounds, each round comprises a plurality of crops to be planted simultaneously, and each crop is allocated to a sub-plot in a round; Define the effect value of each crop on multiple soil functional factors, where positive and negative values ​​represent promoting and consuming effects respectively; Calculating the complementarity value between any two crops based on the promotion value; Calculating the total complementary value score of any crop rotation scheme based on the complementary value, wherein when two crops with high complementarity are allocated to physically adjacent sub-plots, a positive enhancement factor is applied to the total complementary value score; A crop rotation scheme with the highest total complementary value score is selected from the plurality of crop rotation schemes as output.

[0027] In a specific embodiment, the target plot is first divided into multiple sub-plots. The purpose of this division is to enable more detailed management of the soil and crop planting in different areas during the crop rotation process. Each sub-plot can be independently planted with different crops, and in different rounds of the rotation, the crops on the sub-plot will be rotated to achieve balanced utilization of soil nutrients and control of pests and diseases. For example, a 100-acre farm can be divided into 10 sub-plots of 10 acres, and each sub-plot is planted with different crops in each round of the rotation, thereby avoiding continuous cropping obstacles. When dividing the plots, you can use an equal division method, or you can adopt a specific division plan according to the specific situation.

[0028] Next, multiple crop rotations are constructed. Each rotation consists of several rounds; for example, a three-year rotation might include three planting seasons. Each round includes multiple crops to be planted simultaneously, and each crop is assigned to a specific subplot within a round. This means that in each planting season, each subplot will plant one crop, and in the next season, it might plant a different crop. For example, in a three-round rotation, subplot A might plant wheat in the first round, corn in the second, and soybeans in the third, while subplot B might plant corn, soybeans, and wheat, in that order.

[0029] When developing a crop rotation plan, it's important to consider the crop type, planting schedule, and number of subplots. For example, consider five subplots and three rotations, with five crops selected for each rotation. A rotation plan can be visualized as a 3×5 matrix, where each row represents a rotation, each column represents a subplot, and the matrix elements represent the specific crop numbers. The goal of developing multiple rotation plans is to identify the optimal one through subsequent evaluation.

[0030] As shown in Figure 2, to assess the impact of crops on soil, it is necessary to define the effect values ​​of each crop on multiple soil functional factors. These soil functional factors can include nitrogen content, phosphorus content, potassium content, organic matter content, and pH. Effect values ​​are expressed as positive or negative values, where positive values ​​indicate the crop's promotion of the functional factor, such as leguminous crops increasing soil nitrogen content by fixing nitrogen; negative values ​​indicate the crop's consumption of the functional factor, such as wheat absorbing phosphorus and potassium from the soil during growth. Specific effect values ​​can be obtained in any of the following ways: Agricultural science literature and crop databases, such as the FAO (Food and Agriculture Organization of the United Nations), the USDA (United States Department of Agriculture), and the China Soil and Fertilizer Database, all provide evaluation indicators for the impact of crop cultivation on nutrients; Field measurements or laboratory data: Agricultural experimental stations and research institutions can use field trials to measure changes in soil parameters before and after crop planting. If there is no precise measurement, an initial matrix can be constructed by having agronomists score the impact intensity on crops.

[0031] These values ​​reflect the net impact of crop growth on soil nutrients and can serve as the basis for subsequent calculations. These values ​​need to be normalized, and the normalization method can be Z-score standardization.

[0032] As shown in Figure 3, the complementarity value between any two crops is calculated based on the crop's contribution to soil functional factors. This value reflects the degree of complementarity between the two crops on soil functional factors, meaning that the consumption of one crop is likely to be offset by the contribution of another. For example, if one crop consumes nitrogen while the other promotes its production, they have a high degree of complementarity on this functional factor.

[0033] For any two crops i and j , and calculate their complementarity score based on the following formula : ; in For the k The weight coefficient of each functional factor is used to adjust the importance of different functional factors;m is the total number of functional factors; Indicates crops i For the first k The degree of promotion or consumption of each soil functional factor, where promotion is positive and consumption is negative.

[0034] In some embodiments, ,and .

[0035] The design of this formula is based on the following logic: if the effect values ​​of two crops on a certain functional factor are very different (i.e. one is positive and the other is negative), then A large value indicates strong complementarity in this functional factor. The benefit of strong complementarity is that one sub-plot can fully utilize soil nutrients, while the other sub-plot can replenish the corresponding nutrients for subsequent use, thus preserving nutrient space within the overall land. The overall complementarity score is obtained by taking the weighted sum of all functional factors.

[0036] Weight coefficient The weighting of can be determined based on the importance of soil functional factors or specific agricultural objectives. For example, if the goal is to increase soil nitrogen content, the weight of nitrogen can be set to 0.6, while the weights of phosphorus and potassium can be set to 0.3 and 0.1, respectively. The sum of the weights is usually normalized to 1 to ensure comparability of the calculated results.

[0037] When evaluating the pros and cons of a crop rotation scheme, one should not only consider the complementarity between crops, but also the spatial distribution of crops on sub-plots, especially the crop combinations on adjacent sub-plots.

[0038] To this end, the sum of complementary value scores (S(p)) is introduced to comprehensively evaluate a crop rotation scheme (p). Its calculation formula is: ; in, T It is expressed as the number of crop rotations, n is the number of crops per rotation; Indicates the t mid-rotation crops i With crops j Complementarity score between them; Indicates the t mid-rotation crops i With crops j The physical distance between the sub-plots; is the adjacency enhancement function, which is used to increase the weight of the complementarity scores between adjacent sub-plots.

[0039] The significance of this formula lies in its comprehensive consideration of the complementarity of all crop pairs across all cycles and the additional weighting of spatially adjacent crop pairs through the adjacency enhancement function. Adjacency enhancement is introduced because, in real agriculture, nutrient exchange and ecological complementarity between adjacent plots are more significant. For example, if crops grown in one subplot consume nitrogen, while crops grown in an adjacent subplot promote nitrogen production, this proximity effect can improve soil nutrient use efficiency.

[0040] Adjacency Enhancement Function The specific form is: The expression is: ; in, ∈ (0.5, 3.0) is the first adjustment coefficient, which is used to control the intensity of the adjacent effect; ∈(0.1, 1.0) is the second adjustment coefficient, which is used to control the speed at which the enhancement effect weakens with increasing distance. It is the physical distance between the sub-plots where the corresponding crops are located, which can be expressed in actual distance (such as meters) or relative units between plots.

[0041] The design of this function is based on the principle of exponential decay: when When it is smaller (i.e., the sub-plots are adjacent), Close to 1, is larger, thereby amplifying the contribution of the complementarity score; when When it is larger, As it approaches 1, the adjacency effect gradually disappears.

[0042] For example, suppose two subplots are adjacent and the distance .set up ,but: ; This means that the complementarity scores of crop pairs on adjacent sub-plots will be magnified by about 1.9098 times. ,but: ; At this time, the enhancement effect is significantly weakened, which is consistent with the actual rule that the complementary effect between plots that are farther apart is weaker.

[0043] As shown in Figure 4 , since there may be a large number of crop rotation options, manually selecting the optimal option may be difficult. Therefore, an optimization algorithm is used to construct and find the crop rotation option with the highest total complementarity score. In some embodiments, a genetic algorithm, a heuristic search algorithm based on the principles of natural selection, can be used. It is suitable for solving complex combinatorial optimization problems.

[0044] The specific implementation of the genetic algorithm includes the following steps: Encode each candidate crop rotation as a chromosome. Each gene position in the chromosome represents the crop assigned to a subplot in a particular round. For example, if there are three rounds, each with four subplots, the chromosome can be a 3×4 matrix, with each row representing a round, each column representing a subplot, and the matrix elements being the crop numbers. For example, chromosome ([[1, 2, 3, 4], [2, 3, 4, 1], [3, 4, 1, 2]]) means that in the first round, subplots 1-4 will be planted with crops 1-4, respectively; in the second round, crops 2-1 will be planted, respectively; and in the third round, crops 3-2 will be planted, respectively.

[0045] Population initialization randomly generates multiple rotation schemes as the initial population. For example, 100 random chromosomes are generated, each representing a possible rotation scheme. The diversity of the initial population is crucial to the algorithm's global search capability, so a uniform random distribution method is used to generate the initial population.

[0046] Fitness evaluation uses the sum of complementary value scores As a fitness function, the quality of each chromosome is evaluated. The higher the fitness, the better the rotation scheme. For example, for a chromosome, calculate its is 1.749, then its fitness is 1.749.

[0047] Selection selects the best chromosomes for reproduction based on their fitness. Common selection methods include roulette wheel selection (where the probability is proportional to fitness) and tournament selection (where several chromosomes are randomly selected and the one with the highest fitness is chosen). For example, in roulette wheel selection, if a chromosome's fitness accounts for 20% of the total fitness of the population, its probability of being selected is 20%.

[0048] The crossover operation crosses over the selected chromosomes to generate new offspring chromosomes. Crossovers can be performed using a single-point crossover, for example, exchanging the subsequent gene positions at the second round of two parent chromosomes to generate two new offspring chromosomes. The crossover probability is typically set to 0.6 to 0.9, indicating the likelihood of a crossover occurring.

[0049] The mutation operation mutates part of the chromosome, randomly changing the values ​​of certain gene bits. For example, it might change the crop number 1 on a subplot from 1 to 3. The mutation probability is typically set to 0.01 to 0.1 to introduce new possibilities while maintaining population stability.

[0050] Iterative evolution repeats selection, crossover, and mutation operations to generate a new generation of populations and evaluate their fitness. This process continues until a preset number of generations is reached (e.g., 1,000 generations) or the fitness converges to a certain threshold (e.g., the change in the best fitness over 10 consecutive generations is less than 0.01).

[0051] After the evolution is complete, the crop rotation corresponding to the chromosome with the highest fitness is output as the final recommendation. For example, after 1000 generations, the highest fitness found is 2.345, and the corresponding crop rotation is the final output.

[0052] The performance of the genetic algorithm is greatly affected by parameter settings. The following are recommended parameter ranges: The population size is set to 50 to 200 chromosomes. A population that is too small may lead to a local optimum, while a population that is too large may increase the computational cost.

[0053] The crossover probability is set to 0.6 to 0.9. A higher crossover probability helps to explore new combinations.

[0054] The mutation probability is set to 0.01 to 0.1. A lower mutation probability avoids over-randomization of the population.

[0055] The number of iterations can be set to 100 to 1000 generations, depending on the problem size. For small problems (e.g., 5 subplots), 100 generations may be sufficient; for large problems (e.g., 20 subplots), 1000 generations may be required.

[0056] As described above, the present invention actually further discloses a crop rotation system screening system for different soil types, comprising: The plot division module is used to divide the target plot into multiple sub-plots; A crop rotation scheme construction module, used for constructing multiple crop rotation schemes, wherein each crop rotation scheme includes a plurality of rounds, each round includes a plurality of crops to be planted simultaneously, and each crop is allocated to a sub-plot in a round; The crop function modeling module is used to define the effect value of each crop on multiple soil function factors, where positive and negative values ​​represent promotion and consumption effects respectively; a crop complementarity calculation module, configured to calculate a complementarity value between any two crops based on the promotion effect value; a crop rotation scheme scoring module, configured to calculate a total complementary value score of any crop rotation scheme based on the complementary value, wherein when two crops with high complementarity are allocated to physically adjacent sub-plots, a positive enhancement factor is applied to the total complementary value score; The preferred output module is used to select a crop rotation scheme with the highest total complementary value score from the multiple crop rotation schemes as output.

[0057] By breaking down a target plot into multiple subplots and modeling the positive and negative effects of crops on multidimensional soil functional factors, this method systematically evaluates the matching relationships among multiple rotations, multiple crops, and multiple plots during the development of rotation plans. The resulting rotation plan ensures that any crop has at least one suitable subplot in each planting cycle, fundamentally addressing the issue of land inefficiency caused by continuous cropping.

[0058] Furthermore, by defining the complementarity of soil functional factors between crops and incorporating the spatial adjacency effect, this method can accurately measure the ecological compatibility of different crops in rotations. The introduction of the adjacency enhancement factor gives additional weight to highly complementary crop combinations in adjacent plots, thereby achieving more efficient nutrient complementarity and coordinated soil ecological restoration.

[0059] Genetic algorithm-based optimization technology can quickly select the optimal planting layout from a vast space of solutions, avoiding the blindness and local optimality risks of manual configuration. This approach is particularly suitable for agricultural scenarios with complex soil types and diverse crop varieties, such as the grain-producing areas of the North China Plain or the diverse planting regions in the hilly areas of southern China.

[0060] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for screening crop rotation systems for different soil types, characterized in that: The steps include: Split the target plot into multiple sub-plots; Constructing a plurality of crop rotation schemes, wherein each crop rotation scheme comprises a plurality of rounds, each round comprises a plurality of crops to be planted simultaneously, and each crop is allocated to a sub-plot in a round; Define the effect value of each crop on multiple soil functional factors, where positive and negative values ​​represent promoting and consuming effects respectively; Calculating the complementarity value between any two crops based on the promotion value; Calculating the total complementary value score of any crop rotation scheme based on the complementary value, wherein when two crops with high complementarity are allocated to physically adjacent sub-plots, a positive enhancement factor is applied to the total complementary value score; A crop rotation scheme with the highest total complementary value score is selected from the plurality of crop rotation schemes as output.

2. The method for screening crop rotation systems for different soil types according to claim 1, characterized in that: For any two crops i and j , and calculate their complementarity score based on the following formula : ； in For the k The weight coefficient of each functional factor, m is the total number of functional factors; Indicates crops i For the first k The degree of promotion or consumption of each soil functional factor, where promotion is positive and consumption is negative.

3. The method for screening crop rotation systems for different soil types according to claim 1 or 2, characterized in that: The soil functional factors include any one or more of the following: nitrogen content, phosphorus content, potassium content, organic matter content and pH.

4. The method for screening crop rotation systems for different soil types according to claim 1 or 2, characterized in that: The sum of the complementary value scores corresponding to the crop rotation scheme P The expression is: ； in, T It is expressed as the number of crop rotations, n is the number of crops per rotation; Indicates the t mid-rotation crops i With crops j Complementarity score between them; Indicates the t mid-rotation crops i With crops j The physical distance between the sub-plots; is the adjacency enhancement function, which is used to increase the weight of the complementarity scores between adjacent sub-plots.

5. The method for screening crop rotation systems for different soil types according to claim 4, characterized in that: The expression is: ； in, ∈(0.5, 3.0) is the first adjustment coefficient; ∈(0.1, 1.0) is the second adjustment coefficient.

6. The method for screening crop rotation systems for different soil types according to claim 1, characterized in that: An optimization algorithm is used to construct and find a crop rotation scheme with the highest total complementary value score, and the objective function of the optimization algorithm is the total complementary value score.

7. The method for screening crop rotation systems for different soil types according to claim 6, characterized in that: The optimization algorithm is a genetic algorithm, which specifically includes the following steps: Each candidate crop rotation scheme is encoded as a chromosome, and each gene position in the chromosome represents the crop allocated to a sub-plot in a certain round; Initialize the population, including multiple randomly generated crop rotation schemes; In each generation, do the following: The sum of the complementary value scores is used as fitness to evaluate the quality of each chromosome; Select chromosomes with high fitness for crossover operation to generate the next generation of crop rotation plan; Perform mutation operations on some chromosomes to adjust crop distribution on individual sub-plots; Repeat the iteration until the preset number of generations or fitness convergence threshold is reached; Output the crop rotation plan with the highest fitness as the final recommendation plan.

8. A crop rotation system screening system for different soil types, characterized by: include: The plot division module is used to divide the target plot into multiple sub-plots; A crop rotation scheme construction module, used for constructing multiple crop rotation schemes, wherein each crop rotation scheme includes a plurality of rounds, each round includes a plurality of crops to be planted simultaneously, and each crop is allocated to a sub-plot in a round; The crop function modeling module is used to define the effect value of each crop on multiple soil function factors, where positive and negative values ​​represent promotion and consumption effects respectively; a crop complementarity calculation module, configured to calculate a complementarity value between any two crops based on the promotion effect value; a crop rotation scheme scoring module, configured to calculate a total complementary value score of any crop rotation scheme based on the complementary value, wherein when two crops with high complementarity are allocated to physically adjacent sub-plots, a positive enhancement factor is applied to the total complementary value score; The preferred output module is used to select a crop rotation scheme with the highest total complementary value score from the multiple crop rotation schemes as output.

9. The crop rotation system screening system for different soil types according to claim 8, characterized in that: For any two crops i and j , and calculate their complementarity score based on the following formula: ； in For the k The weight coefficient of each functional factor, m is the total number of functional factors; Indicates crops i For the first k The degree of promotion or consumption of each soil functional factor, where promotion is positive and consumption is negative.

10. The crop rotation system screening system for different soil types according to claim 8, characterized in that: The sum of the complementary value scores corresponding to the crop rotation scheme P The expression is: ； in, T It is expressed as the number of crop rotations, n is the number of crops per rotation; Indicates the t mid-rotation crops i With crops j Complementarity score between them; Indicates the t mid-rotation crops i With crops j The physical distance between the sub-plots; is the adjacency enhancement function, which is used to increase the weight of the complementarity scores between adjacent sub-plots.

Images